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, 2024)
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

Comparison of intelligent 4D CT sequence scanning and conventional spiral 4D CT: A first comprehensive phantom study - Werner, René; Szkitsak, Juliane; Sentker, Thilo; Madesta, Frederic; Schwarz, Annette; Fernolendt, Susanne; Vornehm, Marc; Gauer, Tobias; Bert, Christoph; Hofmann, Christian (Physics in Medicine and Biology, 2021)
4D CT imaging is a cornerstone of 4D radiotherapy treatment. Clinical 4D CT data are, however, often affected by severe artifacts. The artifacts are mainly caused by breathing irregularity and retrospective correlation of breathing phase information and acquired projection data, which leads to insufficient projection data coverage to allow for proper reconstruction of 4D CT phase images. The recently introduced 4D CT approach i4DCT (intelligent 4D CT sequence scanning) aims to overcome this problem by breathing signal-driven tube control. The present motion phantom study describes the first in-depth evaluation of i4DCT in a real-world scenario. Twenty-eight 4D CT breathing curves of lung and liver tumor patients with pronounced breathing irregularity were selected to program the motion phantom. For every motion pattern, 4D CT imaging was performed with i4DCT and a conventional spiral 4D CT mode. For qualitative evaluation, the reconstructed 4D CT images were presented to clinical experts, who scored image quality. Further quantitative evaluation was based on established image intensity-based artifact metrics to measure (dis)similarity of neighboring image slices. In addition, beam-on and scan times of the scan modes were analyzed. The expert rating revealed a significantly higher image quality for the i4DCT data. The quantitative evaluation further supported the qualitative: While 20% of the slices of the conventional spiral 4D CT images were found to be artifact-affected, the corresponding fraction was only 4% for i4DCT. The beam-on time (surrogate of imaging dose) did not significantly differ between i4DCT and spiral 4D CT. Overall i4DCT scan times (time between first beam-on and last beam-on event, including scan breaks to compensate for breathing irregularity) were, on average, 53% longer compared to spiral CT. Thus, the results underline that i4DCT significantly improves 4D CT image quality compared to standard spiral CT scanning in the case of breathing irregularity during scanning.
CRIS-Team Scopus Importer:2021-02-12
Keywords: 4D CT; I4DCT; Motion artifacts; Radiotherapy

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

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)

Dose reduction for respiratory signal-guided step-and-shoot 4DCT by online dose modulation - Werner, R; Schwarz, A; Wimmert, L; Buttgen, L; Vornehm, Marc; Gauer, T; Hofmann, C (Radiotherapy and Oncology, 2023)

Dose reduction in sequence scanning 4D CT imaging through respiratory signal-guided tube current modulation: A feasibility study - Schwarz, Annette; Werner, René; Wimmert, Lukas; Vornehm, Marc; Gauer, Tobias; Hofmann, Christian (Medical Physics, 2023)
Background: Respiratory signal-guided 4D CT sequence scanning such as the recently introduced Intelligent 4D CT (i4DCT) approach reduces image artifacts compared to conventional 4D CT, especially for irregular breathing. i4DCT selects beam-on periods during scanning such that data sufficiency conditions are fulfilled for each couch position. However, covering entire breathing cycles during beam-on periods leads to redundant projection data and unnecessary dose to the patient during long exhalation phases. Purpose: We propose and evaluate the feasibility of respiratory signal-guided dose modulation (i.e., temporary reduction of the CT tube current) to reduce the i4DCT imaging dose while maintaining high projection data coverage for image reconstruction. Methods: The study is designed as an in-silico feasibility study. Dose down- and up-regulation criteria were defined based on the patients’ breathing signals and their representative breathing cycle learned before and during scanning. The evaluation (including an analysis of the impact of the dose modulation criteria parameters) was based on 510 clinical 4D CT breathing curves. Dose reduction was determined as the fraction of the downregulated dose delivery time to the overall beam-on time. Furthermore, under the assumption of a 10-phase 4D CT and amplitude-based reconstruction, beam-on periods were considered negatively affected by dose modulation if the downregulation period covered an entire phase-specific amplitude range for a specific breathing phase (i.e., no appropriate reconstruction of the phase image possible for this specific beam-on period). Corresponding phase-specific amplitude bins are subsequently denoted as compromised bins. Results: Dose modulation resulted in a median dose reduction of 10.4% (lower quartile: 7.4%, upper quartile: 13.8%, maximum: 28.6%; all values corresponding to a default parameterization of the dose modulation criteria). Compromised bins were observed in 1.0% of the beam-on periods (72 / 7370 periods) and affected 10.6% of the curves (54/510 curves). The extent of possible dose modulation depends strongly on the individual breathing patterns and is weakly correlated with the median breathing cycle length (Spearman correlation coefficient 0.22, p < 0.001). Moreover, the fraction of beam-on periods with compromised bins is weakly anti-correlated with the patient's median breathing cycle length (Spearman correlation coefficient -0.24; p < 0.001). Among the curves with the 17% longest average breathing cycles, no negatively affected beam-on periods were observed. Conclusion: Respiratory signal-guided dose modulation for i4DCT imaging is feasible and promises to significantly reduce the imaging dose with little impact on projection data coverage. However, the impact on image quality remains to be investigated in a follow-up study.
Created from Fastlane, Scopus look-up
Keywords: 4D CT; dose reduction

Fully Automatic Scar Segmentation on LGE Images with Reduced Contrast Agent Dose - Vornehm, Marc; Spiske, Ute; Fenski, Maximilian; Wetzl, Jens; Preuhs, Elisabeth; Maier, Andreas; Schulz-Menger, Jeanette; Giese, Daniel (Proceedings of the SCMR 26th Annual Scientific Sessions, 2023)

Improving the Sensitivity to Myocardial Scar in Automatic Segmentations of Left Ventricular Myocardium on LGE Images - Vornehm, Marc; Fenski, Maximilian; Preuhs, Elisabeth; Maier, Andreas; Schulz-Menger, Jeanette; Wetzl, Jens; Giese, Daniel (Proceedings of the International Society for Magnetic Resonance in Medicine, 2022)

Intelligent 4D CT sequence scanning (i4DCT): First scanner prototype implementation and phantom measurements of automated breathing signal-guided 4D CT - Werner, Rene; Sentker, Thilo; Madesta, Frederic; Schwarz, Annette; Vornehm, Marc; Gauer, Tobias; Hofmann, Christian (Medical Physics, 2020)
Purpose Four-dimensional (4D) computed tomography (CT) imaging is an essential part of current 4D radiotherapy treatment planning workflows, but clinical 4D CT images are often affected by artifacts. The artifacts are mainly caused by breathing irregularity during data acquisition, which leads to projection data coverage issues for currently available commercial 4D CT protocols. It was proposed to improve projection data coverage by online respiratory signal analysis and signal-guided CT tube control, but related work was always theoretical and presented as pure in silico studies. The present work demonstrates a first CT prototype implementation along with respective phantom measurements for the recently introduced intelligent 4D CT (i4DCT) sequence scanning concept (). Methods Intelligent 4D CT was implemented on the Siemens SOMATOM go platform. Four-dimensional CT measurements were performed using the CIRS motion phantom. Motion curves were programmed to systematically vary from regular to very irregular, covering typical irregular patterns that are known to result in image artifacts using standard 4D CT imaging protocols. Corresponding measurements were performed using i4DCT and routine spiral 4D CT with similar imaging parameters (e.g., mAs setting and gantry rotation time, retrospective ten-phase reconstruction) to allow for a direct comparison of the image data. Results Following technological implementation of i4DCT on the clinical CT scanner platform, 4D CT motion artifacts were significantly reduced for all investigated levels of breathing irregularity when compared to routine spiral 4D CT scanning. Conclusions The present study confirms feasibility of fully automated respiratory signal-guided 4D CT scanning by means of a first implementation of i4DCT on a CT scanner. The measurements thereby support the conclusions of respective in silico studies and demonstrate that respiratory signal-guided 4D CT (here: i4DCT) is ready for integration into clinical CT scanners.
CRIS-Team WoS Importer:2020-09-11

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)

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)

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, 2024)
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

Myocardial scar segmentation on cardiac LGE images with reduced contrast agent dose using deep learning - Vornehm, Marc; Fenski, Maximilian; Preuhs, Elisabeth; Maier, Andreas; Schulz-Menger, Jeanette; Wetzl, Jens; Giese, Daniel (Abstractband der 24. Jahrestagung der Deutschen Sektion der ISMRM, 2022)

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)

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 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)

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)