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Purpose: Subject movement during the MR examination is inevitable and causes not only image artifacts but also deteriorates the homogeneity of the main magnetic field (B0), which is a prerequisite for high quality data. Thus, characterization of changes to B0, for example induced by patient movement, is important for MR applications that are prone to B0 inhomogeneities. Methods: We propose a deep learning based method to predict such changes within the brain from the change of the head position to facilitate retrospective or even real‐time correction. A 3D U‐net was trained on in vivo gradient‐echo brain 7T MRI data. The input consisted of B0 maps and anatomical images at an initial position, and anatomical images at a different head position (obtained by applying a rigid‐body transformation on the initial anatomical image). The output consisted of B0 maps at the new head positions. We further fine‐trained the network weights to each subject by measuring a limited number of head positions of the given subject, and trained the U‐net with these data. Results: Our approach was compared to established dynamic B0 field mapping via interleaved navigators, which suffer from limited spatial resolution and the need for undesirable sequence modifications. Qualitative and quantitative comparison showed similar performance between an interleaved navigator‐equivalent method and proposed method. Conclusion: It is feasible to predict B0 maps from rigid subject movement and, when combined with external tracking hardware, this information could be used to improve the quality of MR acquisitions without the use of navigators. [ABSTRACT FROM AUTHOR]

Purpose: In this study, we demonstrate the first combination of 3D FID proton MRSI and spatial encoding via concentric‐ring trajectories (CRTs) at 3T. FID‐MRSI has many benefits including high detection sensitivity, in particular for J‐coupled metabolites (e.g., glutamate/glutamine). This makes it highly attractive, not only for clinical, but also for, potentially, functional MRSI. However, this requires excellent reliability and temporal stability. We have, therefore, augmented this 3D‐FID‐MRSI sequence with single‐echo, imaging‐based volumetric navigators (se‐vNavs) for real‐time motion/shim‐correction (SHMOCO), which is 2× quicker than the original double‐echo navigators (de‐vNavs), hence allowing more efficient integration also in short‐TR sequences. Methods: The tracking accuracy (position and B0‐field) of our proposed se‐vNavs was compared to the original de‐vNavs in phantoms (rest and translation) and in vivo (voluntary head rotation). Finally, the intra‐session stability of a 5:40 min 3D‐FID‐MRSI scan was evaluated with SHMOCO and no correction (NOCO) in 5 resting subjects. Intra/inter‐subject coefficients of variation (CV) and intra‐class correlations (ICC) over the whole 3D volume and in selected regions of interest ROI were assessed. Results: Phantom and in vivo scans showed highly consistent tracking performance for se‐vNavs compared to the original de‐vNavs, but lower frequency drift. Up to ~30% better intra‐subject CVs were obtained for SHMOCO (P < 0.05), with values of 9.3/6.9/6.5/7.8% over the full VOI for Glx/tNAA/tCho/m‐Ins ratios to tCr. ICCs were good‐to‐high (91% for Glx/tCr in motor cortex), whereas the inter‐subject variability was ~11–19%. Conclusion: Real‐time motion/shim corrected 3D‐FID‐MRSI with time‐efficient CRT‐sampling at 3T allows reliable, high‐resolution metabolic imaging that is fast enough for clinical use and even, potentially, for functional MRSI. [ABSTRACT FROM AUTHOR]

Purpose: A properly characterized macromolecular (MM) contribution is essential for accurate metabolite quantification in FID‐MRSI. MM information can be included into the fitting model as a single component or parameterized and included over several individual MM resonances, which adds flexibility when pathologic changes are present but is prone to potential overfitting. This study investigates the effects of different MM models on MRSI reproducibility. Methods: Clinically feasible, high‐resolution FID‐MRSI data were collected in ~5 min at 7 Tesla from 10 healthy volunteers and quantified via LCModel (version 6.3) with 3 basis sets, each with a different approach for how the MM signal was handled: averaged measured whole spectrum (full MM), 9 parameterized components (param MM) with soft constraints to avoid overparameterization, or without any MM information included in the fitting prior knowledge. The test–retest reproducibility of MRSI scans was assessed voxel‐wise using metabolite coefficients of variation and intraclass correlation coefficients and compared between the basis sets. Correlations of concentration estimates were investigated for the param MM fitting model. Results: The full MM model provided the most reproducible quantification of total NAA, total Cho, myo‐inositol, and glutamate + glutamine ratios to total Cr (coefficients of variations ≤ 8%, intraclass correlation coefficients ≥ 0.76). Using the param MM model resulted in slightly lower reproducibility (up to +3% higher coefficients of variations, up to −0.1 decreased intraclass correlation coefficients). The quantification of the parameterized macromolecules did not affect quantification of the overlapping metabolites. Conclusion: Clinically feasible FID‐MRSI with an experimentally acquired MM spectrum included in prior knowledge provides highly reproducible quantification for the most common neurometabolites in healthy volunteers. Parameterization of the MM spectrum may be preferred as a compromise between quantification accuracy and reproducibility when the MM content is expected to be pathologically altered. [ABSTRACT FROM AUTHOR]

Purpose: Full‐slice magnetic resonance spectroscopic imaging at ≥ 7 T is especially vulnerable to lipid contaminations arising from regions close to the skull. This contamination can be mitigated by improving the point spread function via higher spatial resolution sampling and k‐space filtering, but this prolongs scan times and reduces the signal‐to‐noise ratio (SNR) efficiency. Currently applied parallel imaging methods accelerate magnetic resonance spectroscopic imaging scans at 7T, but increase lipid artifacts and lower SNR‐efficiency further. In this study, we propose an SNR‐efficient spatial‐spectral sampling scheme using concentric circle echo planar trajectories (CONCEPT), which was adapted to intrinsically acquire a Hamming‐weighted k‐space, thus termed density‐weighted‐CONCEPT. This minimizes voxel bleeding, while preserving an optimal SNR. Theory and Methods: Trajectories were theoretically derived and verified in phantoms as well as in the human brain via measurements of five volunteers (single‐slice, field‐of‐view 220 × 220 mm2, matrix 64 × 64, scan time 6 min) with free induction decay magnetic resonance spectroscopic imaging. Density‐weighted‐CONCEPT was compared to (a) the originally proposed CONCEPT with equidistant circles (here termed e‐CONCEPT), (b) elliptical phase‐encoding, and (c) 5‐fold Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration accelerated elliptical phase‐encoding. Results: By intrinsically sampling a Hamming‐weighted k‐space, density‐weighted‐CONCEPT removed Gibbs‐ringing artifacts and had in vivo +9.5%, +24.4%, and +39.7% higher SNR than e‐CONCEPT, elliptical phase‐encoding, and the Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration accelerated elliptical phase‐encoding (all P < 0.05), respectively, which lead to improved metabolic maps. Conclusion: Density‐weighted‐CONCEPT provides clinically attractive full‐slice high‐resolution magnetic resonance spectroscopic imaging with optimal SNR at 7T. Magn Reson Med 79:2874–2885, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. [ABSTRACT FROM AUTHOR]

Purpose: Proton ( 1 H)-MRSI via spatial-spectral encoding poses high demands on gradient hardware at ultra-high fields and high-resolutions. Rosette trajectories help alleviate these problems, but at reduced SNR-efficiency because of their k-space densities not matching any desired k-space filter. We propose modified rosette trajectories, which more closely match a Hamming filter, and thereby improve SNR performance while still staying within gradient hardware limitations and without prolonging scan time., Methods: Analytical and synthetic simulations were validated with phantom and in vivo measurements at 7 T. The rosette and modified rosette trajectories were measured in five healthy volunteers in 6 min in a 2D slice in the brain. An elliptical phase-encoding sequence was measured in one volunteer in 22 min, and a 3D sequence was measured in one volunteer within 19 min. The SNR per-unit-time, linewidth, Cramer-Rao lower bounds (CRLBs), lipid contamination, and data quality of the proposed modified rosette trajectory were compared to the rosette trajectory., Results: Using the modified rosette trajectories, an improved k-space weighting function was achieved resulting in an SNR per-unit-time increase of up to 12% compared to rosette's and 23% compared to elliptical phase-encoding, dependent on the two additional trajectory parameters. Similar results were achieved for the theoretical SNR calculation based on k-space densities, as well as when using the pseudo-replica method for simulated, in vivo, and phantom data. The CRLBs of γ-aminobutyric acid and N-acetylaspartylglutamate improved non-significantly for the modified rosette trajectory, whereas the linewidths and lipid contamination remained similar., Conclusion: By optimizing the shape of the rosette trajectory, the modified rosette trajectories achieved higher SNR per-unit-time and enhanced data quality at the same scan time., (© 2024 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: Subject movement during the MR examination is inevitable and causes not only image artifacts but also deteriorates the homogeneity of the main magnetic field (B 0 ), which is a prerequisite for high quality data. Thus, characterization of changes to B 0 , for example induced by patient movement, is important for MR applications that are prone to B 0 inhomogeneities., Methods: We propose a deep learning based method to predict such changes within the brain from the change of the head position to facilitate retrospective or even real-time correction. A 3D U-net was trained on in vivo gradient-echo brain 7T MRI data. The input consisted of B 0 maps and anatomical images at an initial position, and anatomical images at a different head position (obtained by applying a rigid-body transformation on the initial anatomical image). The output consisted of B 0 maps at the new head positions. We further fine-trained the network weights to each subject by measuring a limited number of head positions of the given subject, and trained the U-net with these data., Results: Our approach was compared to established dynamic B 0 field mapping via interleaved navigators, which suffer from limited spatial resolution and the need for undesirable sequence modifications. Qualitative and quantitative comparison showed similar performance between an interleaved navigator-equivalent method and proposed method., Conclusion: It is feasible to predict B 0 maps from rigid subject movement and, when combined with external tracking hardware, this information could be used to improve the quality of MR acquisitions without the use of navigators., (© 2024 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)