7 tricks for 7 T CEST: Improving the reproducibility of multipool evaluation provides insights into the effects of age and the early stages of Parkinson's disease - Mennecke, Angelika; Khakzar, Katrin M.; German, Alexander; Herz, Kai; Fabian, Moritz; Liebert, Andrzej; Blümcke, Ingmar; Kasper, Burkhard; Nagel, Armin Michael; Laun, Frederik Bernd; Schmidt, Manuel; Winkler, Jürgen; et al. (NMR in Biomedicine, 2022)
The objective of the current study was to optimize the postprocessing pipeline of 7 T chemical exchange saturation transfer (CEST) imaging for reproducibility and to prove this optimization for the detection of age differences and differences between patients with Parkinson's disease versus normal subjects. The following 7 T CEST MRI experiments were analyzed: repeated measurements of a healthy subject, subjects of two age cohorts (14 older, seven younger subjects), and measurements of 12 patients with Parkinson's disease. A slab-selective, (Formula presented.) -homogeneous parallel transmit protocol was used. The postprocessing, consisting of motion correction, smoothing, (Formula presented.) -correction, normalization, denoising, (Formula presented.) -correction and Lorentzian fitting, was optimized regarding the intrasubject and intersubject coefficient of variation (CoV) of the amplitudes of the amide pool and the aliphatic relayed nuclear Overhauser effect (rNOE) pool within the brain. Seven “tricks” for postprocessing accomplished an improvement of the mean voxel CoV of the amide pool and the aliphatic rNOE pool amplitudes of less than 5% and 3%, respectively. These postprocessing steps are: motion correction with interpolation of the motion of low-signal offsets (1) using the amide pool frequency offset image as reference (2), normalization of the Z-spectrum using the outermost saturated measurements (3), (Formula presented.) correction of the Z-spectrum with moderate spline smoothing (4), denoising using principal component analysis preserving the 11 highest intensity components (5), (Formula presented.) correction using a linear fit (6) and Lorentzian fitting using the five-pool fit model (7). With the optimized postprocessing pipeline, a significant age effect in the amide pool can be detected. Additionally, for the first time, an aliphatic rNOE contrast between subjects with Parkinson's disease and age-matched healthy controls in the substantia nigra is detected. We propose an optimized postprocessing pipeline for CEST multipool evaluation. It is shown that by the use of these seven “tricks”, the reproducibility and, thus, the statistical power of a CEST measurement, can be greatly improved and subtle changes can be detected.
CRIS-Team Scopus Importer:2022-03-25
Keywords: 7 T; age; amide; CEST; Lorentzian fitting; Parkinson; rNOE; standardization

Clinically compatible subject-specific dynamic parallel transmit pulse design for homogeneous fat saturation and water-excitation at 141657T: Proof-of-concept for 14165CEST MRI of the brain - Levy, Simon; Herrler, Jürgen; Liebert, Andrzej; Tkotz, Katharina; Fabian, Moritz; Eisen, Christian; Grodzki, David; Uder, Michael; Dörfler, Arnd; Zaiß, Moritz; Nagel, Armin Michael (Magnetic Resonance in Medicine, 2022)
Purpose To evaluate the benefits and challenges of dynamic parallel transmit (pTx) pulses for fat saturation (FS) and water-excitation (WE), in the context of CEST MRI. Methods "Universal" k(T)-points (for FS) and spiral non-selective (for WE) trajectories were optimized offline for flip angle (FA) homogeneity. Routines to optimize the pulse shape online, based on the subject's fields maps, were implemented (target FA of 110 degrees/0 degrees for FS, 0 degrees/5 degrees for WE at fat/water frequencies). The pulses were inserted in a CEST sequence with a pTx readout. The different fat suppression schemes and their effects on CEST contrasts were compared in 12 volunteers at 7T. Results With a 25%-shorter pulse duration, pTx FS largely improved the FA homogeneity (root-mean-square-error (RMSE) = 12.3 degrees vs. 53.4 degrees with circularly-polarized mode, at the fat frequency). However, the spectral selectivity was degraded mainly in the cerebellum and close to the sinuses (RMSE = 5.8 degrees vs. 0.2 degrees at the water frequency). Similarly, pTx WE showed a trade-off between FA homogeneity and spectral selectivity compared to pTx non-selective pulses (RMSE = 0.9 degrees and 1.1 degrees at the fat and water frequencies, vs. 4.6 degrees and 0.5 degrees). In the brain, CEST metrics were reduced by up to 31.9% at -3.3 ppm with pTx FS, suggesting a mitigated lipid-induced bias. Conclusion This clinically compatible implementation of dynamic pTx pulses improved the fat suppression homogeneity at 7T taking into account the subject-specific B-0 heterogeneities online. This study highlights the lipid-induced biases on the CEST z-spectrum. The results are promising for body applications where B-0 heterogeneities and fat are more substantial.
CRIS-Team WoS Importer:2022-09-30

Comprehensive 7 T CEST: A clinical MRI protocol covering multiple exchange rate regimes - Fabian, Moritz; Rajput, Junaid Rasool; Schüre, Jan-Rüdiger; Weinmüller, Simon; Mennecke, Angelika; Möhle, Tim Alexius; Rampp, Stefan; Schmidt, Manuel; Dörfler, Arnd; Zaiß, Moritz (NMR in Biomedicine, 2024)
SECTIONSPDFPDFTOOLS SHARE Abstract Chemical exchange saturation transfer (CEST) is a magnetic resonance (MR) imaging method providing molecular image contrasts based on indirect detection of low concentrated solutes. Previous CEST studies focused predominantly on the imaging of single CEST exchange regimes (e.g., slow, intermediate or fast exchanging groups). In this work, we aim to establish a so-called comprehensive CEST protocol for 7 T, covering the different exchange regimes by three saturation B1 amplitude regimes: low, intermediate and high. We used the results of previous publications and our own simulations in pulseq-CEST to produce a 7 T CEST protocol that has sensitivity to these three B1 regimes. With postprocessing optimization (simultaneous mapping of water shift and B1, B0-fitting, multiple interleaved mode saturation B1 correction, neural network employment (deepCEST) and analytical input feature reduction), we are able to shorten our initially 40 min protocol to 15 min and generate six CEST contrast maps simultaneously. With this protocol, we measured four healthy subjects and one patient with a brain tumor. We established a comprehensive CEST protocol for clinical 7 T MRI, covering three different B1 amplitude regimes. We were able to reduce the acquisition time significantly by more than 50%, while still maintaining decent image quality and contrast in healthy subjects and one patient with a tumor. Our protocol paves the way to perform comprehensive CEST studies in clinical scan times for hypothesis generation regarding molecular properties of certain pathologies, for example, ischemic stroke or high-grade brain tumour

DeepCEST 7 T: Fast and homogeneous mapping of 7 T CEST MRI parameters and their uncertainty quantification - Hunger, Leonie; Rajput, Junaid Rasool; Klein, Kiril; Mennecke, Angelika; Fabian, Moritz; Schmidt, Manuel; Glang, Felix; Herz, Kai; Liebig, Patrick; Nagel, Armin Michael; Scheffler, Klaus; Dörfler, Arnd; et al. (Magnetic Resonance in Medicine, 2022)
Purpose In this work, we investigated the ability of neural networks to rapidly and robustly predict Lorentzian parameters of multi-pool CEST MRI spectra at 7 T with corresponding uncertainty maps to make them quickly and easily available for routine clinical use. Methods We developed a deepCEST 7 T approach that generates CEST contrasts from just 1 scan with robustness against B-1 inhomogeneities. The input data for a neural feed-forward network consisted of 7 T in vivo uncorrected Z-spectra of a single B-1 level, and a B-1 map. The 7 T raw data were acquired using a 3D snapshot gradient echo multiple interleaved mode saturation CEST sequence. These inputs were mapped voxel-wise to target data consisting of Lorentzian amplitudes generated conventionally by 5-pool Lorentzian fitting of normalized, denoised, B-0- and B-1-corrected Z-spectra. The deepCEST network was trained with Gaussian negative log-likelihood loss, providing an uncertainty quantification in addition to the Lorentzian amplitudes. Results The deepCEST 7 T network provides fast and accurate prediction of all Lorentzian parameters also when only a single B-1 level is used. The prediction was highly accurate with respect to the Lorentzian fit amplitudes, and both healthy tissues and hyperintensities in tumor areas are predicted with a low uncertainty. In corrupted cases, high uncertainty indicated wrong predictions reliably. Conclusion The proposed deepCEST 7 T approach reduces scan time by 50% to now 6:42 min, but still delivers both B-0- and B-1-corrected homogeneous CEST contrasts along with an uncertainty map, which can increase diagnostic confidence. Multiple accurate 7 T CEST contrasts are delivered within seconds.
CRIS-Team WoS Importer:2022-12-02

Improved quantification in CEST-MRI by joint spatial total generalized variation - Huemer, Markus; Stilianu, Clemens; Maier, Oliver; Fabian, Moritz; Schmidt, Manuel; Dörfler, Arnd; Bredies, Kristian; Zaiß, Moritz; Stollberger, Rudolf (Magnetic Resonance in Medicine, 2024)
Purpose: In this work, the use of joint Total Generalized Variation (TGV) regularization to improve Multipool-Lorentzian fitting of chemical exchange saturation transfer (CEST) Spectra in terms of stability and parameter signal-to-noise ratio (SNR) was investigated. Theory and Methods: The joint TGV term was integrated into the nonlinear parameter fitting problem. To increase convergence and weight the gradients, preconditioning using a voxel-wise singular value decomposition was applied to the problem, which was then solved using the iteratively regularized Gauss–Newton method combined with a Primal-Dual splitting algorithm. The TGV method was evaluated on simulated numerical phantoms, 3T phantom data and 7T in vivo data with respect to systematic errors and robustness. Three reference methods were also implemented: The standard nonlinear fitting, a method using a nonlocal-means filter for denoising and the pyramid scheme, which uses downsampled images to acquire accurate start values. Results: The proposed regularized fitting method showed significantly improved robustness (compared to the reference methods). In testing, over a range of SNR values the TGV fit outperformed the other methods and showed accurate results even for large amounts of added noise. Parameter values found were closer or comparable to the ground truth. For in vivo datasets, the added regularization increased the parameter map SNR and prevented instabilities. Conclusion: The proposed fitting method using TGV regularization leads to improved results over a range of different data-sets and noise levels. Furthermore, it can be applied to all Z-spectrum data, with different amounts of pools, where the improved SNR and stability can increase diagnostic confidence.
CRIS-Team Scopus Importer:2024-07-26
Keywords: CEST; Lorentzian-fitting; rNOE; ssMT; TGV

Physics-Informed Conditional Autoencoder Approach for Robust Metabolic CEST MRI at 7T - Rajput, Junaid R.; Möhle, Tim A.; Fabian, Moritz; Mennecke, Angelika; Sembill, Jochen; Kuramatsu, Joji; Schmidt, Manuel; Dörfler, Arnd; Maier, Andreas; Zaiß, Moritz (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2023)
Chemical exchange saturation transfer (CEST) is an MRI method that provides insights on the metabolic level. Several metabolite effects appear in the CEST spectrum. These effects are isolated by Lorentzian curve fitting. The separation of CEST effects suffers from the inhomogeneity of the saturation field B1. This leads to inhomogeneities in the associated metabolic maps. Current B1 correction methods require at least two sets of CEST-spectra. This at least doubles the acquisition time. In this study, we investigated the use of an unsupervised physics-informed conditional autoencoder (PICAE) to efficiently correct B1 inhomogeneity and isolate metabolic maps while using a single CEST scan. The proposed approach integrates conventional Lorentzian model into the conditional autoencoder and performs voxel-wise B1 correction and Lorentzian line fitting. The method provides clear interpretation of each step and is inherently generative. Thus, CEST-spectra and fitted metabolic maps can be created at arbitrary B1 levels. This is important because the B1 dispersion contains information about the exchange rates and concentration of metabolite protons, paving the way for their quantification. The isolated maps for tumor data showed a robust B1 correction and more than 25% increase in structural similarity index (SSIM) with gadolinium reference image compared to the standard interpolation-based method and subsequent Lorentzian curve fitting. This efficient correction method directly results in at least 50% reduction in scan time.
CRIS-Team Scopus Importer:2023-11-03
Keywords: Acquisition time; Bound loss; CEST; Interpretability; Lorentzian fit; Physics-informed autoencoder

Snapshot CEST++: Advancing rapid whole-brain APTw-CEST MRI at 3 T - Sedykh, Maria; Liebig, Patrick; Herz, Kai; Fabian, Moritz; Mennecke, Angelika; Weinmüller, Simon; Schmidt, Manuel; Dörfler, Arnd; Zaiß, Moritz (NMR in Biomedicine, 2023)
APTw CEST MRI suffers from long preparation times and consequently long acquisition times ( 5 min). Recently, a consensus on the preparation module for clinical APTw CEST at 3 T was found in the community, and we present a fast whole-brain APTw CEST MRI sequence following this consensus preparation of pulsed RF irradiation of 2 s duration at 90% RF duty-cycle and a B1,rms of 2 μT. After optimization of the snapshot CEST approach for APTw imaging regarding flip angle, voxel size and frequency offset sampling, we extend it by undersampled GRE acquisition and compressed sensing reconstruction. This allows 2 mm isotropic whole-brain APTw imaging for clinical research at 3 T below 2 min. With this sequence, a fast snapshot APTw imaging method is now available for larger clinical studies of brain tumors.
CRIS-Team Scopus Importer:2023-08-04
Keywords: 3T CEST; APTw; brain tumor; compressed sensing; snapshot CEST

Super-Resolution for CEST MRI - Folle, Lukas; Tkotz, Katharina; Liebert, Andrzej; Gadjimuradov, Fasil; Kapsner, Lorenz; Fabian, Moritz; Bickelhaupt, Sebastian; Simon, David; Kleyer, Arnd; Krönke, Gerhard; Roemer, Frank; Zaiß, Moritz; et al. (Joint annual meeting ISMRM-ESMRMB ISMRT, 2022)
The resolution of chemical exchange saturation transfer (CEST) magnetic resonance imaging is limited by physical constraints. To visualize metabolic processes of small structures using CEST in patients knees, an increased resolution is necessary. In this work, we compared trilinear interpolation and zero-filling to neural network-based approaches to estimate a high-resolution image given the corresponding low-resolution data. We could show that a substantial quantitative improvement using neural networks could be achieved for unsaturated images while maintaining a comparable CEST contrast. Generalization of the method to brain CEST MRI was achieved without retraining of the networ
Keywords: MRI; Super-resolution

The Pulseq-CEST Library: definition of preparations and simulations, example data, and example evaluations - Liebeskind, Alexander; Schüre, Jan-Rüdiger; Fabian, Moritz; Weinmüller, Simon; Schünke, Patrick; Golkov, Vladimir; Cremers, Daniel; Zaiß, Moritz (Magnetic Resonance Materials in Physics Biology and Medicine, 2025)
Objectives: Despite prevalent use of chemical exchange saturation transfer (CEST) MRI, standardization remains elusive. Imaging depends heavily on parameters dictating radiofrequency (RF) events, gradients, and apparent diffusion coefficient (ADC). We present the Pulseq-CEST Library, a repository of CEST preparation and simulation definitions, including example data and evaluations, that provides a common basis for reproducible research, rapid prototyping, and in silico deep learning training data generation. Materials and methods: A Pulseq-CEST experiment requires (i) a CEST preparation sequence, (ii) a Bloch–McConnell parameter set, (iii) a Bloch–McConnell simulation, and (iv) an evaluation script. Pulseq-CEST utilizes the Bloch–McConnell equations to model in vitro and in vivo conditions. Using this model, a candidate sequence or environment can be held constant while varying other inputs, enabling robust testing. Results: Data were compared for amide proton transfer weighted (APTw) and water shift and B1 (WASABI) protocols using a five-tube phantom and simulated environments. Real and simulated data matched anticipated spectral shapes and local peak characteristics. The Pulseq-CEST Library supports similar experiments with common sequences and environments to assess new protocols and sample data. Discussion: The Pulseq-CEST Library provides a flexible mechanism for standardizing and prototyping CEST sequences, facilitating collaborative development. With the capability for expansion, including open-source incorporation of new sequences and environments, the library accelerates the invention and spread of novel CEST and other saturation transfer approaches, such as relayed NOEs (rNOEs) and semisolid magnetization transfer contrast (MTC) methods.
CRIS-Team Scopus Importer:2025-04-11
Keywords: Computer assisted; Computer simulation; Image processing; Imaging; Magnetic resonance imaging; Phantoms; Software

Towards Super-resolution CEST MRI for Visualization of Small Structures - Folle, Lukas; Tkotz, Katharina; Gadjimuradov, Fasil; Kapsner, Lorenz; Fabian, Moritz; Bickelhaupt, Sebastian; Simon, David; Kleyer, Arnd; Krönke, Gerhard; Zaiß, Moritz; Nagel, Armin Michael; Maier, Andreas (Bildverarbeitung für die Medizin 2022, 2022)
The onset of rheumatic diseases such as rheumatoid arthritis is typically subclinical, which results in challenging early detection of the disease. However, characteristic changes in the anatomy can be detected using imaging techniques such as MRI or CT. Modern imaging techniques such as chemical exchange saturation transfer (CEST) MRI drive the hope to improve early detection even further through the imaging of metabolites in the body. To image small structures in the joints of patients, typically one of the first regions where changes due to the disease occur, a high resolution for the CEST MR imaging is necessary. Currently, however, CEST MR suffers from an inherently low resolution due to the underlying physical constraints of the acquisition. In this work we compared established up-sampling techniques to neural network-based super-resolution approaches. We could show, that neural networks are able to learn the mapping from low-resolution to high-resolution unsaturated CEST images considerably better than present methods. On the test set a PSNR of 32.29 dB (+10%), a NRMSE of 0.14 (+28%), and a SSIM of 0.85 (+15%) could be achieved using a ResNet neural network, improving the baseline considerably. This work paves the way for the prospective investigation of neural networks for super-resolution CEST MRI and, followingly, might lead to a earlier detection of the onset of rheumatic diseases.
Keywords: Deep Learning; Rheumatic diseases; Reconstruction