Magnetic Resonance in Medicine

PurposeB0 inhomogeneity within the brain-feeding arteries is a major issue for pseudo-continuous arterial spin labeling (PCASL) at 7T because it reduces labeling efficiency and leads to a loss of perfusion signal. This study aimed to develop a vessel-specific dynamic B0 field shimming method for 7T PCASL to enhance labeling efficiency by correcting off-resonance in the arteries within the labeling region. MethodsWe implemented a PCASL sequence with dynamic B shimming at 7T that compensates for B0 field offsets at the brain-feeding arteries by updating linear shimming terms and adding a phase increment to the PCASL RF pulses. Rapidly acquired vessel-specific B field maps were used to calculate dynamic shimming parameters. We evaluated both 2D and 3D variants of our method, comparing their performance against established global frequency offset and optimal-encoding-scheme (OES)-based corrections. Cerebral blood flow (CBF) maps were quantified before and after corrections. CBF values from different methods in the whole brain, white matter, and grey matter regions were compared. ResultsAll off-resonance correction methods significantly enhanced perfusion signals across the brain. The proposed vessel-specific dynamic B shimming method improved labeling efficiency while maintaining optimal static shimming in the imaging region. Perfusion-weighted images demonstrated the superiority of 3D dynamic B shimming method compared to global or 2D-based correction approaches. CBF analysis revealed that 3D dynamic B shimming significantly increased CBF values relative to the other methods. ConclusionOur proposed dynamic B0 shimming method offers a significant advancement in PCASL robustness and effectiveness, enabling full utilization of 7T ASLs high sensitivity and spatial resolution.

PurposeTo demonstrate the advantages of spatiotemporal magnetic field monitoring to correct eddy current-induced artifacts (ghosting and geometric distortions) in high gradient strength diffusion MRI (dMRI). MethodsA dynamic field camera with 16 NMR field probes was used to characterize eddy current fields induced from diffusion gradients for different gradients strengths (up to 300 mT/m), diffusion directions, and shots in a 3D multi-shot EPI sequence on a 3T Connectom scanner. The efficacy of dynamic field monitoring-based image reconstruction was demonstrated on high-resolution whole brain ex vivo dMRI. A 3D multi-shot image reconstruction framework was informed with the actual nonlinear phase evolution measured with the dynamic field camera, thereby accounting for high-order eddy currents fields on top of the image encoding gradients in the image formation model. ResultsEddy current fields from diffusion gradients at high gradient strength in a 3T Connectom scanner are highly nonlinear in space and time, inducing high-order spatial phase modulations between odd/even echoes and shots that are not static during the readout. Superior reduction of ghosting and geometric distortion was achieved with dynamic field monitoring compared to ghosting approaches such as navigator- and structured low-rank-based methods or MUSE, followed by image-based distortion correction with eddy. Improved dMRI analysis is demonstrated with diffusion tensor imaging and high-angular resolution diffusion imaging. ConclusionStrong eddy current artifacts characteristic of high gradient strength dMRI can be well corrected with dynamic field monitoring-based image reconstruction, unlike the two-step approach consisting of ghosting correction followed by geometric distortion reduction with eddy.

The Gradient Impulse Response Function (GIRF) is widely used to model and correct gradient system imperfections in MRI, but scanner-specific GIRF measurement remains inaccessible to many research groups because existing approaches rely on specialised field monitoring hardware or fragmented and non-reproducible software workflows. To address this limitation, an open-source, end-to-end framework for phantom-based GIRF measurement is presented, providing a reproducible workflow requiring only standard MRI hardware and a spherical water phantom. The framework integrates vendor-independent pulse sequence generation, phantom-based data acquisition, automated data processing, and GIRF estimation. The framework was validated by comparing GIRF-predicted non-Cartesian k-space trajectories with independent measurements acquired using NMR field probes, which served as the gold-standard for trajectory characterization. Accurate prediction of rosette and spiral trajectories was demonstrated across multiple imaging orientations, with substantially lower trajectory error than the corresponding nominal trajectories. By providing the first openly available end-to-end implementation for phantom-based GIRF measurement, the barrier to routine scanner-specific GIRF characterisation is reduced, facilitating broader adoption of GIRF-based methods across the MRI community.

PurposeTo evaluate methods for quantification of pulmonary ventilation with ultrashort echo time (UTE) MRI. MethodsWe performed a reproducibility study, acquiring two free-breathing 1H UTE lung MRIs on the same day for six healthy volunteers. The 1) 3D + t cyclic b-spline and 2) symmetric image normalization (SyN) methods for image registration were applied after respiratory phase-resolved image reconstruction. Ventilation maps were calculated using 1) Jacobian determinant of the deformation fields minus one, termed regional ventilation, and 2) intensity percentage difference between the registered and fixed image, termed specific ventilation. We compared the reproducibility of all four method combinations via statistical analysis. ResultsSplit violin plots and Bland-Altman plots are shown for whole lungs and lung sections. The cyclic b-spline registration and Jacobian determinant regional ventilation quantification provide total ventilation volumes that match the segmentation tidal volume, smooth and uniform ventilation maps. The cyclic b-spline registration and specific ventilation combination yields the smallest standard deviation in the Bland-Altman plot. ConclusionCyclic registration performs better than SyN for respiratory phase-resolved 1H UTE MRI ventilation quantification. Regional ventilation correlates better with segmentation lung volume, while specific ventilation is more reproducible.

PurposeIn principle, combined T2-Diffusion (D) MRI has the microstructural and chemical sensitivity to detect axonal and myelin water changes in Alzheimers disease and related dementias (ADRD), but its practical implementation may be hindered by demanding hardware requirements. This work assesses the feasibility and accuracy of T2-D for ex vivo analysis of WM lesions in ADRD tissue. MethodsA thawed ex vivo brain sample from the Michigan Brain Bank and a T2-D phantom were scanned at 7T using a combined diffusion relaxometry (CDR) sequence. A non-negative least squares (NNLS) conventional data processing pipeline was used to disentangle water pools with unique T2-D signatures. Simulations examined the effects of minimum TE and SNR on recovery of myelin water (short T2, slow diffusion). ResultsAcross tissue types, T2-D data consistently resolved three spectral components. Phantom experiments showed detection of short T2 and slow diffusion features similar to those observed in ADRD ex vivo tissue, and confirmed CDRs ability to accurately resolve multiple components. Simulations indicated reliable T2-D recovery for myelin with SNR > 30 dB and minimum TE < 25 ms. ConclusionStrong T2 and D weighting could be combined to capture the expected axonal, myelin, and extracellular (EC) regions in T2-D space. The observed short-T2, restricted-D components are therefore unlikely to be artifacts and instead support interpretations as physically meaningful myelin and axonal water signatures.

1Abstract/SummarySingle-channel surface coils remain central to rodent MRI, but conventional circular loop designs face an inherent trade-off between surface and depth sensitivity, limiting whole-brain coverage for applications such as resting-state BOLD fMRI. This work introduces a single-channel strongly-coupled geometry surface coil. It consists of a stop-sign shaped loop inductively overcoupled to a nested, three-turn elongated racetrack spiral designed to improve depth sensitivity and thru-plane coverage while remaining robust to variable sample loading. Benchtop characterization across three phantoms of differing size showed the parallel resonant mode and loaded quality factor changed negligibly with loading. In phantom imaging at 9.4 T, the SCG coil achieved in-plane SNR and temporal SNR comparable to, and at shallow depths exceeding, a commercial Bruker 2x2 receive-only rat brain array, while showing substantially more consistent tSNR across loading conditions. The SCG coil also demonstrated superior thru-plane tSNR over a 20 mm slice range at 3.5 mm depth, approximating the anterior-posterior extent of the rat brain. In vivo resting-state BOLD fMRI in eight rats, acquired with a double asymmetric spin-echo EPI sequence, yielded a default mode network consistent with prior reports and revealed a previously undescribed subcortical network spanning superior/inferior colliculi and cerebellar regions. These results establish the single-channel SCG as a promising foundation for next-generation rodent receive coil arrays, combining loading-independent tuning with extended sensitive coverage suitable for whole-brain functional imaging.

PurposeTo develop an RF coil with an integrated commercial field camera for ultra-high field (7 T) neuroimaging. The RF coil will operate within a head-only gradient coil and be subject to the corresponding design constraints. The RF coil can thereafter be used for subject-specific correction of k-space trajectories--notably in gradient-sensitive sequences such as single-shot spiral imaging. MethodsThe transmit and receive performance was evaluated before and after the integration of field probes, while field probes were evaluated when in an optimal configuration external to the coil and after their integration. Diffusion-weighted EPI and single-shot spiral acquisitions were employed to evaluate the efficacy of correcting higher order field perturbations and the consequent effect on image quality. ResultsField probes had a negligible effect on RF-coil performance, including the transmit efficiency, transmit uniformity, and mean SNR over the brain. Modest reductions in field-probe signal lifetimes were observed, caused primarily by non-idealities in the gradient and shim fields of the head-only gradient coil at the probe positions. The field monitoring system could correct up to second-order field perturbations in single-shot spiral imaging. ConclusionThe integrated RF coil and field camera was capable of concurrent field monitoring within a 7T head-only scanner and facilitated the subsequent correction of k-space trajectories during spiral imaging.

A 3D density-weighted concentric ring trajectory (CRT) MRSI sequence is implemented for cardiac 31P-MRS at 7T. The point-by-point k-space sampling of traditional phase-encoded CSI sequences severely restricts the minimum scan time at higher spatial resolutions. Our proposed CRT sequence implements a stack of concentric rings trajectory, with a variable number of rings and planes spaced to optimise the density of k-space weighting. This creates flexibility in acquisition time, allowing acquisitions substantially faster than traditional phase-encoded CSI sequences, while retaining high SNR. We first characterise the signal-to-noise ratio and point spread function of the CRT sequence in phantoms. We then evaluate it at five different acquisition times and spatial resolutions in the hearts of five healthy participants at 7T. These different sequence durations are compared with existing published 3D acquisition-weighted CSI sequences with matched acquisition times and spatial resolutions. To minimise the effect of noise on the short acquisitions, low-rank denoising of the spatio-temporal data was also performed after acquisition. The proposed sequence measures 3D localised PCr/ATP ratios of the human myocardium in 2.5 minutes, 2.6 times faster than the minimum scan time for the acquisition-weighted phase-encoded CSI. Alternatively, in the same scan time a 1.7-times smaller nominal voxel volume can be achieved. Low-rank denoising reduced the variance of measured PCr/ATP ratios by 11% across all protocols. The faster acquisitions permitted by 7T CRT 31P-MRSI could make cardiac stress protocols or creatine kinase rate measurements (which involve repeated scans) more tolerable for patients without sacrificing spatial resolution.

Maps of the transverse relaxation rate R2* and magnetic susceptibility () are computed from gradient- echo data acquired at multiple echo times and are sensitive to signal instabilities induced by cardiac pulsation. Here, we introduce two k-space sampling strategies that aim to mitigate the impact of cardiac-induced noise in brain maps of R2* and . The proposed strategies are based on the higher level of cardiac-induced noise near the k-space centre compared to the periphery. Using a CArtesian trajectory with Spiral PRofile (CASPR), the first strategy allows for the acquisition of a specific number of averages at each k-space location, derived from the local level of cardiac-induced noise. The second strategy synchronizes the acquisition near the k-space centre with the cardiac cycle in real time. We compared the variability across 4 repetitions of R2* and maps computed from data acquired using both strategies and with a standard linear trajectory. Data was acquired in 10 healthy volunteers. Compared to linear trajectory, the CASPR trajectory reduced the variability of R2* and maps across repetitions by 26/28/22% and 19/18/16% in the brainstem/cerebellum/whole brain, for a 14% increase in scan time. The CASPR trajectory also reduced the level of aliasing artifacts from pulsating blood vessels. The synchronized trajectory did not reduce the variability of R2* or maps. CASPR trajectories can be designed to mitigate cardiac-induced noise in brain maps of the MRI parameters R2* and . Synchronization of data acquisition with the cardiac cycle did not reduce the level of cardiac-induced noise.

PurposeTo optimize a 100 msec pulse for producing CEST MRI contrast and evaluate in mice. MethodsA gradient ascent algorithm was employed to generate a family of 100 point, 100 msec pulses for use in CEST pulse trains ( PRECISE). Gradient ascent optimizations were performed for exchange rates (kca) = 500 s-1, 1,500 s-1, 2,500 s-1, 3,500 s-1 and 4,500 s-1 and offsets ({Delta}{omega}) = 9.6, 7.8, 4.2 and 2.0 ppm. 7 PRECISE pulse shapes were tested on an 11.7 T scanner using a phantom containing three representative CEST agents with peak saturation B1 = 4 T. The pulse producing the most contrast in phantoms was then evaluated for CEST MRI pH mapping of the kidneys in healthy mice after iopamidol administration. ResultsThe most promising pulse in terms of contrast performance across all three phantoms was the 9.6 ppm, 2500 s-1 optimized pulse with [~]2.7 x improvement over Gaussian and [~]1.3xs over Fermi pulses. This pulse also displayed a large improvement in contrast over the Gaussian pulse after administration of iopamidol in live mice. ConclusionA new 100 msec pulse was developed based on gradient ascent optimizations which produced better contrast compared to standard Gaussian and Fermi pulses in phantoms. This shape also showed a substantial improvement for CEST MRI pH mapping in live mice over the Gaussian shape and appears promising for a wide range of CEST applications.

PurposeDiffusion tensor imaging (DTI) is commonly used in cardiac diffusion magnetic resonance imaging (dMRI). However, the tissues microstructure (cells, membranes, etc.) restricts the movement of the water molecules, making the spin displacements deviate from Gaussian behaviour. This effect may be observed with diffusion kurtosis imaging (DKI) using sufficiently high b-values (b > 450 s/mm2), which are presently outside the realm of routine cardiac dMRI due to the limited gradient strength of clinical scanners. The Connectom scanner with Gmax = 300 mT/m enables high b-values at echo times (TE) similar to DTI on standard clinical scanners, therefore facilitating cardiac DKI in humans. MethodsCardiac-gated, second-order motion-compensated dMRI was performed with bmax = 1350 s/mm2 in 10 healthy volunteers on a 3T MRI scanner with Gmax = 300 mT/m. The signal was fitted to a cumulant expansion up to and including the kurtosis term and diffusion metrics such as fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK), axial kurtosis (AK), and radial kurtosis (RK) were calculated. ResultsWe demonstrate deviation of the signal from monoexponential decay for b-values > 450 s/mm2 (MK = 0.32 {+/-} 0.03). Radial kurtosis (RK = 0.35 {+/-} 0.04) was observed slightly larger than axial kurtosis (AK = 0.27 {+/-} 0.02), and the difference is statistically significant (RK - AK = 0.08 {+/-} 0.04, p = 2e - 4). ConclusionThis work demonstrates the feasibility of quantifying kurtosis effect in the human heart in vivo (at an echo time shorter than typical TEs reported for cardiac DTI), using high-performance gradient systems (which are 4-8 times stronger than on standard clinical scanners). Our work lays the foundation for exploring new biomarkers in cardiac dMRI beyond DTI.

PurposeTo create a realistic in-silico brain phantom for positive and negative magnetic susceptibility that incorporates susceptibility anisotropy, enabling the evaluation of how susceptibility anisotropy influences susceptibility separation algorithm performance. MethodsWe expanded an existing QSM validation phantom by creating separate maps for positive and negative susceptibility, with the option of modeling susceptibility anisotropy. Multi-echo gradient echo data were simulated to evaluate four susceptibility separation techniques ({chi}-separation, DECOMPOSE-QSM, APART-QSM, and [Formula]). To assess the impact of noise, simulations were performed at different SNR levels (50, 100, 200, 300). ResultsOur findings showed that the error in negative susceptibility estimates increased by up to 53% when susceptibility anisotropy was present, compared to the case without susceptibility anisotropy, with {chi}-separation being the algorithm that was most sensitive to anisotropy. Robustness to noise varied across the assessed algorithms, with APART-QSM and {chi}-separation having the highest and lowest sensitivity to noise, respectively. ConclusionThe modified phantom is open-source and can serve as a numerical ground truth for evaluating susceptibility separation methods. Our findings emphasize the importance of incorporating susceptibility anisotropy into susceptibility separation models to improve their accuracy.

AbstractO_ST_ABSPurposeC_ST_ABSMaps of the MRI parameters R2* and magnetic susceptibility () enable the investigation of microscopic tissue changes in brain disease. However, cardiac-induced signal instabilities increase the variability of brain maps of R2* and . In this study, we introduce ISME - a sampling strategy that minimizes the level of cardiac-induced instabilities in brain maps of R2* and . MethodsISME uses phase-encoding gradients to shift the k-space frequency of the acquired data between consecutive readouts of a multi-echo train. As a result, the multi-echo data at a given k-space index is acquired at different phases of the cardiac cycle. We compare the variability of R2* and maps acquired with ISME and with standard multi-echo trajectories in N=10 healthy volunteers. We investigate the effect of both trajectories on the spatial aliasing of pulsating MR signals and propose a weighted-least squares (NWLS) approach for the estimation of R2* that accounts for the increase of the residuals with echo time. ResultsISME reduces the variability of R2* and maps across repetitions by 25/26/21% and 24/32/23% in the cerebellum/brainstem/whole brain, respectively. With ISME, the spatial aliasing of pulsating MR signals is incoherent between raw echo images, leading to visually sharper R2* maps. The proposed NWLS approach for the estimation of R2* reduces the dependence of the fitting residuals on echo time and the variability of R2* by an additional 3/2/1% in the cerebellum/brainstem/whole brain. ConclusionISME allows the mitigation of cardiac-induced signal instabilities in brain maps of R2* and , improving reproducibility.

PurposeTo develop an efficient acquisition technique for distortion-free diffusion MRI and diffusion-relaxometry. MethodsA new ACcelerated Echo-train shifted Echo-Planar Time-resolved Imaging (ACE-EPTI) technique is developed to achieve high-SNR, distortion- and blurring-free diffusion and diffusion-relaxometry imaging. ACE-EPTI employs a newly designed variable density spatiotemporal encoding with self-navigation capability, that allows submillimeter in-plane resolution using only 3-shot. Moreover, an echo-train-shifted acquisition is developed to achieve minimal TE, together with an SNR-optimal readout length, leading to ~30% improvement in SNR efficiency over single-shot EPI. To recover the highly accelerated data with high image quality, a tailored subspace image reconstruction framework is developed, that corrects for odd/even-echo phase difference, shot-to-shot phase variation, and the B0 field changes due to field drift and eddy currents across different dynamics. After the phase-corrected subspace reconstruction, artifacts-free high-SNR diffusion images at multiple TEs are obtained with varying T2* weighting. ResultsSimulation, phantom and in-vivo experiments were performed, which validated the 3-shot spatiotemporal encoding provides accurate reconstruction at submillimeter resolution. The use of echo-train shifting and optimized readout length improves the SNR-efficiency by 27-36% over single-shot EPI. The reconstructed multi-TE diffusion images were demonstrated to be free from distortion (susceptibility and eddy currents) and phase/field variation induced artifacts. These improvements of ACE-EPTI enable improved diffusion tensor imaging and rich multi-TE information for diffusion-relaxometry analysis. ConclusionACE-EPTI was demonstrated to be an efficient and powerful technique for high-resolution diffusion imaging and diffusion-relaxometry, which provides high SNR, distortion- and blurring-free, and time-resolved multi-echo images by a fast 3-shot acquisition.

PurposeTo introduce the OmniShim Toolbox, a software package designed to calibrate and perform B0 shimming in user-defined regions of interest across systems from different vendors, supporting various shim orders. MethodsIn this study, we systematically compared the performance of vendor-implemented B0 shim routines with our custom developed OmniShim toolbox, designed to improve static magnetic field homogeneity. Single-voxel magnetic resonance spectroscopy (MRS) data were acquired from the prefrontal cortex and occipital lobe, while magnetic resonance spectroscopic imaging (MRSI) data were collected from regions above and below the corpus callosum in healthy volunteers. Measurements were conducted on both 3T and 7T MR systems to evaluate the robustness and scalability of each B0 shimming strategy across different field strengths. Additionally, functional MRI (fMRI) data were acquired at 7T to assess the impact of improved shimming on EPI data quality and BOLD contrast. ResultsThe OmniShim Toolbox demonstrated superior B0 homogeneity across all applications and field strength, leading to reduced signal dropout in fMRI and MRSI data, significantly improved spectral linewidths in SV MRS and MRSI data as well improved detection of neural networks by resting state fMRI. ConclusionThe proposed OmniShim Toolbox offers a robust and flexible approach to control B0 inhomogeneity, resulting in substantial improvements in image quality and spectral resolution. These enhancements benefit a broad range of MR applications.

Physiological brain pulsations, primarily driven by cardiac and respiratory activity, play a key role in driving neurofluid circulation and waste clearance. Capturing the temporal dynamics of cardiac- and respiratory-driven brain pulsations (0.2-1.5 Hz) requires fast imaging with TRs near 100 ms, which is often unachievable in functional MRI or dynamic diffusion MRI. As a result, valuable physiological information remains hidden in these datasets. Here, we introduce TRACC-PHYSIO, a time-domain analytical framework designed to quantify physiological coupling and pulse time delays in dynamic MRI without requiring a fast acquisition. TRACC-PHYSIO uses cross-correlation to detect co-fluctuations between slowly sampled dynamic MRI data and simultaneously recorded physiological waveforms. It measures two key metrics: the peak Coupling Coefficient (peak CorrCoeff), quantifying the strength of co-fluctuations, and the TimeDelay, reflecting the relative arrival time of the physiological impulse in the brain with millisecond-level temporal resolution. The primary aim of this study is to validate TRACC-PHYSIO through systematic simulations that model realistic dynamic MR signals with mixed physiological components. We comprehensively evaluate TRACC-PHYSIOs performance under a wide range of conditions, including varying cardiac-to-respiratory composition ratios, TRs, and acquisition times. Results demonstrate that TRACC-PHYSIO can robustly assess coupling strengths and time delays for both cardiac (TRACC-Cardiac) and respiratory (TRACC-Respiratory) components, even in datasets with long TRs up to 3 seconds. By enabling a reliable time-domain coupling analysis, TRACC-PHYSIO opens new avenues for revealing brain pulsation mechanisms and elucidating the physiological drivers of neurofluid dynamics in health and disease. This stimulation study provides a valuable reference for interpreting TRACC-PHYSIO results and understanding associated uncertainties in future applications. Highlights- TRACC-PHYSIO is a time-domain method developed to estimate cardiac and respiratory coupling and their pulsation time delays in dynamic MRI without requiring high temporal resolution. - TRACC-PHYSIO was validated through systematic simulations across varying physiological compositions and MR acquisition parameters. - Results demonstrated that TRACC-PHYSIO reliably quantifies cardiac and respiratory components in dynamic MR signals. - The stimulation study provides a useful reference for interpreting TRACC-PHYSIO results and understanding associated uncertainties in future applications.

Cerebrospinal fluid (CSF) flow is a key component of the brains waste clearance system. However, our understanding of CSF flow in the human brain, particularly within the brain-wide subarachnoid space (SAS), is limited due to a lack of non-invasive tools for measuring slow flow. Here, we propose a CSF flowmetry technique using phase-contrast MRI combined with a slow-flow-sensitized acquisition. It achieves high sensitivity in measuring slow CSF flow (e.g., 100 m/s), and enables quantitative measurement of the velocity and direction with whole-brain coverage, spanning from ventricles to SAS. Our proof-of-concept results demonstrate repeatable flow measurements and show that cardiac pulsation induces coherent CSF flow changes within the SAS. Our data also suggest that cardiac pulsation has a stronger driving effect on brain-wide CSF flow compared to respiration. This technique provides a valuable tool for investigating CSF dynamics and pathways to advance a holistic understanding of brain-wide CSF flow. TeaserA novel MR technique enables noninvasive, quantitative mapping of slow cerebrospinal fluid flow in the subarachnoid space across the human brain.

BackgroundMethylmalonic acidemia (MMAemia) is a genetic metabolic disorder characterized by an accumulation of methylmalonic acid (MMA) and impaired energy metabolism leading to increased lactate (Lac). The signals of MMA (1.23 ppm) and Lac (1.33 ppm) overlap, making their separation using conventional MRS challenging. An MRS method to differentiate the two metabolites could enhance pathophysiological understanding and improve treatment monitoring - Hadamard-edited MRS has the potential to achieve this. PurposeTo develop a Hadamard-encoded J-difference editing approach for independent detection of MMA and Lac at 3T. MethodsA novel Hadamard-encoded editing scheme was implemented and evaluated with density-matrix simulations, phantom and in vivo experiments. The new four-step scheme uses frequency-selective editing pulses, applied at 3.2 ppm and 4.1 ppm to modulate the J-coupled methyl resonances of MMA and Lac, respectively. Hadamard combinations of the four sub-experiments yield the separate difference-edited spectra for each target metabolite. ResultsSimulations and phantom experiments clearly illustrate the separated signals of MMA and Lac. In vivo validation experiments show a Lac signal (but no MMA) in a healthy infant, and both Lac and MMA (separated into their respective Hadamard-combination spectra) in a patient with MMAemia. ConclusionHadamard-encoded editing at 3T can separate MMA and Lac signals and shows promise for studying altered metabolism in patients with MMAemia.

PurposeSegmented 3D Gradient and Spin Echo (GRASE) is commonly used in Arterial Spin Labeling (ASL) perfusion imaging. However, it is vulnerable to inter-shot motion, leading to subtraction errors that cannot be corrected. We developed a retrospective self-navigated inter-shot motion correction method for segmented 3D-GRASE ASL imaging with Controlled Aliasing in Parallel Imaging (CAIPI). MethodsMultiple shots, each uniformly covering k-space at distinct sample locations, allow a self-navigator image to be reconstructed using SENSE for each shot. Rigid-body motion estimation across the self-navigators is incorporated into a motion-compensated forward model for image reconstruction. To support self-navigation, two CAIPI-sampled segmented 3D-GRASE trajectories that ensure full k-space coverage were explored for point spread function (PSF) profiles and g-factor effects. Our approach was evaluated against conventional inter-volume registration and a previously proposed method, alignedSENSE. Additionally, we compared tag-control interleaving strategies to assess their impact on motion robustness in five healthy volunteers with instructed head motion. ResultsOur method effectively reduced motion artifacts and outperformed conventional inter-volume correction by 12.3% in correlation coefficient, 4.5% in Structural Similarity Index Measure (SSIM), and 40.1% in temporal SNR. It matched alignedSENSE performance while requiring only 20% of the computational time. All evaluated CAIPI sampling variants enabled robust motion correction, although tradeoffs were observed between through-plane blurring and SNR performance. The tag-control (T/C) inner loop acquisition yielded better motion robustness across all quantitative metrics. ConclusionSelf-navigated inter-shot motion correction using CAIPI sampling and a T/C inner loop for segmented 3D-GRASE ASL can improve image quality and motion robustness.

PurposeThis study aims to reduce Diffusion Tensor MRI (DT-MRI) scan time by minimizing diffusion-weighted measurements. Using machine learning, DT-MRI parameters are accurately estimated with just four tetrahedrally-arranged diffusion-encoded measurements, instead of the usual six or more. This significantly shortens scan duration and is particularly useful in ultra-low field (ULF) MRI studies and for non-compliant populations (e.g., children, the elderly, or those with movement disorders) where long scan times are impractical. MethodsTo improve upon a previous tetrahedral encoding approach, this study used a deep learning (DL) model to predict parallel and radial diffusivities and the principal eigenvector of the diffusion tensor with four tetrahedrally-arranged diffusion-weighted measurements. Synthetic data were generated for model training, covering a range of diffusion tensors with uniformly distributed eigenvectors and eigenvalues. Separate DL models were trained to predict diffusivities and principal eigenvectors, then evaluated on a digital phantom and in vivo data collected at 64 mT. ResultsThe DL models outperformed the previous tetrahedral encoding method in estimating diffusivities, fractional anisotropy, and principal eigenvectors, with significant improvements in ULF experiments, confirming the DL approachs feasibility in low SNR scenarios. However, the models had limitations when the tensors principal eigenvector aligned with the scanners axes ConclusionThe study demonstrates the potential of using DL to perform DT-MRI with only four directions in ULF environments, effectively reducing scan durations and addressing numerical instability seen in previous methods. These findings open new possibilities for ULF DT-MRI applications in research and clinical settings, particularly in pediatric neuroimaging