Accelerated diffusion-weighted magnetic resonance imaging at 7 T: Joint reconstruction for shift-encoded navigator-based interleaved echo planar imaging (JETS-NAViEPI) - Tan, Zhengguo; Liebig, Patrick Alexander; Heidemann, Robin Martin; Laun, Frederik Bernd; Knoll, Florian (Imaging Neuroscience, 2024)
The pursuit of high spatial-angular-temporal resolution for in vivo diffusion-weighted magnetic resonance imaging (DW-MRI) at ultra-high field strength (7 T and above) is important in understanding brain microstructure and function. Such pursuit, however, faces several technical challenges. First, increased off-resonance and shorter (Formula presented.) relaxation require faster echo train readouts. Second, existing high-resolution DW-MRI techniques usually employ in-plane fully-sampled multi-shot EPI, which not only prolongs the scan time but also induces a high specific absorption rate (SAR) at 7 T. To address these challenges, we develop in this work navigator-based interleaved EPI (NAViEPI) which enforces the same effective echo spacing (ESP) between the imaging and the navigator echo. First, NAViEPI renders no distortion mismatch between the two echoes, and thus simplifies shot-to-shot phase variation correction. Second, NAViEPI allows for a large number of shots (e.g., (Formula presented.)) with undersampled iEPI acquisition, thereby rendering clinically-feasible high-resolution sub-milliemeter protocols. To retain signal-to-noise ratio (SNR) and to reduce undersampling artifacts, we developed a (Formula presented.) -shift encoding among diffusion encodings to explore complementary (Formula presented.) - (Formula presented.) -space sampling. Moreover, we developed a novel joint reconstruction with overlapping locally low-rank regularization generalized to the multi-band multi-shot acquisition at 7 T (dubbed JETS-NAViEPI). Our method was demonstrated, with experimental results covering 1 mm isotropic resolution multi (Formula presented.) -value DWI and sub-millimeter in-plane resolution fast TRACE acquisition.
CRIS-Team Scopus Importer:2025-07-10
Keywords: Diffusion-weighted magnetic resonance imaging; echo planar imaging; joint reconstruction; low rank; navigator; ultra-high field

Adapted random sampling patterns for accelerated MRI - Knoll, Florian; Clason, Christian; Diwoky, Clemens; Stollberger, Rudolf (Magnetic Resonance Materials in Physics Biology and Medicine, 2011)
Objective: Variable density random sampling patterns have recently become increasingly popular for accelerated imaging strategies, as they lead to incoherent aliasing artifacts. However, the design of these sampling patterns is still an open problem. Current strategies use model assumptions like polynomials of different order to generate a probability density function that is then used to generate the sampling pattern. This approach relies on the optimization of design parameters which is very time consuming and therefore impractical for daily clinical use. Materials and methods: This work presents a new approach that generates sampling patterns by making use of power spectra of existing reference data sets and hence requires neither parameter tuning nor an a priori mathematical model of the density of sampling points. Results: The approach is validated with downsampling experiments, as well as with accelerated in vivo measurements. The proposed approach is compared with established sampling patterns, and the generalization potential is tested by using a range of reference images. Quantitative evaluation is performed for the downsampling experiments using RMS differences to the original, fully sampled data set. Conclusion: Our results demonstrate that the image quality of the method presented in this paper is comparable to that of an established model-based strategy when optimization of the model parameter is carried out and yields superior results to non-optimized model parameters. However, no random sampling pattern showed superior performance when compared to conventional Cartesian subsampling for the considered reconstruction strategy. © 2010 ESMRMB.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Accelerated imaging; Parallel imaging; Random sampling; Variable density sampling

Adrenal gland volume measurement in depressed patients - Stepansky, Leonard; Ruppel, Richard; Sommerfeld, Lisa; Kleiß, Joy-Marie; Türkan, Kaan; Arndt, Sebastian; Bickelhaupt, Sebastian; Knoll, Florian; Uder, Michael; May, Matthias (Journal of Psychiatric Research, 2025)
Purpose: Prior studies have shown contradicting results regarding adrenal gland volume (AGV) in depressed patients, with some reporting significant enlargement and others not. The aim of this study was to retrospectively compare CT image segmentations of the adrenal glands in patients with depression to a control group with stringent exclusion criteria to minimize confounding factors. Methods: We included patients diagnosed with depression (ICD-10: F32/33) who underwent abdominal CT imaging between 2012 and 2022 and did not have any other psychiatric disorders. Diagnoses that could potentially influence AGV were excluded. The resulting 31 depressed patients were compared to a matching control group of 31 patients without depression. The AGV was manually segmented in thin-sliced reconstructions (≤1 mm). Results: Total AGV in the depressed group was 6.78 (5.19–7.56) cm3 compared to 6.90 (5.54–10.05) cm3 in the control group. There was no significant difference in AGV between the two groups after adjusting for age, height, and weight. A positive correlation was observed between AGV and height (r = 0.41, p < 0.001) and weight (r = 0.52, p < 0.001). Males showed significantly larger AGV than females (p ≤ 0.001), and left AGV was significantly larger than right AGV (p < 0.001). Patients within the depressed group who underwent imaging after a suicide attempt showed larger total AGV compared to the control group, though not statistically significant. Conclusion: AGV is not increased in the well-selected cohort of depressed patients in this study, which contrasts with some previous reports in literature. Further multi-centric studies are required to identify potentially influencing factors such as attempted suicide.
CRIS-Team Scopus Importer:2025-05-16

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge - Knoll, Florian; Murrell, Tullie; Sriram, Anuroop; Yakubova, Nafissa; Zbontar, Jure; Rabbat, Michael; Defazio, Aaron; Muckley, Matthew J.; Sodickson, Daniel K.; Zitnick, C. Lawrence; Recht, Michael P. (Magnetic Resonance in Medicine, 2020)
Purpose: To advance research in the field of machine learning for MR image reconstruction with an open challenge. Methods: We provided participants with a dataset of raw k-space data from 1,594 consecutive clinical exams of the knee. The goal of the challenge was to reconstruct images from these data. In order to strike a balance between realistic data and a shallow learning curve for those not already familiar with MR image reconstruction, we ran multiple tracks for multi-coil and single-coil data. We performed a two-stage evaluation based on quantitative image metrics followed by evaluation by a panel of radiologists. The challenge ran from June to December of 2019. Results: We received a total of 33 challenge submissions. All participants chose to submit results from supervised machine learning approaches. Conclusions: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption.
CRIS-Team Scopus Importer:2021-08-17
Keywords: challenge; compressed sensing; fast imaging; image reconstruction; machine learning; optimization; parallel imaging; public dataset

Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications - Zibetti, Marcelo Victor Wust; Knoll, Florian; Regatte, Ravinder R. (IEEE Transactions on Computational Imaging, 2022)
This work proposes an alternating learning approach to learn the sampling pattern (SP) and the parameters of variational networks (VN) in accelerated parallel magnetic resonance imaging (MRI). We investigate four variations of the learning approach, that alternates between improving the SP, using bias-accelerated subset selection, and improving parameters of the VN, using ADAM. The variations include the use of monotone or non-monotone alternating steps and systematic reduction of learning rates. The algorithms learn an effective pair to be used in future scans, including an SP that captures fewer k-space samples in which the generated undersampling artifacts are removed by the VN reconstruction. The quality of the VNs and SPs obtained by the proposed approaches is compared against different methods, including other kinds of joint learning methods and state-of-art reconstructions, on two different datasets at various acceleration factors (AF). We observed improvements visually and in three different figures of merit commonly used in deep learning (RMSE, SSIM, and HFEN) on AFs from 2 to 20 with brain and knee joint datasets when compared to the other approaches. The improvements ranged from 1% to 62% over the next best approach tested with VNs. The proposed approach has shown stable performance, obtaining similar learned SPs under different initial training conditions. We observe that the improvement is not only due to the learned sampling density, it is also due to the learned position of samples in k-space. The proposed approach was able to learn effective pairs of SPs and reconstruction VNs, improving 3D Cartesian accelerated parallel MRI applications.
CRIS-Team Scopus Importer:2022-06-03
Keywords: Accelerated MRI; alternating optimization; compressed sensing; Deep learning; deep learning; Encoding; Image reconstruction; image reconstruction; Imaging; Magnetic resonance imaging; Sensitivity; Three-dimensional displays; variational networks

A model-based reconstruction for undersampled radial spin-echo DTI with variational penalties on the diffusion tensor - Knoll, Florian; Raya, Jose G.; Halloran, Rafael O.; Baete, Steven; Sigmund, Eric; Bammer, Roland; Block, Tobias; Otazo, Ricardo; Sodickson, Daniel K. (NMR in Biomedicine, 2015)
Radial spin-echo diffusion imaging allows motion-robust imaging of tissues with very low T2 values like articular cartilage with high spatial resolution and signal-to-noise ratio (SNR). However, in vivo measurements are challenging, due to the significantly slower data acquisition speed of spin-echo sequences and the less efficient k-space coverage of radial sampling, which raises the demand for accelerated protocols by means of undersampling. This work introduces a new reconstruction approach for undersampled diffusion-tensor imaging (DTI). A model-based reconstruction implicitly exploits redundancies in the diffusion-weighted images by reducing the number of unknowns in the optimization problem and compressed sensing is performed directly in the target quantitative domain by imposing a total variation (TV) constraint on the elements of the diffusion tensor. Experiments were performed for an anisotropic phantom and the knee and brain of healthy volunteers (three and two volunteers, respectively). Evaluation of the new approach was conducted by comparing the results with reconstructions performed with gridding, combined parallel imaging and compressed sensing and a recently proposed model-based approach. The experiments demonstrated improvements in terms of reduction of noise and streaking artifacts in the quantitative parameter maps, as well as a reduction of angular dispersion of the primary eigenvector when using the proposed method, without introducing systematic errors into the maps. This may enable an essential reduction of the acquisition time in radial spin-echo diffusion-tensor imaging without degrading parameter quantification and/or SNR.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Compressed sensing; Diffusion-tensor imaging; Iterative reconstruction; Model-based image reconstruction; Non-Cartesian imaging

Analysis of Deep Learning-based Reconstruction Models for Highly Accelerated MR Cholangiopancreatography: to Fine-tune or not to Fine-tune - Kim, Jinho; Benkert, Thomas; Riemenschneider, Bruno; Nickel, Marcel Dominik; Knoll, Florian (Analysis of Deep Learning-baed Reconstruction Models for Highly Accelerated MR Cholangiopancreatography: to Fine-tune or not to Fine-tune, 2023)

An image space approach to Cartesian based parallel MR imaging with total variation regularization - Keeling, Stephen L.; Clason, Christian; Hintermueller, Michael; Knoll, Florian; Laurain, Antoine; Von Winckel, Gregory (Medical image analysis, 2012)
The Cartesian parallel magnetic imaging problem is formulated variationally using a high-order penalty for coil sensitivities and a total variation like penalty for the reconstructed image. Then the optimality system is derived and numerically discretized. The objective function used is non-convex, but it possesses a bilinear structure that allows the ambiguity among solutions to be resolved technically by regularization and practically by normalizing a pre-estimated norm of the reconstructed image. Since the objective function is convex in each single argument, convex analysis is used to formulate the optimality condition for the image in terms of a primal-dual system. To solve the optimality system, a nonlinear Gauss-Seidel outer iteration is used in which the objective function is minimized with respect to one variable after the other using an inner generalized Newton iteration. Computational results for in vivo MR imaging data show that a significant improvement in reconstruction quality can be obtained by using the proposed regularization methods in relation to alternative approaches. © 2011 Elsevier B.V.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Aliasing correction; Bilinear residual; Cartesian parallel magnetic resonance imaging; Primal-dual; Total variation regularization

Artificial Intelligence Explained for Nonexperts - Razavian, Narges; Knoll, Florian; Geras, Krzysztof J. (Seminars in Musculoskeletal Radiology, 2020)
Artificial intelligence (AI) has made stunning progress in the last decade, made possible largely due to the advances in training deep neural networks with large data sets. Many of these solutions, initially developed for natural images, speech, or text, are now becoming successful in medical imaging. In this article we briefly summarize in an accessible way the current state of the field of AI. Furthermore, we highlight the most promising approaches and describe the current challenges that will need to be solved to enable broad deployment of AI in clinical practice.
CRIS-Team Scopus Importer:2021-08-17
Keywords: artificial intelligence; deep learning; medical imaging; review

Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians - Lin, Dana J.; Johnson, Patricia M.; Knoll, Florian; Lui, Yvonne W. (Journal of Magnetic Resonance Imaging, 2021)
Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with recent breakthroughs applying deep-learning models for data acquisition, classification problems, segmentation, image synthesis, and image reconstruction. With an eye towards clinical applications, we summarize the active field of deep-learning-based MR image reconstruction. We review the basic concepts of how deep-learning algorithms aid in the transformation of raw k-space data to image data, and specifically examine accelerated imaging and artifact suppression. Recent efforts in these areas show that deep-learning-based algorithms can match and, in some cases, eclipse conventional reconstruction methods in terms of image quality and computational efficiency across a host of clinical imaging applications, including musculoskeletal, abdominal, cardiac, and brain imaging. This article is an introductory overview aimed at clinical radiologists with no experience in deep-learning-based MR image reconstruction and should enable them to understand the basic concepts and current clinical applications of this rapidly growing area of research across multiple organ systems.
CRIS-Team Scopus Importer:2021-08-17
Keywords: deep learning; image reconstruction; MRI

Artificial intelligence in musculoskeletal imaging: Current status and future directions - Gyftopoulos, Soterios; Lin, Dana; Knoll, Florian; Doshi, Ankur M.; Rodrigues, Tatiane Cantarelli; Recht, Michael P. (American Journal of Roentgenology, 2019)
OBJECTIVE. The objective of this article is to show how artificial intelligence (AI) has impacted different components of the imaging value chain thus far as well as to describe its potential future uses. CONCLUSION. The use of AI has the potential to greatly enhance every component of the imaging value chain. From assessing the appropriateness of imaging orders to helping predict patients at risk for fracture, AI can increase the value that musculoskeletal imagers provide to their patients and to referring clinicians by improving image quality, patient centricity, imaging efficiency, and diagnostic accuracy.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Artificial intelligence; Deep learning; Fast MRI; Machine learning; MRI; Musculoskeletal imaging

A Simulation Pipeline to Generate Realistic Breast Images for Learning DCE-MRI Reconstruction - Huang, Zhengnan; Bae, Jonghyun; Johnson, Patricia M.; Sood, Terlika; Heacock, Laura; Fogarty, Justin; Moy, Linda; Kim, Sungheon Gene; Knoll, Florian (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021)
Dynamic contrast enhancement (DCE) MRI has been increasingly utilized in clinical practice. While machine learning (ML) applications are gaining momentum in MRI reconstruction, the dynamic nature of image acquisition for DCE-MRI limits access to a simultaneously high spatial and temporal resolution ground truth image for supervised ML applications. In this study, we introduced a pipeline to simulate the ground truth DCE-MRI k-space data from real breast perfusion images. Based on physical model and the clinical images, we estimate the perfusion parameters. Treating those as ground truth, we simulated the signal. Using our simulated images, we trained ML reconstruction models. We demonstrate the utility of our simulation pipeline using two ML models and one conventional reconstruction method. Our results suggest that, even though the image quality of the ML reconstructions seem to be very close to the simulated ground truth, the temporal pattern and its kinetic parameters may not be close to the ground truth data.
CRIS-Team Scopus Importer:2023-03-08
Keywords: DCE-MRI; Machine learning; Reconstruction

Assessment of Deep Learning-based Reconstruction with Imperfect Ground Truth for MRCP - Kim, Jinho; Nickel, Marcel Dominik; Knoll, Florian (Assessment of Deep Learning-based Reconstruction with Imperfect Ground Truth for MRCP, 2024)

Assessment of the generalization of learned image reconstruction and the potential for transfer learning - Knoll, Florian; Hammernik, Kerstin; Kobler, Erich; Pock, Thomas; Recht, Michael P.; Sodickson, Daniel K. (Magnetic Resonance in Medicine, 2019)
Purpose: Although deep learning has shown great promise for MR image reconstruction, an open question regarding the success of this approach is the robustness in the case of deviations between training and test data. The goal of this study is to assess the influence of image contrast, SNR, and image content on the generalization of learned image reconstruction, and to demonstrate the potential for transfer learning. Methods: Reconstructions were trained from undersampled data using data sets with varying SNR, sampling pattern, image contrast, and synthetic data generated from a public image database. The performance of the trained reconstructions was evaluated on 10 in vivo patient knee MRI acquisitions from 2 different pulse sequences that were not used during training. Transfer learning was evaluated by fine-tuning baseline trainings from synthetic data with a small subset of in vivo MR training data. Results: Deviations in SNR between training and testing led to substantial decreases in reconstruction image quality, whereas image contrast was less relevant. Trainings from heterogeneous training data generalized well toward the test data with a range of acquisition parameters. Trainings from synthetic, non-MR image data showed residual aliasing artifacts, which could be removed by transfer learning–inspired fine-tuning. Conclusion: This study presents insights into the generalization ability of learned image reconstruction with respect to deviations in the acquisition settings between training and testing. It also provides an outlook for the potential of transfer learning to fine-tune trainings to a particular target application using only a small number of training cases.
CRIS-Team Scopus Importer:2021-08-17
Keywords: accelerated imaging; deep learning; iterative image reconstruction; machine learning; transfer learning; variational network

A total variation based approach to correcting surface coil magnetic resonance images - Keeling, Stephen L.; Hintermueller, Michael; Knoll, Florian; Kraft, Daniel; Laurain, Antoine (Applied Mathematics and Computation, 2011)
Magnetic resonance images which are corrupted by noise and by smooth modulations are corrected using a variational formulation incorporating a total variation like penalty for the image and a high order penalty for the modulation. The optimality system is derived and numerically discretized. The cost functional used is non-convex, but it possesses a bilinear structure which allows the ambiguity among solutions to be resolved technically by regularization and practically by normalizing the maximum value of the modulation. Since the cost is convex in each single argument, convex analysis is used to formulate the optimality condition for the image in terms of a primal-dual system. To solve the optimality system, a nonlinear Gauss-Seidel outer iteration is used in which the cost is minimized with respect to one variable after the other using an inner generalized Newton iteration. Favorable computational results are shown for artificial phantoms as well as for realistic magnetic resonance images. Reported computational times demonstrate the feasibility of the approach in practice. © 2011 Elsevier Inc. All rights reserved.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Bilinear residual; Denoising; Magnetic resonance imaging; Modulation correction; Primal dual; Total variation regularization

Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction - Narnhofer, Dominik; Effland, Alexander; Kobler, Erich; Hammernik, Kerstin; Knoll, Florian; Pock, Thomas (IEEE Transactions on Medical Imaging, 2022)
Recent deep learning approaches focus on improving quantitative scores of dedicated benchmarks, and therefore only reduce the observation-related (aleatoric) uncertainty. However, the model-immanent (epistemic) uncertainty is less frequently systematically analyzed. In this work, we introduce a Bayesian variational framework to quantify the epistemic uncertainty. To this end, we solve the linear inverse problem of undersampled MRI reconstruction in a variational setting. The associated energy functional is composed of a data fidelity term and the total deep variation (TDV) as a learned parametric regularizer. To estimate the epistemic uncertainty we draw the parameters of the TDV regularizer from a multivariate Gaussian distribution, whose mean and covariance matrix are learned in a stochastic optimal control problem. In several numerical experiments, we demonstrate that our approach yields competitive results for undersampled MRI reconstruction. Moreover, we can accurately quantify the pixelwise epistemic uncertainty, which can serve radiologists as an additional resource to visualize reconstruction reliability.
CRIS-Team Scopus Importer:2023-03-08
Keywords: Bayes' theorem; Convolutional neural network; Epistemic uncertainty estimation; Image reconstruction; Magnetic resonance imaging; Total deep variation; Undersampled MRI

CG-SENSE revisited: Results from the first ISMRM reproducibility challenge - Maier, Oliver; Baete, Steven Hubert; Fyrdahl, Alexander; Hammernik, Kerstin; Harrevelt, Seb; Kasper, Lars; Karakuzu, Agah; Loecher, Michael; Patzig, Franz; Tian, Ye; Wang, Ke; Gallichan, Daniel; et al. (Magnetic Resonance in Medicine, 2021)
Purpose: The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of “Advances in sensitivity encoding with arbitrary k-space trajectories" by Pruessmann et al. Methods: The task of the challenge was to reconstruct radially acquired multicoil k-space data (brain/heart) following the method in the original paper, reproducing its key figures. Results were compared to consolidated reference implementations created after the challenge, accounting for the two most common programming languages used in the submissions (Matlab/Python). Results: Visually, differences between submissions were small. Pixel-wise differences originated from image orientation, assumed field-of-view, or resolution. The reference implementations were in good agreement, both visually and in terms of image similarity metrics. Discussion and Conclusion: While the description level of the published algorithm enabled participants to reproduce CG-SENSE in general, details of the implementation varied, for example, density compensation or Tikhonov regularization. Implicit assumptions about the data lead to further differences, emphasizing the importance of sufficient metadata accompanying open datasets. Defining reproducibility quantitatively turned out to be nontrivial for this image reconstruction challenge, in the absence of ground-truth results. Typical similarity measures like NMSE of SSIM were misled by image intensity scaling and outlier pixels. Thus, to facilitate reproducibility, researchers are encouraged to publish code and data alongside the original paper. Future methodological papers on MR image reconstruction might benefit from the consolidated reference implementations of CG-SENSE presented here, as a benchmark for methods comparison.
CRIS-Team Scopus Importer:2021-08-17
Keywords: CG-SENSE; image reconstruction; MRI; nonuniform sampling; NUFFT; reproducibility

CineVN: Variational network reconstruction for rapid functional cardiac cine MRI - Vornehm, Marc; Wetzl, Jens; Giese, Daniel; Fürnrohr, Florian; Pang, Jianing; Chow, Kelvin; Gebker, Rolf; Ahmad, Rizwan; Knoll, Florian (Magnetic Resonance in Medicine, 2025)
Purpose To develop a reconstruction method for highly accelerated cardiac cine MRI with high spatiotemporal resolution and low temporal blurring, and to demonstrate accurate estimation of ventricular volumes and myocardial strain in healthy subjects and in patients. Methods The proposed method, called CineVN, employs a spatiotemporal Variational Network combined with conjugate gradient descent for optimized data consistency and improved image quality. The method is first evaluated on retrospectively undersampled cine MRI data in terms of image quality. Then, prospectively accelerated data are acquired in 18 healthy subjects both segmented over two heartbeats per slice as well as in real time with 1.6 mm isotropic resolution. Ventricular volumes and strain parameters are computed and compared to a compressed sensing reconstruction and to a conventional reference cine MRI acquisition. Lastly, the method is demonstrated in 46 patients and ventricular volumes and strain parameters are evaluated. Results CineVN outperformed compressed sensing in image quality metrics on retrospectively undersampled data. Functional parameters and myocardial strain were the most accurate for CineVN compared to two state-of-the-art compressed sensing methods. Conclusion Deep learning-based reconstruction using our proposed method enables accurate evaluation of cardiac function in real-time cine MRI with high spatiotemporal resolution. This has the potential to improve cardiac imaging particularly for patients with arrhythmia or impaired breath-hold capability.<br

Comparative Evaluation of Deep Learning and Compressed Sensing Methods for Dynamic Contrast-Enhanced MRI Reconstruction - Gösche, Erik; Tan, Zhengguo; Flaßkamp, Kathrin; Kim, Sungheon Gene; Knoll, Florian (2025 ISMRM & ISMRT Annual Meeting & Exhibition, 2025)

Comparative Study on Co-registration Techniques for Diffusion-Weighted Breast MRI and Improved ADC Mapping - Brock, Luise; Liebert, Andrzej; Schreiter, Hannes; Skwierawska, Dominika; Ehring, Chris; Eberle, Jessica; Laun, Frederik Bernd; Uder, Michael; Kapsner, Lorenz; Ohlmeyer, Sabine; Hadler, Dominique; Knoll, Florian; et al. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2024)
Breast magnetic resonance imaging (MRI) increasingly incorporates diffusion-weighted imaging (DWI) sequences. DWI allows to calculate apparent diffusion coefficient (ADC) maps which can supplement differentiation between malignant and benign breast lesions. However, artifacts in DWI are not infrequent, e.g., due to patient motion, pulsation or other sources, which can cause shifts between the different b-value acquisitions and affect the accuracy of the ADC map. This IRB-approved, retrospective study includes n = 141 women with breast lesions undergoing breast MRI including an ultra-high b-value DWI acquisition (b = 1500s/mm2). Thirteen different rigid and non-rigid co-registration methods facilitated by the ANTs library were evaluated. The targets for co-registration were different b-value DWI. ADC maps were calculated from the co-registered DWI images and analyzed quantitatively, measuring mean, standard deviation, and within-lesion coefficient of variance (CoV), using repeated-measures ANOVA alongside Dunnett post-hoc tests in manual segmentations of the target lesions performed on ultra-high b-value images. In addition, we evaluated between-patient CoV and area under the curve (AUC) for differentiation of benign and malignant lesions, as well as sensitivity, specificity, and accuracy to measure clinical efficacy. Amongst the n = 13 co-registration techniques, the iterative SyNCC co-registration method with ultra-high b-value DWI images as target demonstrated the most notable improvement in standard deviation and within-lesion CoV while maintaining consistent mean ADC values. However, none of the co-registration method significantly improved the differentiation of lesion types. Nonetheless, the study offers a detailed comparison of co-registration methods for DWI images in ADC map generation.
CRIS-Team Scopus Importer:2024-11-08
Keywords: ADC; Breast MRI; Co-registration

Coupled regularization with multiple data discrepancies - Holler, Martin; Huber, Richard; Knoll, Florian (Inverse Problems, 2018)
We consider a class of regularization methods for inverse problems where a coupled regularization is employed for the simultaneous reconstruction of data from multiple sources. Applications for such a setting can be found in multi-spectral or multi-modality inverse problems, but also in inverse problems with dynamic data. We consider this setting in a rather general framework and derive stability and convergence results, including convergence rates. In particular, we show how parameter choice strategies adapted to the interplay of different data channels allow to improve upon convergence rates that would be obtained by treating all channels equally. Motivated by concrete applications, our results are obtained under rather general assumptions that allow to include the Kullback-Leibler divergence as data discrepancy term. To simplify their application to concrete settings, we further elaborate several practically relevant special cases in detail. To complement the analytical results, we also provide an algorithmic framework and source code that allows to solve a class of jointly regularized inverse problems with any number of data discrepancies. As concrete applications, we show numerical results for multi-contrast MR and joint MR-PET reconstruction.
CRIS-Team Scopus Importer:2021-08-17
Keywords: coupled regularization; dynamic inverse problems; ill-posed problems; Kullback Leibler divergence; multi-modality/multi-spectral inverse problems; multi-parameter regularization; PET-MR imaging

Data Consistent Variational Networks for Zero-shot Self-supervised MR Reconstruction - Fürnrohr, Florian; Wetzl, Jens; Vornehm, Marc; Giese, Daniel; Knoll, Florian (Bildverarbeitung für die Medizin 2024, 2024)
Variational Networks are a common approach in deep learning-based accelerated MR reconstruction. Due to their architecture, they may however fail in enforcing data consistency.We propose an adjustment to the Variational Network, integrating an optimization block that ensures consistency with the measured kspace points. We show the superiority of the method for zero-shot self-supervised 3D reconstruction quantitatively on retrospectively undersampled knee-data, and qualitatively in prospectively undersampled MR angiography images.<br

Death by Retrospective Undersampling - Caveats and Solutions for Learning-Based MRI Reconstructions - Rajput, Junaid Rasool; Weinmüller, Simon; Endres, Jonathan; Dawood, Peter; Knoll, Florian; Maier, Andreas; Zaiß, Moritz (Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. 27th International Conference, Marrakesh, Morocco, October 6–10, 2024, Proceedings, Part VII, 2024)
This study challenges the validity of retrospective undersampling in MRI data science by analysis via an MRI physics simulation. We demonstrate that retrospective undersampling, a method often used to create training data for reconstruction models, can inherently alter MRI signals from their prospective counterparts. This arises from the sequential nature of MRI acquisition, where undersampling post-acquisition effectively alters the MR sequence and the magnetization dynamic in a non-linear fashion. We show that even in common sequences, this effect can make learning-based reconstructions unreliable. Our simulation provides both, (i) a tool for generating accurate prospective undersampled datasets for analysis of such effects, or for MRI training data augmentation, and (ii) a differentiable reconstruction operator that models undersampling correctly. The provided insights are crucial for the development and evaluation of AI-driven acceleration of diagnostic MRI tools.
CRIS-Team Scopus Importer:2025-02-04
Keywords: Bloch simulation; Magnetization; Retrospective undersampling

Deep Learning-Based Accelerated MR Cholangiopancreatography Without Fully-Sampled Data - Kim, Jinho; Nickel, Marcel Dominik; Knoll, Florian (NMR in Biomedicine, 2025)
The purpose of this study was to accelerate MR cholangiopancreatography (MRCP) acquisitions using deep learning-based (DL) reconstruction at 3 and 0.55 T. A total of 35 healthy volunteers underwent conventional twofold accelerated MRCP scans at field strengths of 3 and 0.55 T. We trained DL reconstructions using two different training strategies, supervised (SV) and self-supervised (SSV), with retrospectively sixfold undersampled data obtained at 3 T. We then evaluated the DL reconstructions against standard techniques, parallel imaging (PI) and compressed sensing (CS), focusing on peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as metrics. We also tested DL reconstructions with prospectively accelerated acquisitions and evaluated their robustness when changing fields strengths from 3 to 0.55 T. DL reconstructions demonstrated a reduction in average acquisition time from 599/542 to 255/180 s for MRCP at 3 T/0.55 T. In both retrospective and prospective undersampling, PSNR and SSIM of DL reconstructions were higher than those of PI and CS. At the same time, DL reconstructions preserved the image quality of undersampled data, including sharpness and the visibility of hepatobiliary ducts. In addition, both DL approaches produced high-quality reconstructions at 0.55 T. In summary, DL reconstructions trained for highly accelerated MRCP enabled a reduction in acquisition time by a factor of 2.4/3.0 at 3 T/0.55 T while maintaining the image quality of conventional acquisitions. <
CRIS-Team Scopus Importer:2025-02-14
Keywords: accelerated reconstruction; deep learning; image reconstruction; magnetic resonance cholangiopancreatography; self-supervised training; supervised training

Deep learning-based accelerated T1 mapping for cardiac MRI - Amsel, Daniel; Vornehm, Marc; Wetzl, Jens; Schmidt, Michaela; Knoll, Florian (25. Jahrestagung der Deutschen Sektion der ISMRM, 2023)

Deep learning-based image reconstruction for higher resolution cardiac T1 mapping - Amsel, Daniel; Vornehm, Marc; Wetzl, Jens; Schmidt, Michaela; Tillmanns, Christoph; Gebker, Rolf; Giese, Daniel; Knoll, Florian; Kuestner, Thomas (Proceedings of the International Society for Magnetic Resonance in Medicine, 2024)

Deep Learning-Based Reconstruction of Accelerated Cardiac Cine MRI at 0.55T - Vornehm, Marc; Wetzl, Jens; Giese, Daniel; Pang, Jianing; Ahmad, Rizwan; Knoll, Florian (Proceedings of the International Society for Magnetic Resonance in Medicine, 2023)

Deep Learning-based Reconstruction of Accelerated MR Cholangiopancreatography - Kim, Jinho; Nickel, Marcel Dominik; Knoll, Florian (Deep Learning-based Reconstruction of Accelerated MR Cholangiopancreatography, 2024)

Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues - Knoll, Florian; Hammernik, Kerstin; Zhang, Chi; Moeller, Steen; Pock, Thomas; Sodickson, Daniel K.; Akcakaya, Mehmet (IEEE Signal Processing Magazine, 2020)
Following the success of deep learning in a wide range of applications, neural network-based machine-learning techniques have received interest as a means of accelerating magnetic resonance imaging (MRI). A number of ideas inspired by deeplearning techniques for computer vision and image processing have been successfully applied to nonlinear image reconstruction in the spirit of compressed sensing for both low-dose computed tomography and accelerated MRI. The additional integration of multicoil information to recover missing k-space lines in the MRI reconstruction process is studied less frequently, even though it is the de facto standard for the currently used accelerated MR acquisitions. This article provides an overview of the recent machine-learning approaches that have been proposed specifically for improving parallel imaging. A general background introduction to parallel MRI is given and structured around the classical view of image- and k-space-based methods. Linear and nonlinear methods are covered, followed by a discussion of the recent efforts to further improve parallel imaging using machine learning and, specifically, artificial neural networks. Image domain-based techniques that introduce improved regularizers are covered as well as k-space-based methods, where the focus is on better interpolation strategies using neural networks. Issues and open problems are discussed and recent efforts for producing open data sets and benchmarks for the community are examined.
CRIS-Team Scopus Importer:2021-08-17

Deep Learning Reconstruction Enables Highly Accelerated Biparametric MR Imaging of the Prostate - Johnson, Patricia M.; Tong, Angela; Donthireddy, Awani; Melamud, Kira; Petrocelli, Robert; Smereka, Paul; Qian, Kun; Keerthivasan, Mahesh B.; Chandarana, Hersh; Knoll, Florian (Journal of Magnetic Resonance Imaging, 2022)
Background: Early diagnosis and treatment of prostate cancer (PCa) can be curative; however, prostate-specific antigen is a suboptimal screening test for clinically significant PCa. While prostate magnetic resonance imaging (MRI) has demonstrated value for the diagnosis of PCa, the acquisition time is too long for a first-line screening modality. Purpose: To accelerate prostate MRI exams, utilizing a variational network (VN) for image reconstruction. Study Type: Retrospective. Subjects: One hundred and thirteen subjects (train/val/test: 70/13/30) undergoing prostate MRI. Field Strength/Sequence: 3.0 T; a T2 turbo spin echo (TSE) T2-weighted image (T2WI) sequence in axial and coronal planes, and axial echo-planar diffusion-weighted imaging (DWI). Assessment: Four abdominal radiologists evaluated the image quality of VN reconstructions of retrospectively under-sampled biparametric MRIs (bp-MRI), and standard bp-MRI reconstructions for 20 test subjects (studies). The studies included axial and coronal T2WI, DWI B50 seconds/mm2 and B1000 seconds/mm (4-fold T2WI, 3-fold DWI), all of which were evaluated separately for image quality on a Likert scale (1: non-diagnostic to 5: excellent quality). In another 10 test subjects, three readers graded lesions on bp-MRI—which additionally included calculated B1500 seconds/mm2, and apparent diffusion coefficient map—according to the Prostate Imaging Reporting and Data System (PI-RADS v2.1), for both VN and standard reconstructions. Accuracy of PI-RADS ≥3 for clinically significant cancer was computed. Projected scan time of the retrospectively under-sampled biparametric exam was also computed. Statistical Tests: One-sided Wilcoxon signed-rank test was used for comparison of image quality. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated for lesion detection and grading. Generalized estimating equation with cluster effect was used to compare differences between standard and VN bp-MRI. A P-value of <0.05 was considered statistically significant. Results: Three of four readers rated no significant difference for overall quality between the standard and VN axial T2WI (Reader 1: 4.00 ± 0.56 (Standard), 3.90 ± 0.64 (VN) P = 0.33; Reader 2: 4.35 ± 0.74 (Standard), 3.80 ± 0.89 (VN) P = 0.003; Reader 3: 4.60 ± 0.50 (Standard), 4.55 ± 0.60 (VN) P = 0.39; Reader 4: 3.65 ± 0.99 (Standard), 3.60 ± 1.00 (VN) P = 0.38). All four readers rated no significant difference for overall quality between standard and VN DWI B1000 seconds/mm2 (Reader 1: 2.25 ± 0.62 (Standard), 2.45 ± 0.75 (VN) P = 0.96; Reader 2: 3.60 ± 0.92 (Standard), 3.55 ± 0.82 (VN) P = 0.40; Reader 3: 3.85 ± 0.72 (Standard), 3.55 ± 0.89 (VN) P = 0.07; Reader 4: 4.70 ± 0.76 (Standard); 4.60 ± 0.73 (VN) P = 0.17) and three of four readers rated no significant difference for overall quality between standard and VN DWI B50 seconds/mm2 (Reader 1: 3.20 ± 0.70 (Standard), 3.40 ± 0.75 (VN) P = 0.98; Reader 2: 2.85 ± 0.81 (Standard), 3.00 ± 0.79 (VN) P = 0.93; Reader 3: 4.45 ± 0.72 (Standard), 4.05 ± 0.69 (VN) P = 0.02; Reader 4: 4.50 ± 0.69 (Standard), 4.45 ± 0.76 (VN) P = 0.50). In the lesion evaluation study, there was no significant difference in the number of PI-RADS ≥3 lesions identified on standard vs. VN bp-MRI (P = 0.92, 0.59, 0.87) with similar sensitivity and specificity for clinically significant cancer. The average scan time of the standard clinical biparametric exam was 11.8 minutes, and this was projected to be 3.2 minutes for the accelerated exam. Data Conclusion: Diagnostic accelerated biparametric prostate MRI exams can be performed using deep learning methods in <4 minutes, potentially enabling rapid screening prostate MRI. Level of Evidence: 3. Technical Efficacy: Stage 5.
CRIS-Team Scopus Importer:2023-03-08
Keywords: accelerated imaging; deep learning; image reconstruction; prostate MRI

Deep Learning Reconstruction Enables Prospectively Accelerated Clinical Knee MRI - Johnson, Patricia M.; Lin, Dana J.; Zbontar, Jure; Zitnick, C. Lawrence; Sriram, Anuroop; Muckley, Matthew; Babb, James S.; Kline, Mitchell; Ciavarra, Gina; Alaia, Erin; Samim, Mohammad; Walter, William R.; et al. (Radiology, 2023)
Background MRI is a powerful diagnostic tool with a long acquisition time. Recently, deep learning (DL) methods have provided accelerated high-quality image reconstructions from undersampled data, but it is unclear if DL image reconstruction can be reliably translated to everyday clinical practice. Purpose To determine the diagnostic equivalence of prospectively accelerated DL-reconstructed knee MRI compared with conventional accelerated MRI for evaluating internal derangement of the knee in a clinical setting. Materials and Methods A DL reconstruction model was trained with images from 298 clinical 3-T knee examinations. In a prospective analysis, patients clinically referred for knee MRI underwent a conventional accelerated knee MRI protocol at 3 T followed by an accelerated DL protocol between January 2020 and February 2021. The equivalence of the DL reconstruction of the images relative to the conventional images for the detection of an abnormality was assessed in terms of interchangeability. Each examination was reviewed by six musculoskeletal radiologists. Analyses pertaining to the detection of meniscal or ligament tears and bone marrow or cartilage abnormalities were based on four-point ordinal scores for the likelihood of an abnormality. Additionally, the protocols were compared with use of four-point ordinal scores for each aspect of image quality: overall image quality, presence of artifacts, sharpness, and signal-to-noise ratio. Results A total of 170 participants (mean age ± SD, 45 years ± 16; 76 men) were evaluated. The DL-reconstructed images were determined to be of diagnostic equivalence with the conventional images for detection of abnormalities. The overall image quality score, averaged over six readers, was significantly better (P < .001) for the DL than for the conventional images. Conclusion In a clinical setting, deep learning reconstruction enabled a nearly twofold reduction in scan time for a knee MRI and was diagnostically equivalent with the conventional protocol. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Roemer in this issue.
CRIS-Team Scopus Importer:2023-04-21

Digital reference object toolkit of breast DCE MRI for quantitative evaluation of image reconstruction and analysis methods - Bae, Jonghyun; Tan, Zhengguo; Solomon, Eddy; Huang, Zhengnan; Heacock, Laura; Moy, Linda; Knoll, Florian; Kim, Sungheon Gene (Magnetic Resonance in Medicine, 2024)
Purpose: To develop a digital reference object (DRO) toolkit to generate realistic breast DCE-MRI data for quantitative assessment of image reconstruction and data analysis methods. Methods: A simulation framework in a form of DRO toolkit has been developed using the ultrafast and conventional breast DCE-MRI data of 53 women with malignant (n = 25) or benign (n = 28) lesions. We segmented five anatomical regions and performed pharmacokinetic analysis to determine the ranges of pharmacokinetic parameters for each segmented region. A database of the segmentations and their pharmacokinetic parameters is included in the DRO toolkit that can generate a large number of realistic breast DCE-MRI data. We provide two potential examples for our DRO toolkit: assessing the accuracy of an image reconstruction method using undersampled simulated radial k-space data and assessing the impact of the (Formula presented.) field inhomogeneity on estimated parameters. Results: The estimated pharmacokinetic parameters for each region showed agreement with previously reported values. For the assessment of the reconstruction method, it was found that the temporal regularization resulted in significant underestimation of estimated parameters by up to 57% and 10% with the weighting factor λ = 0.1 and 0.01, respectively. We also demonstrated that spatial discrepancy of (Formula presented.) and (Formula presented.) increase to about 33% and 51% without correction for (Formula presented.) field. Conclusion: We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE-MRI reconstruction and analysis methods.
CRIS-Team Scopus Importer:2024-05-31
Keywords: digital reference object; dynamic contrast-enhanced MRI; GRASP; ultrafast DCE MRI

Direct Reconstruction of Tracer Kinetic Parameter Maps in Abbreviated Breast MRI - Brenes, Arianna; Tan, Zhengguo; Bae, Jonghyun; Solomon, Eddy; Gösche, Erik; Knoll, Florian; Kim, Sungheon Gene (2025 ISMRM & ISMRT Annual Meeting & Exhibition, 2025)

Domain Influence in MRI Medical Image Segmentation: Spatial Versus k-Space Inputs - Gösche, Erik; Eghbali, Reza; Knoll, Florian; Rauschecker, Andreas M (Machine Learning in Medical Imaging, 2024)
Transformer-based networks applied to image patches have achieved cutting-edge performance in many vision tasks. However, lacking the built-in bias of convolutional neural networks (CNN) for local image statistics, they require large datasets and modifications to capture relationships between patches, especially in segmentation tasks. Images in the frequency domain might be more suitable for the attention mechanism, as local features are represented globally. By transforming images into the frequency domain, local features are represented globally. Due to MRI data acquisition properties, these images are particularly suitable. This work investigates how the image domain (spatial or k-space) affects segmentation results of deep learning (DL) models, focusing on attention-based networks and other non-convolutional models based on MLPs. We also examine the necessity of additional positional encoding for Transformer-based networks when input images are in the frequency domain. For evaluation, we pose a skull stripping task and a brain tissue segmentation task. The attention-based models used are PerceiverIO and a vanilla Transformer encoder. To compare with non-attention-based models, an MLP and ResMLP are also trained and tested. Results are compared with the Swin-Unet, the state-of-the-art medical image segmentation model. Experimental results indicate that using k-space for the input domain can significantly improve segmentation results. Also, additional positional encoding does not seem beneficial for attention-based networks if the input is in the frequency domain. Although none of the models matched the Swin-Unet’s performance, the less complex models showed promising improvements with a different domain choic
Keywords: Medical Image Segmentation; Frequency Domain Analysis; Attention-based Networks

End-to-End Variational Networks for Accelerated MRI Reconstruction - Sriram, Anuroop; Zbontar, Jure; Murrell, Tullie; Defazio, Aaron; Zitnick, C. Lawrence; Yakubova, Nafissa; Knoll, Florian; Johnson, Patricia (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2020)
The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing). While the combination of these methods has the potential to allow much faster scan times, reconstruction from such undersampled multi-coil data has remained an open problem. In this paper, we present a new approach to this problem that extends previously proposed variational methods by learning fully end-to-end. Our method obtains new state-of-the-art results on the fastMRI dataset [16] for both brain and knee MRIs.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Deep learning; End-to-end learning; MRI acceleration

Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach - Bae, Jonghyun; Huang, Zhengnan; Knoll, Florian; Geras, Krzysztof; Sood, Terlika Pandit; Feng, Li; Heacock, Laura; Moy, Linda; Kim, Sungheon Gene (Magnetic Resonance in Medicine, 2022)
Purpose: To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI. Methods: A deep convolutional network was trained with numerically simulated data to estimate the CIF. The trained network was tested using simulated lesion data and used to estimate voxel-wise CIF for pharmacokinetic model analysis of breast DCE-MRI data using an abbreviated protocol from women with malignant (n = 25) and benign (n = 28) lesions. The estimated parameters were used to build a logistic regression model to detect the malignancy. Result: The pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for all parameters. Testing the diagnostic performance with the estimated parameters, the conventional approach with arterial input function (AIF) showed an area under the curve (AUC) between 0.76 and 0.87, and the proposed approach with CIF demonstrated similar performance with an AUC between 0.79 and 0.81. Conclusion: This study shows the feasibility of estimating voxel-wise CIF using a deep neural network. The proposed approach could eliminate the need to measure AIF manually without compromising the diagnostic performance to detect the malignancy in the clinical setting.
CRIS-Team Scopus Importer:2023-03-08
Keywords: arterial input function; breast cancer; capillary input function; deep learning; dynamic contrast enhanced MRI

Evaluation of Parallel Level Sets and Bowsher's Method as Segmentation-Free Anatomical Priors for Time-of-Flight PET Reconstruction - Schramm, Georg; Holler, Martin; Rezaei, Ahmadreza; Vunckx, Kathleen; Knoll, Florian; Bredies, Kristian; Boada, Fernando; Nuyts, Johan (IEEE Transactions on Medical Imaging, 2018)
In this article, we evaluate Parallel Level Sets (PLS) and Bowsher's method as segmentation-free anatomical priors for regularized brain positron emission tomography (PET) reconstruction. We derive the proximity operators for two PLS priors and use the EM-TV algorithm in combination with the first order primal-dual algorithm by Chambolle and Pock to solve the non-smooth optimization problem for PET reconstruction with PLS regularization. In addition, we compare the performance of two PLS versions against the symmetric and asymmetric Bowsher priors with quadratic and relative difference penalty function. For this aim, we first evaluate reconstructions of 30 noise realizations of simulated PET data derived from a real hybrid positron emission tomography/magnetic resonance imaging (PET/MR) acquisition in terms of regional bias and noise. Second, we evaluate reconstructions of a real brain PET/MR data set acquired on a GE Signa time-of-flight PET/MR in a similar way. The reconstructions of simulated and real 3D PET/MR data show that all priors were superior to post-smoothed maximum likelihood expectation maximization with ordered subsets (OSEM) in terms of bias-noise characteristics in different regions of interest where the PET uptake follows anatomical boundaries. Our implementation of the asymmetric Bowsher prior showed slightly superior performance compared with the two versions of PLS and the symmetric Bowsher prior. At very high regularization weights, all investigated anatomical priors suffer from the transfer of non-shared gradients.
CRIS-Team Scopus Importer:2021-08-17
Keywords: anatomical priors; Image reconstruction; positron emission tomography (PET)

Evaluation of SparseCT on patient data using realistic undersampling models - Chen, Baiyu; Muckley, Matthew; Sodickson, Aaron; O'Donnell, Thomas; Knoll, Florian; Sodickson, Daniel; Otazo, Ricardo (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2018)
Compressed sensing (CS) requires undersampled projection data, but CT x-ray tubes cannot be pulsed quickly enough to achieve reduced-view undersampling. We propose an alternative within-view undersampling strategy, named SparseCT, as a practical CS technique to reduce CT radiation dose. SparseCT uses a multi-slit collimator (MSC) to interrupt the x-ray beam, thus acquiring undersampled projection data directly. This study evaluated the feasibility of SparseCT via simulations using a standardized patient dataset. Because the projection data in the dataset are fully sampled, we retrospectively undersample the projection data to simulate SparseCT acquisitions in three steps. First, two photon distributions were simulated, representing the cases with and without the MSC. Second, by comparing the two distributions, detector regions with more than 80% of x-ray blocked by the MSC were identified and the corresponding projection data were not used. Third, noise was inserted into the rest of the projection data to account for the increase in quantum noise due to reduced flux (partial MSC blockage). The undersampled projection data were then reconstructed iteratively using a penalized weighted least squares cost function with the conjugate gradient algorithm. The image reconstruction problem promotes sparsity in the solution and incorporates the undersampling model. Weighting factors were applied to the projection data during the reconstruction to account for the noise variation in the undersampled projection. Compared to images acquired with reduced tube current (provided in the standardized patient dataset), SparseCT undersampling presented less image noise while preserving pathologies and fine structures such as vessels in the reconstructed images.
CRIS-Team Scopus Importer:2021-08-17
Keywords: compressed sensing (CS); Computed tomography (CT); penumbra; sparseCT; undersampling

Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge - Johnson, Patricia M.; Jeong, Geunu; Hammernik, Kerstin; Schlemper, Jo; Qin, Chen; Duan, Jinming; Rueckert, Daniel; Lee, Jingu; Pezzotti, Nicola; De Weerdt, Elwin; Yousefi, Sahar; Elmahdy, Mohamed S.; et al. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021)
The 2019 fastMRI challenge was an open challenge designed to advance research in the field of machine learning for MR image reconstruction. The goal for the participants was to reconstruct undersampled MRI k-space data. The original challenge left an open question as to how well the reconstruction methods will perform in the setting where there is a systematic difference between training and test data. In this work we tested the generalization performance of the submissions with respect to various perturbations, and despite differences in model architecture and training, all of the methods perform very similarly.
CRIS-Team Scopus Importer:2023-03-08

Exploring GPT-4 as MR Sequence and Reconstruction Programming Assistant - Zaiß, Moritz; Rajput, Junaid Rasool; Dang, Hoai Nam; Golkov, Vladimir; Cremers, Daniel; Knoll, Florian; Maier, Andreas (Bildverarbeitung für die Medizin 2024. BVM 2024, 2024)
In this study, we explore the potential of generative pre-trained transformer (GPT), as a coding assistant for MRI sequence programming using the Pulseq framework. The programming of MRI sequences is traditionally a complex and time-consuming task, and the Pulseq standard has recently simplified this process. It allows researchers to define and generate complex pulse sequences used in MRI experiments. Leveraging GPT-4’s capabilities in natural language generation, we adapted it for MRI sequence programming, creating a specialized assistant named GPT4MR. Our tests involved generating various MRI sequences, revealing that GPT-4, guided by a tailored prompt, outperformed GPT-3.5, producing fewer errors and demonstrating improved reasoning. Despite limitations in handling complex sequences, GPT4MR corrected its own errors and successfully generated code with step-by-step instructions. The study showcases GPT4MR’s ability to accelerate MRI sequence development, even for novel ideas absent in its training set. While further research and improvement are needed to address complexity limitations, a well-designed prompt enhances performance. The findings propose GPT4MR as a valuable MRI sequence programming assistant, streamlining prototyping and development. The future prospect involves integrating a PyPulseq plugin into lightweight, open-source LLMs, potentially revolutionizing MRI sequence development and prototyping.
CRIS-Team Scopus Importer:2024-03-29

Exploring the Acceleration Limits of Deep Learning Variational Network–based Two-dimensional Brain MRI - Radmanesh, Alireza; Muckley, Matthew J.; Murrell, Tullie; Lindsey, Emma; Sriram, Anuroop; Knoll, Florian; Sodickson, Daniel K.; Lui, Yvonne W. (Radiology: Artificial Intelligence, 2022)
Purpose: To explore the limits of deep learning–based brain MRI reconstruction and identify useful acceleration ranges for generalpurpose imaging and potential screening. Materials and Methods: In this retrospective study conducted from 2019 through 2021, a model was trained for reconstruction on 5847 brain MR images. Performance was evaluated across a wide range of accelerations (up to 100-fold along a single phase-encoded direction for two-dimensional [2D] sections) on the fastMRI test set collected at New York University, consisting of 558 image volumes. In a sample of 69 volumes, reconstructions were classified by radiologists for identification of two clinical thresholds: (a) general-purpose diagnostic imaging and (b) potential use in a screening protocol. A Monte Carlo procedure was developed to estimate reconstruction error with only undersampled data. The model was evaluated on both in-domain and out-of-domain data. The 95% CIs were calculated using the percentile bootstrap method. Results: Radiologists rated 100% of 69 volumes as having sufficient image quality for general-purpose imaging at up to 4× acceleration and 65 of 69 volumes (94%) as having sufficient image quality for screening at up to 14× acceleration. The Monte Carlo procedure estimated ground truth peak signal-to-noise ratio and mean squared error with coefficients of determination greater than 0.5 at 2× to 20× acceleration levels. Out-of-distribution experiments demonstrated the model’s ability to produce images substantially distinct from the training set, even at 100× acceleration. Conclusion: For 2D brain images using deep learning–based reconstruction, maximum acceleration for potential screening was three to four times higher than that for diagnostic general-purpose imaging.
CRIS-Team Scopus Importer:2022-12-23
Keywords: Deep Learning; High Acceleration; MRI Reconstruction; Out of Distribution; Screening

FastMRI: A publicly available raw k-space and DICOM dataset of knee images for accelerated MR image reconstruction using machine learning - Knoll, Florian; Zbontar, Jure; Sriram, Anuroop; Muckley, Matthew J.; Bruno, Mary; Defazio, Aaron; Parente, Marc; Geras, Krzysztof J.; Katsnelson, Joe; Chandarana, Hersh; Zhang, Zizhao; Drozdzalv, Michal; et al. (Radiology: Artificial Intelligence, 2020)
A publicly available dataset containing k-space and image data of knee examinations for accelerated MR image reconstruction using machine learning is presented.
CRIS-Team Scopus Importer:2023-03-08

FastMRI Breast: A Publicly Available Radial k-Space Dataset of Breast Dynamic Contrast-enhanced MRI - Solomon, Eddy; Johnson, Patricia M.; Tan, Zhengguo; Tibrewala, Radhika; Lui, Yvonne W.; Knoll, Florian; Moy, Linda; Kim, Sungheon Gene; Heacock, Laura (Radiology: Artificial Intelligence, 2025)
The fastMRI breast dataset is the first large-scale dataset of radial k-space and Digital Imaging and Communications in Medicine data for breast dynamic contrast-enhanced MRI with case-level labels, and its public availability aims to advance fast and quantitative machine learning research.
CRIS-Team Scopus Importer:2025-02-07

fastMRI+, Clinical pathology annotations for knee and brain fully sampled magnetic resonance imaging data - Zhao, Ruiyang; Yaman, Burhaneddin; Zhang, Yuxin; Stewart, Russell; Dixon, Austin; Knoll, Florian; Huang, Zhengnan; Lui, Yvonne W.; Hansen, Michael S.; Lungren, Matthew P. (Scientific Data, 2022)
Improving speed and image quality of Magnetic Resonance Imaging (MRI) using deep learning reconstruction is an active area of research. The fastMRI dataset contains large volumes of raw MRI data, which has enabled significant advances in this field. While the impact of the fastMRI dataset is unquestioned, the dataset currently lacks clinical expert pathology annotations, critical to addressing clinically relevant reconstruction frameworks and exploring important questions regarding rendering of specific pathology using such novel approaches. This work introduces fastMRI+, which consists of 16154 subspecialist expert bounding box annotations and 13 study-level labels for 22 different pathology categories on the fastMRI knee dataset, and 7570 subspecialist expert bounding box annotations and 643 study-level labels for 30 different pathology categories for the fastMRI brain dataset. The fastMRI+ dataset is open access and aims to support further research and advancement of medical imaging in MRI reconstruction and beyond.
CRIS-Team Scopus Importer:2023-03-08

Fast reduction of undersampling artifacts in radial MR angiography with 3D total variation on graphics hardware - Knoll, Florian; Unger, Markus; Diwoky, Clemens; Clason, Christian; Pock, Thomas; Stollberger, Rudolf (Magnetic Resonance Materials in Physics Biology and Medicine, 2010)
Objective: Subsampling of radially encoded MRI acquisitions in combination with sparsity promoting methods opened a door to significantly increased imaging speed, which is crucial for many important clinical applications. In particular, it has been shown recently that total variation (TV) regularization efficiently reduces undersampling artifacts. The drawback of the method is the long reconstruction time which makes it impossible to use in daily clinical practice, especially if the TV optimization problem has to be solved repeatedly to select a proper regularization parameter. Materials and Methods: The goal of this work was to show that for the case of MR Angiography, TV filtering can be performed as a post-processing step, in contrast to the common approach of integrating TV penalties in the image reconstruction process. With this approach, it is possible to use TV algorithms with data fidelity terms in image space, which can be implemented very efficiently on graphic processing units (GPUs). The combination of a special radial sampling trajectory and a full 3D formulation of the TV minimization problem is crucial for the effectiveness of the artifact elimination process. Results and Conclusion: The computation times of GPU-TV show that interactive elimination of undersampling artifacts is possible even for large volume data sets, in particular allowing the interactive determination of the regularization parameter. Results from phantom measurements and in vivo angiography data sets show that 3D TV, together with the proposed sampling trajectory, leads to pronounced improvements in image quality. However, while artifact removal was very efficient for angiography data sets in this work, it cannot be expected that the proposed method of TV post-processing will work for arbitrary types of scans. © 2010 ESMRMB.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Accelerated imaging; Angiography; GPU computing; Radial sampling; Total variation

Fast T2 mapping with improved accuracy using undersampled spin-echo MRI and model-based reconstructions with a generating function - Sumpf, Tilman J.; Petrovic, Andreas; Uecker, Martin; Knoll, Florian; Frahm, Jens (IEEE Transactions on Medical Imaging, 2014)
A model-based reconstruction technique for accelerated T2 mapping with improved accuracy is proposed using undersampled Cartesian spin-echo magnetic resonance imaging (MRI) data. The technique employs an advanced signal model for T2 relaxation that accounts for contributions from indirect echoes in a train of multiple spin echoes. An iterative solution of the nonlinear inverse reconstruction problem directly estimates spin-density and T2 maps from undersampled raw data. The algorithm is validated for simulated data as well as phantom and human brain MRI at 3T. The performance of the advanced model is compared to conventional pixel-based fitting of echo-time images from fully sampled data. The proposed method yields more accurate T2 values than the mono-exponential model and allows for retrospective undersampling factors of at least 6. Although limitations are observed for very long T2 relaxation times, respective reconstruction problems may be overcome by a gradient dampening approach. The analytical gradient of the utilized cost function is included as Appendix. The source code is made available to the community.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Fast spin echo (FSE); indirect echoes; model-based reconstruction; relaxometry; T2 mapping

Gibbs ringing in diffusion MRI - Veraart, Jelle; Fieremans, Els; Jelescu, Ileana O.; Knoll, Florian; Novikov, Dmitry S. (Magnetic Resonance in Medicine, 2016)
Purpose: To study and reduce the effect of Gibbs ringing artifact on computed diffusion parameters. Methods: We reduce the ringing by extrapolating the k-space of each diffusion weighted image beyond the measured part by selecting an adequate regularization term. We evaluate several regularization terms and tune the regularization parameter to find the best compromise between anatomical accuracy of the reconstructed image and suppression of the Gibbs artifact. Results: We demonstrate empirically and analytically that the Gibbs artifact, which is typically observed near sharp edges in magnetic resonance images, has a significant impact on the quantification of diffusion model parameters, even for infinitesimal diffusion weighting. We find the second order total generalized variation to be a good choice for the penalty term to regularize the extrapolation of the k-space, as it provides a parsimonious representation of images, a practically full suppression of Gibbs ringing, and the absence of staircasing artifacts typical for total variation methods. Conclusions: Regularized extrapolation of the k-space data significantly reduces truncation artifacts without compromising spatial resolution in comparison to the default option of window filtering. In particular, accuracy of estimating diffusion tensor imaging and diffusion kurtosis imaging parameters improves so much that unconstrained fits become possible. Magn Reson Med 76:301–314, 2016. © 2015 Wiley Periodicals, Inc.
CRIS-Team Scopus Importer:2021-08-17
Keywords: diffusion kurtosis imaging; diffusion tensor imaging; extrapolation; total variation; truncation artifact

How to best match voxels - evaluating different sequential co-registration strategies for ultra-high b-value DWI in multiparametric breast MRI - Brock, Luise; Liebert, Andrzej; Schreiter, Hannes; Ehring, Chris; Eberle, Jessica; Laun, Frederik Bernd; Uder, Michael; Kapsner, Lorenz; Ohlmeyer, Sabine; Knoll, Florian; Bickelhaupt, Sebastian (2024 ISMRM & ISMRT Annual Meeting & Exhibition, 2024)

Image reconstruction for interrupted-beam x-ray CT on diagnostic clinical scanners - Muckley, Matthew J.; Chen, Baiyu; Vahle, Thomas; O'Donnell, Thomas; Knoll, Florian; Sodickson, Aaron D.; Sodickson, Daniel K.; Otazo, Ricardo (Physics in Medicine and Biology, 2019)
Low-dose x-ray CT is a major research area with high clinical impact. Compressed sensing using view-based sparse sampling and sparsity-promoting regularization has shown promise in simulations, but these methods can be difficult to implement on diagnostic clinical CT scanners since the x-ray beam cannot be switched on and off rapidly enough. An alternative to view-based sparse sampling is interrupted-beam sparse sampling. SparseCT is a recently-proposed interrupted-beam scheme that achieves sparse sampling by blocking a portion of the beam using a multislit collimator (MSC). The use of an MSC necessitates a number of modifications to the standard compressed sensing reconstruction pipeline. In particular, we find that SparseCT reconstruction is feasible within a model-based image reconstruction framework that incorporates data fidelity weighting to consider penumbra effects and source jittering to consider the effect of partial source obstruction. Here, we present these modifications and demonstrate their application in simulations and real-world prototype scans. In simulations compared to conventional low-dose acquisitions, SparseCT is able to achieve smaller normalized root-mean square differences and higher structural similarity measures on two reduction factors. In prototype experiments, we successfully apply our reconstruction modifications and maintain image resolution at quarter-dose reduction level. The SparseCT design requires only small hardware modifications to current diagnostic clinical scanners, opening up new possibilities for CT dose reduction.
CRIS-Team Scopus Importer:2021-08-17
Keywords: compressed sensing; interrupted-beam CT; low-dose CT

Improving the Speed of MRI with Artificial Intelligence - Johnson, Patricia M.; Recht, Michael P.; Knoll, Florian (Seminars in Musculoskeletal Radiology, 2020)
Magnetic resonance imaging (MRI) is a leading image modality for the assessment of musculoskeletal (MSK) injuries and disorders. A significant drawback, however, is the lengthy data acquisition. This issue has motivated the development of methods to improve the speed of MRI. The field of artificial intelligence (AI) for accelerated MRI, although in its infancy, has seen tremendous progress over the past 3 years. Promising approaches include deep learning methods for reconstructing undersampled MRI data and generating high-resolution from low-resolution data. Preliminary studies show the promise of the variational network, a state-of-the-art technique, to generalize to many different anatomical regions and achieve comparable diagnostic accuracy as conventional methods. This article discusses the state-of-the-art methods, considerations for clinical applicability, followed by future perspectives for the field.
CRIS-Team Scopus Importer:2021-08-17
Keywords: accelerated imaging; artificial intelligence; machine learning; magnetic resonance imaging

Inverse GANs for accelerated MRI reconstruction - Narnhofer, Dominik; Hammernik, Kerstin; Knoll, Florian; Pock, Thomas (Proceedings of SPIE - The International Society for Optical Engineering, 2019)
State-of-the-art algorithms for accelerated magnetic resonance image (MRI) reconstruction are nowadays dominated by deep learning-based techniques. However, the majority of these methods require the respective sampling patterns in training, which limits their application to a specific problem class. We propose an iterative reconstruction approach that incorporates the implicit prior provided by a generative adversarial network (GAN), which learns the probability distribution of uncorrupted MRI data in an off-line step. Since the unsupervised training of the GAN is completely independent of the measurement process, our method is in principle able to address multiple sampling modalities using a single pre-trained model. However, it turns out that the desired target images potentially lie outside the range space of the learned GAN, leading to reconstructions that resemble the target images only at a coarse level of detail. To overcome this issue, we propose a refinement scheme termed GAN prior adaption, that allows for additional adaption of the generating network with respect to the measured data. The proposed method is evaluated on multi-coil knee MRI data for different acceleration factors and compared to a classical as well as a deep learning-based approach showing promising quantitative and qualitative results.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Accelerated MRI; deep learning; GAN prior; machine learning; unsupervised learning

Joint MR-PET Reconstruction Using a Multi-Channel Image Regularizer - Knoll, Florian; Holler, Martin; Koesters, Thomas; Otazo, Ricardo; Bredies, Kristian; Sodickson, Daniel K. (IEEE Transactions on Medical Imaging, 2017)
While current state of the art MR-PET scanners enable simultaneous MR and PET measurements, the acquired data sets are still usually reconstructed separately. We propose a new multi-modality reconstruction framework using second order Total Generalized Variation (TGV) as a dedicated multi-channel regularization functional that jointly reconstructs images from both modalities. In this way, information about the underlying anatomy is shared during the image reconstruction process while unique differences are preserved. Results from numerical simulations and in-vivo experiments using a range of accelerated MR acquisitions and different MR image contrasts demonstrate improved PET image quality, resolution, and quantitative accuracy.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Iterative image reconstruction; MR-PET; multi-modality imaging; total generalized variation; variational regularization methods

Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions - Johnson, Patricia M.; Muckley, Matthew J.; Bruno, Mary; Kobler, Erich; Hammernik, Kerstin; Pock, Thomas; Knoll, Florian (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019)
Magnetic resonance imaging is a leading image modality for many clinical applications; however, a significant drawback is the lengthy data acquisition. This motivates the development of methods for reconstruction of sparsely sampled image data. One such technique is the Variational Network (VN), a machine learning method that generalizes traditional iterative reconstruction techniques, learning the regularization term from large amounts of image data. Previously, with the VN technique, reconstruction of 4-fold accelerated knee images was shown to be highly successful. In this work we extend the VN approach to applications beyond knee imaging and evaluate the classic VN and a newly developed Unet-VN in 5 different anatomical regions. We evaluate the networks trained individually for each anatomical area as well as jointly trained with data from all anatomical areas. The VN and Unet-VN were trained to reconstruct 4-fold accelerated images of knees, brains, hips, ankles and shoulders. SSIM was calculated to quantitatively evaluate the reconstructed images. Results show that the Unet-VN outperforms the classic VN, both quantitatively – in terms of structural similarity – and qualitatively. The networks jointly trained with multi-anatomy data approach the performance of the individually trained networks and offer the simplicity of a single network for a range of clinical applications which has substantial benefit for clinical translation.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Machine learning; MR image reconstruction; Variational network

k-t Adaptive Regularization in Variational Networks for Cardiac Cine Reconstruction - Vornehm, Marc; Wetzl, Jens; Giese, Daniel; Knoll, Florian (Proceedings of the ISMRM Workshop on Data Sampling & Image Reconstruction, 2023)

Learning a variational network for reconstruction of accelerated MRI data - Hammernik, Kerstin; Klatzer, Teresa; Kobler, Erich; Recht, Michael P.; Sodickson, Daniel K.; Pock, Thomas; Knoll, Florian (Magnetic Resonance in Medicine, 2018)
Purpose: To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning. Theory and Methods: Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data. Results: The variational network approach is evaluated on a clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data. The variational network reconstructions outperform standard reconstruction algorithms, verified by quantitative error measures and a clinical reader study for regular sampling and acceleration factor 4. Conclusion: Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, that is, reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow. Magn Reson Med 79:3055–3071, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
CRIS-Team Scopus Importer:2021-08-17
Keywords: accelerated MRI; compressed sensing; deep learning; image reconstruction; parallel imaging; variational network

Low-Latency Reconstruction of Real-Time Cine MRI Using an Unrolled Network - Vornehm, Marc; Wetzl, Jens; Fürnrohr, Florian; Giese, Daniel; Ahmad, Rizwan; Knoll, Florian (Proceedings of the International Society for Magnetic Resonance in Medicine, 2024)

Low rank alternating direction method of multipliers reconstruction for MR fingerprinting - Asslander, Jakob; Cloos, Martijn A.; Knoll, Florian; Sodickson, Daniel K.; Hennig, Juergen; Lattanzi, Riccardo (Magnetic Resonance in Medicine, 2018)
Purpose: The proposed reconstruction framework addresses the reconstruction accuracy, noise propagation and computation time for magnetic resonance fingerprinting. Methods: Based on a singular value decomposition of the signal evolution, magnetic resonance fingerprinting is formulated as a low rank (LR) inverse problem in which one image is reconstructed for each singular value under consideration. This LR approximation of the signal evolution reduces the computational burden by reducing the number of Fourier transformations. Also, the LR approximation improves the conditioning of the problem, which is further improved by extending the LR inverse problem to an augmented Lagrangian that is solved by the alternating direction method of multipliers. The root mean square error and the noise propagation are analyzed in simulations. For verification, in vivo examples are provided. Results: The proposed LR alternating direction method of multipliers approach shows a reduced root mean square error compared to the original fingerprinting reconstruction, to a LR approximation alone and to an alternating direction method of multipliers approach without a LR approximation. Incorporating sensitivity encoding allows for further artifact reduction. Conclusion: The proposed reconstruction provides robust convergence, reduced computational burden and improved image quality compared to other magnetic resonance fingerprinting reconstruction approaches evaluated in this study. Magn Reson Med 79:83–96, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
CRIS-Team Scopus Importer:2021-08-17
Keywords: augmented Lagrangian; MRF; parallel imaging; parameter mapping; quantitative MRI; SVD

Machine learning for image reconstruction - Hammernik, Kerstin; Knoll, Florian (Elsevier, 2019)
This chapter provides an overview of current developments in the fast growing field of machine learning for medical image reconstruction. A comprehensive overview of recent developments is provided for a range of imaging applications. The main focus lies on a mathematical understanding how deep learning techniques can be employed for image reconstruction tasks, and how they can be connected to traditional approaches to solve inverse problems. Approaches are categorized based on the properties of the underlying optimization problems that need to be solved during the image reconstruction process and the domain(s) in which the neural networks process the data. Additional material includes discussions on availability and size of existing training data, initiatives towards data sharing and reproducible research, and the evaluation of the performance of machine learning based medical image reconstruction methods.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Convolutional neural networks; Data-driven regularization; Deep learning; Image reconstruction; Inverse problems; Medical imaging; Numerical optimization

Motion-Guided Deep Image Prior for Dynamic Cardiac MRI - Vornehm, Marc; Chen, Chong; Sultan, Muhammad Ahmad; Arshad, Syed Murtaza; Knoll, Florian; Ahmad, Rizwan (Proceedings of the International Society for Magnetic Resonance in Medicine, 2025)

MRI at low field: A review of software solutions for improving SNR - Ayde, Reina; Vornehm, Marc; Zhao, Yujiao; Knoll, Florian; Wu, Ed X.; Sarracanie, Mathieu (NMR in Biomedicine, 2025)
Low magnetic field magnetic resonance imaging (MRI) ( < 1 T) is regaining interest in the magnetic resonance (MR) community as a complementary, more flexible, and cost-effective approach to MRI diagnosis. Yet, the impaired signal-to-noise ratio (SNR) per square root of time, or SNR efficiency, leading in turn to prolonged acquisition times, still challenges its relevance at the clinical level. To address this, researchers investigate various hardware and software solutions to improve SNR efficiency at low field, including the leveraging of latest advances in computing hardware. However, there may not be a single recipe for improving SNR at low field, and it is key to embrace the challenges and limitations of each proposed solution. In other words, suitable solutions depend on the final objective or application envisioned for a low-field scanner and, more importantly, on the characteristics of a specific low field. In this review, we aim to provide an overview on software solutions to improve SNR efficiency at low field. First, we cover techniques for efficient k-space sampling and reconstruction. Then, we present post-acquisition techniques that enhance MR images such as denoising and super-resolution. In addition, we summarize recently introduced electromagnetic interference cancellation approaches showing great promises when operating in shielding-free environments. Finally, we discuss the advantages and limitations of these approaches that could provide directions for future application

Multicompartment magnetic resonance fingerprinting - Tang, Sunli; Fernandez-Granda, Carlos; Lannuzel, Sylvain; Bernstein, Brett; Lattanzi, Riccardo; Cloos, Martijn; Knoll, Florian; Asslander, Jakob (Inverse Problems, 2018)
Magnetic resonance fingerprinting (MRF) is a technique for quantitative estimation of spin-relaxation parameters from magnetic-resonance data. Most current MRF approaches assume that only one tissue is present in each voxel, which neglects intravoxel structure, and may lead to artifacts in the recovered parameter maps at boundaries between tissues. In this work, we propose a multicompartment MRF model that accounts for the presence of multiple tissues per voxel. The model is fit to the data by iteratively solving a sparse linear inverse problem at each voxel, in order to express the measured magnetization signal as a linear combination of a few elements in a precomputed fingerprint dictionary. Thresholding-based methods commonly used for sparse recovery and compressed sensing do not perform well in this setting due to the high local coherence of the dictionary. Instead, we solve this challenging sparse-recovery problem by applying reweighted-l1-norm regularization, implemented using an efficient interior-point method. The proposed approach is validated with simulated data at different noise levels and undersampling factors, as well as with a controlled phantom-imaging experiment on a clinical magnetic-resonance system.
CRIS-Team Scopus Importer:2021-08-17
Keywords: coherent dictionaries; magnetic resonance fingerprinting; multicompartment models; quantitative MRI; reweighted l1-norm; sparse recovery

Multi-dynamic deep image prior for cardiac MRI - Vornehm, Marc; Chen, Chong; Sultan, Muhammad A.; Arshad, Syed M.; Han, Yuchi; Knoll, Florian; Ahmad, Rizwan (Magnetic Resonance in Medicine, 2025)
Purpose: Cardiovascular magnetic resonance imaging is a powerful diagnostic tool for assessing cardiac structure and function. However, traditional breath-held imaging protocols pose challenges for patients with arrhythmias or limited breath-holding capacity. This work aims to overcome these limitations by developing a reconstruction framework that enables high-quality imaging in free-breathing conditions for various dynamic cardiac MRI protocols. Methods: Multi-Dynamic Deep Image Prior (M-DIP), a novel unsupervised reconstruction framework for accelerated real-time cardiac MRI, is introduced. To capture contrast or content variation, M-DIP first employs a spatial dictionary to synthesize a time-dependent intermediate image. Then, this intermediate image is further refined using time-dependent deformation fields that model cardiac and respiratory motion. Unlike prior DIP-based methods, M-DIP simultaneously captures physiological motion and frame-to-frame content variations, making it applicable to a wide range of dynamic applications. Results: We validate M-DIP using simulated MRXCAT cine phantom data as well as free-breathing real-time cine, single-shot late gadolinium enhancement (LGE), and first-pass perfusion data from clinical patients. Comparative analyses against state-of-the-art supervised and unsupervised approaches demonstrate M-DIP's performance and versatility. M-DIP achieved better image quality metrics on phantom data, higher reader scores on in-vivo cine and LGE data, and comparable scores on in-vivo perfusion data relative to another DIP-based approach. Conclusion: M-DIP enables high-quality reconstructions of real-time free-breathing cardiac MRI without requiring external training data. Its ability to model physiological motion and content variations makes it a promising approach for various dynamic imaging applications.
Created from Fastlane, Scopus look-up
Keywords: cardiac MRI; deep image prior; image reconstruction; real-time imaging; unsupervised learning

Multiparametric imaging with heterogeneous radiofrequency fields - Cloos, Martijn A.; Knoll, Florian; Zhao, Tiejun; Block, Kai T.; Bruno, Mary; Wiggins, Graham C.; Sodickson, Daniel K. (Nature Communications, 2016)
Magnetic resonance imaging (MRI) has become an unrivalled medical diagnostic technique able to map tissue anatomy and physiology non-invasively. MRI measurements are meticulously engineered to control experimental conditions across the sample. However, residual radiofrequency (RF) field inhomogeneities are often unavoidable, leading to artefacts that degrade the diagnostic and scientific value of the images. Here we show that, paradoxically, these artefacts can be eliminated by deliberately interweaving freely varying heterogeneous RF fields into a magnetic resonance fingerprinting data-acquisition process. Observations made based on simulations are experimentally confirmed at 7 Tesla (T), and the clinical implications of this new paradigm are illustrated with in vivo measurements near an orthopaedic implant at 3T. These results show that it is possible to perform quantitative multiparametric imaging with heterogeneous RF fields, and to liberate MRI from the traditional struggle for control over the RF field uniformity.
CRIS-Team Scopus Importer:2021-08-17

New-Generation Low-Field Magnetic Resonance Imaging of Hip Arthroplasty Implants Using Slice Encoding for Metal Artifact Correction: First in Vitro Experience at 0.55 T and Comparison with 1.5 T - Khodarahmi, Iman; Brinkmann, Inge M.; Lin, Dana J.; Bruno, Mary; Johnson, Patricia M.; Knoll, Florian; Keerthivasan, Mahesh B.; Chandarana, Hersh; Fritz, Jan (Investigative Radiology, 2022)
Objectives Despite significant progress, artifact-free visualization of the bone and soft tissues around hip arthroplasty implants remains an unmet clinical need. New-generation low-field magnetic resonance imaging (MRI) systems now include slice encoding for metal artifact correction (SEMAC), which may result in smaller metallic artifacts and better image quality than standard-of-care 1.5 T MRI. This study aims to assess the feasibility of SEMAC on a new-generation 0.55 T system, optimize the pulse protocol parameters, and compare the results with those of a standard-of-care 1.5 T MRI. Materials and Methods Titanium (Ti) and cobalt-chromium total hip arthroplasty implants embedded in a tissue-mimicking American Society for Testing and Materials gel phantom were evaluated using turbo spin echo, view angle tilting (VAT), and combined VAT and SEMAC (VAT + SEMAC) pulse sequences. To refine an MRI protocol at 0.55 T, the type of metal artifact reduction techniques and the effect of various pulse sequence parameters on metal artifacts were assessed through qualitative ranking of the images by 3 expert readers while taking measured spatial resolution, signal-to-noise ratios, and acquisition times into consideration. Signal-to-noise ratio efficiency and artifact size of the optimized 0.55 T protocols were compared with the 1.5 T standard and compressed-sensing SEMAC sequences. Results Overall, the VAT + SEMAC sequence with at least 6 SEMAC encoding steps for Ti and 9 for cobalt-chromium implants was ranked higher than other sequences for metal reduction (P < 0.05). Additional SEMAC encoding partitions did not result in further metal artifact reductions. Permitting minimal residual artifacts, low magnetic susceptibility Ti constructs may be sufficiently imaged with optimized turbo spin echo sequences obviating the need for SEMAC. In cross-platform comparison, 0.55 T acquisitions using the optimized protocols are associated with 45% to 64% smaller artifacts than 1.5 T VAT + SEMAC and VAT + compressed-sensing/SEMAC protocols at the expense of a 17% to 28% reduction in signal-to-noise ratio efficiency. B1-related artifacts are invariably smaller at 0.55 T than 1.5 T; however, artifacts related to B0 distortion, although frequently smaller, may appear as signal pileups at 0.55 T. Conclusions Our results suggest that new-generation low-field SEMAC MRI reduces metal artifacts around hip arthroplasty implants to better advantage than current 1.5 T MRI standard of care. While the appearance of B0-related artifacts changes, reduction in B1-related artifacts plays a major role in the overall benefit of 0.55 T.
CRIS-Team Scopus Importer:2023-03-08
Keywords: hip arthroplasty; low-field; magnetic resonance imaging; metal artifact; SEMAC

Overview of Magnetic Resonance Imaging Reconstruction Methods Using Various Constraints to Solve the Ill-posed Problems - Kim, Jinho; Nickel, Marcel Dominik; Knoll, Florian (Overview of Magnetic Resonance Imaging Reconstruction Methods Using Various Constraints to Solve the Ill-posed Problems, 2023)

Parallel imaging with nonlinear reconstruction using variational penalties - Knoll, Florian; Clason, Christian; Bredies, Kristian; Uecker, Martin; Stollberger, Rudolf (Magnetic Resonance in Medicine, 2012)
A new approach based on nonlinear inversion for autocalibrated parallel imaging with arbitrary sampling patterns is presented. By extending the iteratively regularized Gauss-Newton method with variational penalties, the improved reconstruction quality obtained from joint estimation of image and coil sensitivities is combined with the superior noise suppression of total variation and total generalized variation regularization. In addition, the proposed approach can lead to enhanced removal of sampling artifacts arising from pseudorandom and radial sampling patterns. This is demonstrated for phantom and in vivo measurements. Copyright © 2011 Wiley Periodicals, Inc.
CRIS-Team Scopus Importer:2021-08-17
Keywords: nonlinear inversion; parallel imaging; pseudorandom sampling; radial sampling; total generalized variation; total variation

PET reconstruction with non-smooth gradient-based priors - Schramm, G.; Holler, M.; Koesters, T.; Boada, F.; Knoll, Florian; Bredies, K.; Nuyts, J. (2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016, 2017)
Quantitative PET imaging is hindered by limited spatial resolution and high Poisson noise. A way to overcome those limitations, especially in brain PET/MR examinations, is the inclusion of anatomical prior knowledge in the image reconstruction process. Recently, the concept of Parallel Level Sets (PLS) [1]-[3] has been introduced as a promising anatomical prior for PET reconstruction. This prior relies on the exchange of gradient information. However, PLS is a non-smooth function which hampers the optimization process in iterative reconstruction. In this proceeding we show how to use the EM-TV algorithm by Sawatzki et al. [4] to solve the non-smooth PLS-regularized PET reconstruction problem efficiently which enables application to 3D clinical PET data.
CRIS-Team Scopus Importer:2021-08-17

Phase unwinding for dictionary compression with multiple channel transmission in magnetic resonance fingerprinting - Lattanzi, Riccardo; Zhang, Bei; Knoll, Florian; Asslander, Jakob; Cloos, Martijn A. (Magnetic Resonance Imaging, 2018)
Purpose: Magnetic Resonance Fingerprinting reconstructions can become computationally intractable with multiple transmit channels, if the B1 + phases are included in the dictionary. We describe a general method that allows to omit the transmit phases. We show that this enables straightforward implementation of dictionary compression to further reduce the problem dimensionality. Methods: We merged the raw data of each RF source into a single k-space dataset, extracted the transceiver phases from the corresponding reconstructed images and used them to unwind the phase in each time frame. All phase-unwound time frames were combined in a single set before performing SVD-based compression. We conducted synthetic, phantom and in-vivo experiments to demonstrate the feasibility of SVD-based compression in the case of two-channel transmission. Results: Unwinding the phases before SVD-based compression yielded artifact-free parameter maps. For fully sampled acquisitions, parameters were accurate with as few as 6 compressed time frames. SVD-based compression performed well in-vivo with highly under-sampled acquisitions using 16 compressed time frames, which reduced reconstruction time from 750 to 25 min. Conclusion: Our method reduces the dimensions of the dictionary atoms and enables to implement any fingerprint compression strategy in the case of multiple transmit channels.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Dictionary compression; MR fingerprinting; MRF; SVD; T; T

Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging - Hammernik, Kerstin; Kustner, Thomas; Yaman, Burhaneddin; Huang, Zhengnan; Rueckert, Daniel; Knoll, Florian; Akcakaya, Mehmet (IEEE Signal Processing Magazine, 2023)
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provides an overview of the recent developments in incorporating physics information into learning-based MRI reconstruction. We consider inverse problems with both linear and nonlinear forward models for computational MRI and review the classical approaches for solving these. We then focus on physics-driven deep learning approaches, covering physics-driven loss functions, plug-and-play (PnP) methods, generative models, and unrolled networks. We highlight domain-specific challenges, such as real- and complex-valued building blocks of neural networks, and translational applications in MRI with linear and nonlinear forward models. Finally, we discuss common issues and open challenges, and we draw connections to the importance of physics-driven learning when combined with other downstream tasks in the medical imaging pipeline.
CRIS-Team Scopus Importer:2023-02-10

Positive contrast of SPIO-labeled cells by off-resonant reconstruction of 3D radial half-echo bSSFP - Diwoky, Clemens; Liebmann, Daniel; Neumayer, Bernhard; Reinisch, Andreas; Knoll, Florian; Strunk, Dirk; Stollberger, Rudolf (NMR in Biomedicine, 2015)
This article describes a new acquisition and reconstruction concept for positive contrast imaging of cells labeled with superparamagnetic iron oxides (SPIOs). Overcoming the limitations of a negative contrast representation as gained with gradient echo and fully balanced steady state (bSSFP), the proposed method delivers a spatially localized contrast with high cellular sensitivity not accomplished by other positive contrast methods. Employing a 3D radial bSSFP pulse sequence with half-echo sampling, positive cellular contrast is gained by adding artificial global frequency offsets to each half-echo before image reconstruction. The new contrast regime is highlighted with numerical intravoxel simulations including the point-spread function for 3D half-echo acquisitions. Furthermore, the new method is validated on the basis of in vitro cell phantom measurements on a clinical MRI platform, where the measured contrast-to-noise ratio (CNR) of the new approach exceeds even the negative contrast of bSSFP. Finally, an in vivo proof of principle study based on a mouse model with a clear depiction of labeled cells within a subcutaneous cell islet containing a cell density as low as 7 cells/mm3 is presented. The resultant isotropic images show robustness to motion and a high CNR, in addition to an enhanced specificity due to the positive contrast of SPIO-labeled cells.
CRIS-Team Scopus Importer:2021-08-17
Keywords: 3D radial half-echo sampling; BSSFP; Off-resonant reconstruction; Positive contrast; Stem cell tracking

Preconditioned ADMM with nonlinear operator constraint - Benning, Martin; Knoll, Florian; Schönlieb, Carola Bibiane; Valkonen, Tuomo (IFIP Advances in Information and Communication Technology, 2016)
We are presenting a modification of the well-known Alternating Direction Method of Multipliers (ADMM) algorithm with additional preconditioning that aims at solving convex optimisation problems with nonlinear operator constraints. Connections to the recently developed Nonlinear Primal-Dual Hybrid Gradient Method (NL-PDHGM) are presented, and the algorithm is demonstrated to handle the nonlinear inverse problem of parallel Magnetic Resonance Imaging (MRI).
CRIS-Team Scopus Importer:2021-08-17
Keywords: ADMM; Iterative Bregman method; Nonlinear inverse problems; Operator splitting; Parallel MRI; Primal-dual; Proximal point method

Rapid mono and biexponential 3D-T1ρ mapping of knee cartilage using variational networks - Zibetti, Marcelo V. W.; Johnson, Patricia M.; Sharafi, Azadeh; Hammernik, Kerstin; Knoll, Florian; Regatte, Ravinder R. (Scientific Reports, 2020)
In this study we use undersampled MRI acquisition methods to obtain accelerated 3D mono and biexponential spin–lattice relaxation time in the rotating frame (T1ρ) mapping of knee cartilage, reducing the usual long scan time. We compare the accelerated T1ρ maps obtained by deep learning-based variational network (VN) and compressed sensing (CS). Both methods were compared with spatial (S) and spatio-temporal (ST) filters. Complex-valued fitting was used for T1ρ parameters estimation. We tested with seven in vivo and six synthetic datasets, with acceleration factors (AF) from 2 to 10. Median normalized absolute deviation (MNAD), analysis of variance (ANOVA), and coefficient of variation (CV) were used for analysis. The methods CS-ST, VN-S, and VN-ST performed well for accelerating monoexponential T1ρ mapping, with MNAD around 5% for AF = 2, which increases almost linearly with the AF to an MNAD of 13% for AF = 8, with all methods. For biexponential mapping, the VN-ST was the best method starting with MNAD of 7.4% for AF = 2 and reaching MNAD of 13.1% for AF = 8. The VN was able to produce 3D-T1ρ mapping of knee cartilage with lower error than CS. The best results were obtained by VN-ST, improving CS-ST method by nearly 7.5%.
CRIS-Team Scopus Importer:2021-08-17

Reconstruction of undersampled radial PatLoc imaging using total generalized variation - Knoll, Florian; Schultz, Gerrit; Bredies, Kristian; Gallichan, Daniel; Zaitsev, Maxim; Hennig, Juergen; Stollberger, Rudolf (Magnetic Resonance in Medicine, 2013)
In the case of radial imaging with nonlinear spatial encoding fields, a prominent star-shaped artifact has been observed if a spin distribution is encoded with an undersampled trajectory. This work presents a new iterative reconstruction method based on the total generalized variation, which reduces this artifact. For this approach, a sampling operator (as well as its adjoint) is needed that maps data from PatLoc k-space to the final image space. It is shown that this can be realized as a type-3 nonuniform fast Fourier transform, which is implemented by a combination of a type-1 and type-2 nonuniform fast Fourier transform. Using this operator, it is also possible to implement an iterative conjugate gradient SENSE based method for PatLoc reconstruction, which leads to a significant reduction of computation time in comparison to conventional PatLoc image reconstruction methods. Results from numerical simulations and in vivo PatLoc measurements with as few as 16 radial projections are presented, which demonstrate significant improvements in image quality with the total generalized variation-based approach. © 2012 Wiley Periodicals, Inc.
CRIS-Team Scopus Importer:2021-08-17
Keywords: constrained image reconstruction; nonlinear encoding fields; parallel imaging; PatLoc imaging; radial sampling; total generalized variation

Reproducibility and quality assurance in MRI - Stöcker, Tony; Keenan, Kathryn E.; Knoll, Florian; Priovoulos, Nikos; Uecker, Martin; Zaitsev, Maxim (Magnetic Resonance Materials in Physics Biology and Medicine, 2025)
CRIS-Team Scopus Importer:2025-07-04

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction - Muckley, Matthew J.; Riemenschneider, Bruno; Radmanesh, Alireza; Kim, Sunwoo; Jeong, Geunu; Ko, Jingyu; Jun, Yohan; Shin, Hyungseob; Hwang, Dosik; Mostapha, Mahmoud; Arberet, Simon; Nickel, Dominik; et al. (IEEE Transactions on Medical Imaging, 2021)
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NYU Langone Health), holding back the fully-sampled data from 894 of these scans for challenge evaluation purposes. In contrast to the 2019 challenge, we focused our radiologist evaluations on pathological assessment in brain images. We also debuted a new Transfer track that required participants to submit models evaluated on MRI scanners from outside the training set. We received 19 submissions from eight different groups. Results showed one team scoring best in both SSIM scores and qualitative radiologist evaluations. We also performed analysis on alternative metrics to mitigate the effects of background noise and collected feedback from the participants to inform future challenges. Lastly, we identify common failure modes across the submissions, highlighting areas of need for future research in the MRI reconstruction community.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Challenge; Compressed Sensing; Fast Imaging; Machine Learning; MR Image Reconstruction; Optimization; Parallel Imaging; Public Data Set

Second order total generalized variation (TGV) for MRI - Knoll, Florian; Bredies, Kristian; Pock, Thomas; Stollberger, Rudolf (Magnetic Resonance in Medicine, 2011)
Total variation was recently introduced in many different magnetic resonance imaging applications. The assumption of total variation is that images consist of areas, which are piecewise constant. However, in many practical magnetic resonance imaging situations, this assumption is not valid due to the inhomogeneities of the exciting B1 field and the receive coils. This work introduces the new concept of total generalized variation for magnetic resonance imaging, a new mathematical framework, which is a generalization of the total variation theory and which eliminates these restrictions. Two important applications are considered in this article, image denoising and image reconstruction from undersampled radial data sets with multiple coils. Apart from simulations, experimental results from in vivo measurements are presented where total generalized variation yielded improved image quality over conventional total variation in all cases. © 2010 Wiley-Liss, Inc.
CRIS-Team Scopus Importer:2021-08-17
Keywords: accelerated imaging; constrained reconstruction; denoising; radial sampling; total generalized variation

Simultaneous PET-MRI reconstruction with vectorial second order total generalized variation - Knoll, Florian; Holler, Martin; Koesters, Thomas; Bredies, Kristian; Sodickson, Daniel K. (2015 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2015, 2016)
State of the art PET-MR systems are capable of performing both PET and MR measurements simultaneously. However, the resulting data sets are usually processed in two separate reconstruction pipelines. The goal of this work is to complement simultaneous data acquisition with a new multi-modality reconstruction framework based on second order total generalized variation that simultaneously reconstructs both PET and MR images. Information of the underlying anatomy is shared during the image reconstruction process with a dedicated multi-channel regularization functional. Results of numerical simulations and in-vivo data are presented that demonstrate improved PET resolution and reduced noise in MR in comparison to conventional reconstructions.
CRIS-Team Scopus Importer:2021-08-17
Keywords: iterative image reconstruction; multi-modality imaging; PET-MR; total generalized variation; variational regularization

SparseCT: Interrupted-beam acquisition and sparse reconstruction for radiation dose reduction - Koesters, Thomas; Knoll, Florian; Sodickson, Aaron; Sodickson, Daniel K.; Otazo, Ricardo (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2017)
State-of-the-art low-dose CT methods reduce the x-ray tube current and use iterative reconstruction methods to denoise the resulting images. However, due to compromises between denoising and image quality, only moderate dose reductions up to 30-40% are accepted in clinical practice. An alternative approach is to reduce the number of x-ray projections and use compressed sensing to reconstruct the full-tube-current undersampled data. This idea was recognized in the early days of compressed sensing and proposals for CT dose reduction appeared soon afterwards. However, no practical means of undersampling has yet been demonstrated in the challenging environment of a rapidly rotating CT gantry. In this work, we propose a moving multislit collimator as a practical incoherent undersampling scheme for compressed sensing CT and evaluate its application for radiation dose reduction. The proposed collimator is composed of narrow slits and moves linearly along the slice dimension (z), to interrupt the incident beam in different slices for each x-ray tube angle (θ). The reduced projection dataset is then reconstructed using a sparse approach, where 3D image gradients are employed to enforce sparsity. The effects of the collimator slits on the beam profile were measured and represented as a continuous slice profile. SparseCT was tested using retrospective undersampling and compared against commercial current-reduction techniques on phantoms and in vivo studies. Initial results suggest that SparseCT may enable higher performance than current-reduction, particularly for high dose reduction factors.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Collimator; Compressed sensing; Iterative reconstruction; Low-dose CT; Sparsity; Undersampling

Spatiotemporal variational neural network for reconstruction of highly accelerated cardiac cine MRI - Vornehm, Marc; Wetzl, Jens; Giese, Daniel; Ahmad, R; Knoll, Florian (European Heart Journal - Cardiovascular Imaging, 2022)

Stable Deep MRI Reconstruction using Generative Priors - Zach, Martin; Knoll, Florian; Pock, Thomas (IEEE Transactions on Medical Imaging, 2023)
Data-driven approaches recently achieved remarkable success in magnetic resonance imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to a lack of generalizability and interpretability. In this paper, we address these challenges in a unified framework based on generative image priors. We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only. After training, the regularizer encodes higher-level domain statistics which we demonstrate by synthesizing images without data. Embedding the trained model in a classical variational approach yields high-quality reconstructions irrespective of the sub-sampling pattern. In addition, the model shows stable behavior when confronted with out-of-distribution data in the form of contrast variation. Furthermore, a probabilistic interpretation provides a distribution of reconstructions and hence allows uncertainty quantification. To reconstruct parallel MRI, we propose a fast algorithm to jointly estimate the image and the sensitivity maps. The results demonstrate competitive performance, on par with state-of-the-art end-to-end deep learning methods, while preserving the flexibility with respect to sub-sampling patterns and allowing for uncertainty quantification.
CRIS-Team Scopus Importer:2023-10-27
Keywords: Data models; Generative priors; Image reconstruction; Magnetic resonance imaging; MRI; Probabilistic logic; Sensitivity; Stable inverse problems; Training; Uncertainty

TGV for diffusion tensors: A comparison of fidelity functions - Valkonen, Tuomo; Bredies, Kristian; Knoll, Florian (Journal of Inverse and Ill-posed Problems, 2013)
We study the total generalised variation regularisation of symmetric tensor fields from medical applications, namely diffusion tensor regularisation. We study the effect of the pointwise positivity constraint on the tensor field, as well as the difference between direct denoising of the tensor field first solved from the Stejskal-Tanner equation, as was done in our earlier work, and of incorporating this equation into the fidelity function. Our results indicate that the latter novel approach provides improved computational results. © de Gruyter 2013.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Diffusion tensor imaging; Medical imaging; Regularisation; Total deformation; Total generalised variation

The agile library for biomedical image reconstruction using GPU acceleration - Freiberger, Manuel; Knoll, Florian; Bredies, Kristian; Scharfetter, Hermann; Stollberger, Rudolf (Computing in Science & Engineering, 2013)
A cheap way to speed up image-reconstruction software is to use modern graphics hardware that can execute algorithms in a massively parallel manner. Here, the authors discuss Agile, an open source library designed for image reconstruction in biomedical sciences. Its modular, object-oriented, and templated design eases the integration of the library into user code. © 1999-2011 IEEE.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Biomedical science; Finite-element methods; GPUs; Medical resonance imaging; MRI; Parallel algorithms; Scientific computing

The rapid imaging renaissance: Sparser samples, denser dimensions, and glimmerings of a grand unified tomography - Sodickson, Daniel K.; Feng, Li; Knoll, Florian; Cloos, Martijn; Ben-Eliezer, Noam; Axel, Leon; Chandarana, Hersh; Block, Tobias; Otazo, Ricardo (Progress in Biomedical Optics and Imaging - Proceedings of SPIE, 2015)
The task of imaging is to gather spatiotemporal information which can be organized into a coherent map. Tomographic imaging in particular involves the use of multiple projections, or other interactions of a probe (light, sound, etc.) with a body, in order to determine cross-sectional information. Though the probes and the corresponding imaging modalities may vary, and though the methodology of particular imaging approaches is in constant ferment, the conceptual underpinnings of tomographic imaging have in many ways remained fixed for many decades. Recent advances in applied mathematics, however, have begun to roil this intellectual landscape. The advent of compressed sensing, anticipated in various algorithms dating back many years but unleashed in full theoretical force in the last decade, has changed the way imagers have begun to think about data acquisition and image reconstruction. The power of incoherent sampling and sparsity-enforcing reconstruction has been demonstrated in various contexts and, when combined with other modern fast imaging techniques, has enabled unprecedented increases in imaging efficiency. Perhaps more importantly, however, such approaches have spurred a shift in perspective, prompting us to focus less on nominal data sufficiency than on information content. Beginning with examples from MRI, then proceeding through selected other modalities such as CT and PET, as well as multimodality combinations, this paper explores the potential of newly evolving acquisition and reconstruction paradigms to change the way we do imaging in the lab and in the clinic.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Compressed sensing; CT; MRI; Multimodality imaging; Parallel imaging; PET; Rapid imaging; Sparsity

Total generalized variation in diffusion tensor imaging - Valkonen, Tuomo; Bredies, Kristian; Knoll, Florian (Siam Journal on Imaging Sciences, 2013)
We study the extension of total variation (TV), total deformation (TD), and (second-order) total generalized variation (TGV2) to symmetric tensor fields. We show that for a suitable choice of finite-dimensional norm, these variational seminorms are rotation-invariant in a sense natural and well suited for application to diffusion tensor imaging (DTI). Combined with a positive definiteness constraint, we employ these novel seminorms as regularizers in Rudin-Osher-Fatemi (ROF) type denoising of medical in vivo brain images. For the numerical realization, we employ the Chambolle-Pock algorithm, for which we develop a novel duality-based stopping criterion which guarantees error bounds with respect to the functional values. Our findings indicate that TD and TGV2, both of which employ the symmetrized differential, provide improved results compared to other evaluated approaches. © 2013 Society for Industrial and Applied Mathematics.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Diffusion tensor imaging; Medical imaging; Regularization; Total deformation; Total generalized variation; Total variation

Towards Low-Latency Deep Learning-Based Reconstruction of Real-Time Cine MRI - Vornehm, Marc; Wetzl, Jens; Giese, Daniel; Knoll, Florian (25. Jahrestagung der Deutschen Sektion der ISMRM, 2023)

Training a neural network for Gibbs and noise removal in diffusion MRI - Muckley, Matthew J.; Ades-Aron, Benjamin; Papaioannou, Antonios; Lemberskiy, Gregory; Solomon, Eddy; Lui, Yvonne W.; Sodickson, Daniel K.; Fieremans, Els; Novikov, Dmitry S.; Knoll, Florian (Magnetic Resonance in Medicine, 2021)
Purpose: To develop and evaluate a neural network–based method for Gibbs artifact and noise removal. Methods: A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images. Results: Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions. Conclusions: The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.
CRIS-Team Scopus Importer:2021-08-17
Keywords: denoising; diffusion MRI; Gibbs ringing; neural network

Training deep learning reconstruction models for radial real-time cardiac cine MRI using synthetic golden-angle data - Ott, Yannik; Vornehm, Marc; Wetzl, Jens; Giese, Daniel; Knoll, Florian (26th Annual Meeting of the German-speaking section of ISMRM, 2024)

Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study - Recht, Michael P.; Zbontar, Jure; Sodickson, Daniel K.; Knoll, Florian; Yakubova, Nafissa; Sriram, Anuroop; Murrell, Tullie; Defazio, Aaron; Rabbat, Michael; Rybak, Leon; Kline, Mitchell; Ciavarra, Gina; et al. (American Journal of Roentgenology, 2020)
OBJECTIVE. Deep learning (DL) image reconstruction has the potential to disrupt the current state of MRI by significantly decreasing the time required for MRI examinations. Our goal was to use DL to accelerate MRI to allow a 5-minute comprehensive examination of the knee without compromising image quality or diagnostic accuracy. MATERIALS AND METHODS. A DL model for image reconstruction using a variational network was optimized. The model was trained using dedicated multisequence training, in which a single reconstruction model was trained with data from multiple sequences with different contrast and orientations. After training, data from 108 patients were retrospectively undersampled in a manner that would correspond with a net 3.49-fold acceleration of fully sampled data acquisition and a 1.88-fold acceleration compared with our standard twofold accelerated parallel acquisition. An interchangeability study was performed, in which the ability of six readers to detect internal derangement of the knee was compared for clinical and DL-accelerated images. RESULTS. We found a high degree of interchangeability between standard and DL-accelerated images. In particular, results showed that interchanging the sequences would produce discordant clinical opinions no more than 4% of the time for any feature evaluated. Moreover, the accelerated sequence was judged by all six readers to have better quality than the clinical sequence. CONCLUSION. An optimized DL model allowed acceleration of knee images that performed interchangeably with standard images for detection of internal derangement of the knee. Importantly, readers preferred the quality of accelerated images to that of standard clinical images.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Acceleration; Deep learning; Internal derangement; Knee; MRI

Variational Deep Learning for Low-Dose Computed Tomography - Kobler, Erich; Muckley, Matthew; Chen, Baiyu; Knoll, Florian; Hammernik, Kerstin; Pock, Thomas; Sodickson, Daniel; Otazo, Ricardo (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2018)
In this work, we propose a learning-based variational network (VN) approach for reconstruction of low-dose 3D computed tomography data. We focus on two methods to decrease the radiation dose: (1) x-ray tube current reduction, which reduces the signal-to-noise ratio, and (2) x-ray beam interruption, which undersamples data and results in images with aliasing artifacts. While the learned VN denoises the current-reduced images in the first case, it reconstructs the undersampled data in the second case. Different VNs for denoising and reconstruction are trained on a single clinical 3D abdominal data set. The VNs are compared against state-of-the-art model-based denoising and sparse reconstruction techniques on a different clinical abdominal 3D data set with 4-fold dose reduction. Our results suggest that the proposed VNs enable higher radiation dose reductions and/or increase the image quality for a given dose.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Compressed sensing; Computed tomography; Machine learning; Medical imaging; Variational networks

Variational Network Meets Conjugate Gradient: Inline Reconstruction and Strain Analysis of Accelerated Cardiac Cine MRI - Vornehm, Marc; Wetzl, Jens; Fürnrohr, Florian; Giese, Daniel; Ahmad, Rizwan; Knoll, Florian (Proceedings of the International Society for Magnetic Resonance in Medicine, 2024)

Virtual Mouse Brain Histology from Multi-contrast MRI via Deep Learning - Liang, Zifei; Lee, Choong H.; Arefin, Tanzil M.; Dong, Zijun; Walczak, Piotr; Shi, Song-Hai; Knoll, Florian; Ge, Yulin; Ying, Leslie; Zhang, Jiangyang (eLife, 2022)
1H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from MRI findings, however, remains challenging due to absence of direct links between MRI signals and cellular structures. Here, we show that deep convolutional neural networks, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can estimate histological staining intensity directly from MRI signals at each voxel. The results provide three-dimensional maps of axons and myelin with tissue contrasts that closely mimics target histology and enhanced sensitivity and specificity compared to conventional MRI markers. Furthermore, the relative contribution of each MRI contrast within the networks can be used to optimize multi-contrast MRI acquisition. We anticipate our method to be a starting point for translation of MRI results into easy-to-understand virtual histology for neurobiologists and provide resources for validating novel MRI techniques.
CRIS-Team Scopus Importer:2023-03-08