Magnetic Resonance in Medicine

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.

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.

Quantitative Susceptibility Mapping (QSM) relies on a tissue's underlying macroscopic geometry to lead to measurable orientation-dependent field perturbations. To understand and assess QSM error in vivo, anthropomorphic phantoms provide a useful model that mimic the electromagnetic properties and morphology of underlying tissue. Herein, we designed and manufactured an MRI compatible anthropomorphic phantom with cylindrical and spheroid compartments containing realistic susceptibilities to mimic hemorrhages, calcifications, and blood vessels. We estimated accuracy ({epsilon}, bias, RMSE) and repeatability (RC) of MEDI-susceptibility measurements within ROIs. We evaluated voxel-based agreement to validate susceptibility mapping under different acquisition conditions (3T versus 7T) and reconstruction algorithms (COSMOS versus MEDI). Reliable MEDI-based susceptibility measurements were obtained from ellipsoids but not from straws. The ellipsoids (|{epsilon}| = 0.007 to 0.083 ppm at 3T; 0.050 to 0.118 ppm at 7T) were more accurate than the straws (|{epsilon}| = 0.084 to 0.190 ppm at 3T; 0.105 to 0.160 ppm at 7T). The repeatability coefficient across all 6 ROIs (RC = 0.652 ppm at 3T; 0.459 ppm at 7T) was substantially larger than across the 4 ellipsoid ROIs only (RC' = 0.168 ppm at 3T; 0.141 ppm at 7T). The accuracy at 3T (bias = -0.002 ppm, RMSE = 0.082 ppm) was better than the accuracy at 7T (bias = -0.056 ppm, RMSE = 0.092 ppm). Using voxels from the 4 ellipsoid ROIs, we observed excellent agreement between COSMOS and MEDI susceptibility maps at 3T, with linear regression of y=1.00x-0.01 (r=0.99). We observed some underestimation of MEDI susceptibility maps relative to COSMOS at 7T, with linear regression and y=0.93x-0.04 (r=0.99). The results imply that QSM reconstructions are reliable with 3T scanners but can be challenging with 7T scanners at high magnetic susceptibilities.

Purpose: Quantitative susceptibility mapping (QSM) provides venous contrast through the paramagnetic susceptibility of deoxyhemoglobin and can be used to estimate oxygen extraction fraction (OEF), a marker of cerebral metabolism. However, cerebral vein segmentation remains challenging due to artifacts, variability across QSM reconstruction methods, and limited sensitivity of conventional vessel-filtering approaches to small cortical veins. This study proposes a physics- and morphology-constrained deep learning framework for cerebral vein segmentation on QSM. Methods: Thirty subjects were manually segmented and used to train an attention-gated UNet. In addition to supervised Dice and cross-entropy losses, the network incorporated two selfsupervised constraints. A physics-informed loss enforced consistency between the measured local field and a field simulated from predicted veins using dipole convolution. A Frangi vesselness loss encouraged anatomically plausible tubular structures. Segmentation performance was evaluated on an independent multi-center cohort and compared with a prior vessel-filtering method and a supervised U-Net baseline. Physiological relevance was assessed by comparing QSM-derived venous OEF with calibrated fMRI-derived OEF. Results: The proposed model achieved superior segmentation performance (centreline Dice = 0.70 {+/-} 0.08) compared with MSVF (0.44 {+/-} 0.09) and the U-Net baseline (0.57 {+/-} 0.14). Improvements were most pronounced in smaller veins, resulting in greater vessel continuity. Venous OEF derived from the proposed framework demonstrated stronger agreement with calibrated fMRI-derived OEF than the previous approach. Conclusion: Combining supervised learning with physics- and morphology-based constraints improves vein segmentation and enhances the sensitivity of QSM-derived OEF measurements. Code and model weights are publicly available at https://github.com/QuantitativePhysiologyImagingLab/VeinSeg

Abstract: Purpose: Superparamagnetic iron oxide nanoparticles (SPION) are widely used as MRI contrast agents for cell tracking and molecular imaging. However, their detection using conventional T2/T2* weighted and susceptibility-weighted MRI relies on negative contrast (signal voids), which lacks specificity because similar hypointense signals can arise from other sources of magnetic susceptibility. This study introduces Frequency Shift Imaging (FSI), a positive-contrast technique based on an adiabatic Shinnar-Le Roux (SLR) self-refocused pulse that addresses the limited specificity of conventional SPION imaging. Methods: An adiabatic self-refocused RF pulse was designed using the SLR algorithm for frequency-selective positive-contrast imaging. The optimized pulse had a spectral bandwidth of 370 Hz, a peak RF amplitude of 14 T, and a duration of 15 ms. FSI data were acquired on a 7T MRI scanner. Phantom experiments used agarose-embedded 30 nm cell-labeled SPION at 0%, 25%, 75%, and 100% concentrations (pure). In vivo validation was performed following intramuscular SPION injection into the hind limb of a mouse. FSI images were compared with conventional acquisitions. Results: Quantitative phantom analysis showed normalized FSI signal intensities of 25.02% and 75.16% for samples containing 25% and 75% labeled cells, respectively, demonstrating an approximately proportional relationship between FSI signal and labeled-cell concentration. In vivo, FSI generated localized hyperintense positive contrast at the SPIO injection site, providing improved conspicuity and localization compared with the corresponding negative-contrast signal void. Discussion and Conclusion: FSI enables robust, B1-insensitive, positive-contrast SPION imaging at 7T with reduced echo time and SAR compared to paired adiabatic approaches, supporting its potential for quantitative cell tracking and molecular imaging applications.

Purpose: To demonstrate the feasibility of whole-brain multidimensional T1-T2 relaxation imaging on a portable 0.064 T MRI scanner. Methods: A two-dimensional inversion-recovery, fast spin-echo acquisition was used to jointly encode T1 and T2 relaxation. Joint relaxation distributions were reconstructed voxel-wise using marginal-distribution constrained optimization (MADCO). The approach was evaluated in a quantitative relaxation MRI phantom and subsequently applied in vivo in a healthy volunteer. Joint distributions, marginal distributions, distribution-derived measures, and statistical dependencies between the two relaxation dimensions were examined. Results: Phantom joint relaxation estimates showed good overall agreement with mono-exponential relaxation measurements and previously reported values at 0.064 T. The in vivo acquisition provided whole-brain coverage over a broad range of relaxation weightings and tissue contrasts. Reconstructed joint T1-T2 distributions showed spatially organized features across the relaxation space, including broad and overlapping relaxation components that were not fully represented by single-value relaxation maps or by either 1D marginal distribution alone. Selected regions of the joint relaxation space produced component maps with distinct spatial patterns. Statistical analysis further showed a dependence between the two relaxation dimensions, supporting the presence of added information in the joint distribution that is lost when treating T1 and T2 separately. Conclusion: Whole-brain multidimensional T1-T2 relaxation imaging is clinically feasible on a portable 0.064 T MRI scanner. Joint relaxation distributions provide information beyond single-value relaxation mapping and may support further developments of quantitative multidimensional imaging at ultra-low field.

Purpose: We aim to acquire and reconstruct high signal-to-noise ratio (SNR), whole-brain, mesoscale (isotropic submillimeter) diffusion MRI (dMRI) in vivo in a clinically feasible scan time. Methods: We present ROVER-dMRI, a unified rotating-view acquisition and super-resolution reconstruction framework. Thick slices (up to 10 mm)acquired across many rotated orientations improve the base SNR, and a shared multi-resolution hash-encoded implicit neural representation (INR) with per-direction MLP head and a physics-informed, Bloch-simulated slice-profile forward model reconstructs these views into a continuous isotropic volume (up to 13x super-resolution) in a self-supervised manner. ROVER-dMRI was evaluated in vivo on human brain (0.78 mm isotropic and 0.5 mm isotropic) and monkey brain (0.6 mm isotropic) across MRI platforms with varying gradient performance, and benchmarked against gSlider-SMS and least-squares super-resolution reconstruction (LS-SRR). Results: At 0.78 mm isotropic resolution, ROVER-dMRI diffusion- weighted images achieved higher SNR than the vendor product sequence, whose images were limited by the noise floor, while its directionally encoded color (DEC) maps showed lower noise than gSlider. When reducing the number of views from 20 to 13 (reducing the scan time by 65% ), ROVER-dMRI acquisition/reconstruction preserved all fine details in the fiber orientations. In the monkey brain, its DEC map matched a 60-direction gSlider acquisition at only 40% of the scan time. ROVER-dMRI preserved coherent fiber-orientation-distribution (FOD) structure, including crossing fibers, and recovered fine details in the cerebellum at 0.5 mm isotropic, that are not visible at 0.78 mm resolution. Compared to state-of- the-art 0.5 mm methods, ROVER-dMRI reduced scan time by 73% while acquiring more diffusion directions. Conclusion: ROVER-dMRI offers a practical route to high-SNR whole-brain mesoscale diffusion imaging in vivo, with the pulse sequence and reconstruction pipeline released as open source. KEYWORDS: diffusion MRI, super-resolution, implicit neural representation, hash encoding, physics informed learning

Abstract Purpose: To determine whether structural MRI information can improve reconstruction of highly accelerated 3D snapshot chemical exchange saturation transfer (CEST) MRI. Methods: Fully sampled brain CEST data were retrospectively undersampled at acceleration factors (AFs) of 4, 6, 8, 10, and 12. We compared reconstruction without structural information with population-level pretraining using T1-weighted (T1w), T2-weighted (T2w), or combined T1w-T2w images, as well as direct conditioning using a co-registered subject-specific T1w image. Performance was evaluated in two held-out healthy subjects using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean absolute error (MAE) across frequency offsets. Z-spectrum fidelity and derived magnetization transfer ratio asymmetry (MTRasym) maps were additionally evaluated within three-dimensional gray- and white-matter masks. Results: Subject-specific T1w conditioning consistently achieved the highest PSNR and SSIM and the lowest MAE across AFs, with greater improvements at higher acceleration. It also improved Z-spectrum agreement, particularly in white matter and combined gray- and white-matter regions across most AFs. Improvements in MTRasym were more modest and varied across tissue types and acceleration factors. Conclusion: Incorporating subject-specific structural information can improve reconstruction of highly accelerated snapshot CEST MRI, particularly at higher acceleration factors. However, improvements in reconstructed source images did not consistently translate into better preservation of derived CEST contrast, highlighting the need for reconstruction strategies that more directly incorporate CEST spectral information.

Arterial spin labeling (ASL) provides a valuable non-invasive tool for investigating cerebrospinal fluid (CSF) dynamics. While existing generalized kinetic models provide a useful analytical framework, incorporating explicit fluid mass-conservation constraints may improve the reliability of ventricular CSF quantification. We developed and validated a physics-constrained kinetic model that assumes a constant ventricular volume, under which the volumetric influx and efflux rates are balanced. Under this assumption, the localized CSF renewal rate (f) is modeled as a distinct washout process that acts jointly with intrinsic CSF T1 relaxation R1,CSF, yielding an effective decay rate Reff = R1,CSF + f, providing a more mechanistically interpretable description of the post-arrival signal decay. The model was further tailored to the global inversion physics of the FAIR (Flow-sensitive Alternating Inversion Recovery) sequence and incorporates an explicit zero-clamped pre-arrival boundary condition. When applied to an in vivo preclinical dataset, the proposed model, improved fitting stability and removed the finite-bolus truncation observed in the raw conventional model. Compared with the conventional model, the proposed model showed significantly improved goodness-of-fit (R2 = 0.95 {+/-} 0.05 vs. 0.82 {+/-} 0.06, p < 0.001) and a lower Akaike Information Criterion (AIC = 94.96 {+/-} 5.83 vs. 106.93 {+/-} 2.50, p < 0.001), suggesting it provides a more adequate and efficient representation of CSF dynamics. The proposed model yielded CSF dynamics estimates 14.70% higher than those obtained with the conventional model, with a mean ventricular CSF renewal time of 4.01 {+/-} 0.97 min and a renewal-equivalent volumetric flow rate of 0.97 {+/-} 0.41 {micro}L/min. The resulting estimates might be interpreted as localized, ASL-derived renewal metrics rather than direct measurements of net CSF production.

Cerebrospinal fluid (CSF) motion is coupled with global blood oxygenation level-dependent (BOLD) fluctuations, but the spatial relationship between regional brain activity and CSF dynamics remains poorly understood. Here, we developed a single-shot BOLD-VENC sequence that combines gradient-echo BOLD imaging with spin-echo velocity encoding following the same RF excitation, enabling simultaneous measurement of brain-wide BOLD activity and spatially resolved slow CSF velocity at 3T. The velocity measurement was validated in a slow-flow phantom and in five healthy participants using paced-breathing, breath-holding, and visual-stimulation experiments. Phantom measurements showed strong agreement with prescribed velocities over 0.1-1.0 mm/s (R2 = 0.93-0.98). In vivo measurements demonstrated respiratory- and cardiac-dependent changes in CSF velocity magnitude and direction across the ventricles and cortical subarachnoid spaces (SAS). The established coupling between the negative derivative of the global BOLD signal and fourth-ventricle CSF inflow was reproduced, with a peak lag of 0.9 s. Global BOLD fluctuations were also coupled with spatially distributed CSF velocity changes across ventricular and cortical CSF spaces, with a similar peak lag of 1.2 s. During visual checkerboard stimulation, BOLD-CSF velocity coupling was localized primarily to the SAS surrounding the activated visual cortex, demonstrating a regional relationship between local BOLD activity and nearby CSF motion. These findings establish the feasibility of simultaneous BOLD and slow CSF velocity imaging and extend BOLD-CSF coupling from a global measure toward spatially resolved assessment of hemodynamic-CSF interactions.

PurposeRecent improvements in MRI gradient design and amplifiers, advanced MRI scanners are now routinely utilizing slew rates of several hundred T/m/s. However, full gradient performance cannot be exploited in high-resolution EPI due to peripheral nerve stimulation (PNS) limits. We aim to characterize and mitigate these PNS constraints using a simple sequence modification: PNS-optimized EPI gradient pulse shapes. MethodsPNS-Optimized Pulses for EPI (POPE): we selectively reduce the slew rate of gradient pulses at periods of high predicted PNS spikes, while leaving the rest of the waveform unchanged. PNS sensation was evaluated. ResultsPOPE allows 7%-35% faster imaging of EPI protocols resolutions of 1mm-0.3mm resolutions without exceeding predicted PNS. With such improvements, POPE allows robust 0.3 mm isotropic fMRI protocols that would have exceeded safety limits without it. ConclusionPOPE facilitates locally precise fMRI activation mapping on clinical 7T scanners at spatial resolution that were previously unattainable due to PNS limitations.

Extracranial lipid contamination remains a challenge in proton magnetic resonance spectroscopic imaging (MRSI), especially in short acquisition delay MRSI, where broad lipid resonances overlap with metabolite and macromolecular signals. Although retrospective lipid suppression techniques are widely used in human MRSI, their effects on metabolite quantification in preclinical MRSI, which is more prone to lipid contamination, have not yet been examined. In this study, we assessed how the retrospective lipid suppression and spectral fitting range influence spectral quality, spatial metabolite mapping, and quantification variability using proton MRSI of rat brains at 14.1 T. Lipid suppression was applied via an orthogonal projection method to both fully sampled and compressed sensing datasets, each comprising data with minimal and pronounced lipid contamination. Spectral fitting was performed with both broad (4.1 - 0.2 ppm) and narrow (4.1 - 1.8 ppm) ranges. When lipid contamination was minimal, suppression caused only slight spectral and spatial changes, with consistent metabolite quantification across conditions. In contrast, datasets with pronounced lipid contamination exhibited notable spectral changes following suppression, consequently affecting spatial metabolite mapping and concentration estimates. Group analysis revealed that metabolites with low concentration estimates were most affected. Similar effects were observed in compressed sensing datasets. Our results provide a better understanding of the impact of retrospective lipid suppression on metabolite quantification in preclinical MRSI, thereby supporting future optimizations for its effective application.

The shift towards open science in preclinical research requires high-quality, comparable data adhering to FAIR principles, yet rigorous quality assurance (QA) and quality control (QC) frameworks remain less established in preclinical magnetic resonance imaging (MRI) than in clinical imaging. To address this limitation, a multicenter study was conducted across 21 international laboratories using standardized commercial liquid phantoms for mouse and rat MRI setups and a harmonized acquisition protocol. Data processing was centralized using AIDAqc, an automated pipeline extracting quantitative metrics including signal-to-noise ratio (SNR), temporal SNR (tSNR), ghost-to-signal ratio (GSR), and motion-equivalent temporal instability, combined with five complementary outlier-detection algorithms. The evaluated datasets encompassed magnetic field strengths from 3.0 to 16.4 T and heterogeneous coil and acquisition configurations. In mouse phantom data, SNR and tSNR were significantly lower at 3 T than at 7 and 9.4 T, whereas artifact-related metrics did not differ significantly between field-strength groups. In rat phantom data, differences between field strengths were less pronounced. At 7 T, substantial dataset-specific differences were observed in anatomical and functional quality metrics, also among datasets using the similar coil configuration. Multicenter reference values for frequently represented 7 T configurations were established, including SNR/tSNR of 48 {+/-} 3/48 {+/-} 1 dB for mouse surface-coil datasets and 49 {+/-} 3/44 {+/-} 4 dB for rat array-coil datasets. Longitudinal anatomical SNR showed generally low variability, whereas ghosting was more variable across time. Automated outlier detection additionally identified transient acquisition instabilities that were not always apparent by visual inspection. Together, these findings show that field strength and nominal coil configuration alone do not adequately characterize MRI performance and support standardized, longitudinal phantom-based QC combined with automated analysis as a practical approach for improving the reliability, transparency, and interoperability of multicenter preclinical MRI data.

Subject motion and gradient heating-induced frequency and phase offsets result in spectral misalignment during single-voxel MR spectroscopy (SVS) acquisitions. Several methods have been presented to align the spectra, but their performance on a 7T system is unclear. This study aimed to evaluate the practical importance of four retrospective correction methods, namely creatine fitting, residual water, spectral registration, and cross-correlation. SVS data were collected from 127 participants (88/39 female/male, 39{+/-}15 years) using a semi-localization by adiabatic selective refocusing sequence at 7T. Changes in the spectral linewidth, signal-to-noise ratio (SNR), similarity (mean similarity matrix (SImean)), and metabolite quantification (concentration, relative Cramer-Rao lower bounds (rCRLB)) after correction were evaluated. A Wilcoxon signed rank test was used to evaluate these changes. The p-values were adjusted using the false discovery rate (q<0.05) for multiple comparisons. For significant changes, the effect size was further calculated using the Rosenthal formula. The results demonstrated that all correction methods resulted in a significant decrease in the spectral linewidth and significant increases in the spectral SNR and SImean. The mean changes by using the four methods were 0.76-0.99 Hz for the spectral linewidth change (effect sizes:0.73-0.86), 5.79-6.95 for the SNR change (effect size:0.60-0.72), and 0.03-0.03 for the SImean change (effect size:0.87-0.87). The correction also significantly increased the estimates of the metabolite concentrations of PCr, Gln, Glu, GPC, Ins, NAA, Scyllo, and Tau (effect size:0.25-0.57), and significantly decreased the rCRLBs (effect size:0.26-0.42) of Scyllo, Tau, and Gly. These results indicate that each method provides practical benefits in improving the spectral linewidth, SNR, and similarity by showing a strong to very strong effect of the improvements (effect size>0.60). The correction only showed a weak to moderate effect of the changes in the metabolite quantification (effect size<0.60), suggesting that whether the correction results in more reliable metabolite quantification requires further validation.

Purpose: Proton density (PD) and macromolecular proton fraction (MPF) are fundamental MR parameters characterizing water and macromolecular content in tissues. Both parameters can be mapped using a time-efficient MRI protocol, though PD maps require additional corrections for the reception profile (RP) non-uniformity and global scaling. Existing PD mapping methods suffer from uncertainties related to PD normalization to reference tissue. This study aimed to develop a data-driven RP correction method for PD mapping based on MPF as an internal scale. Theory and Methods: One-parameter model linking PD and MPF was derived under assumption of proportionality between total macromolecular volume and proton concentration. This model with a uniform parameter value across brain tissues generates a surrogate PD map, which is used to estimate RP from an uncorrected PD map. The proposed algorithm was tested against a common PD mapping method utilizing bias-field correction and normalization to cerebrospinal fluid. Brain MRI data were acquired at 3T from seven healthy individuals. The protocol included 3D multi-echo T1-, PD-, and magnetization-transfer-weighted gradient-echo sequences. Parametric maps were reconstructed using the single-point MPF mapping algorithm with data-driven B1 correction. Results: MPF-based and bias-field corrections yielded similar PD measurements in white and gray matter with no significant bias and remarkably small variability (coefficients of variation <0.6%). Conclusions: MPF-based RP correction enables computationally simple and robust quantitative PD mapping at no additional cost if MPF maps are available. The main advantage of the proposed method is automatic elimination of global scaling that obviates the need for PD normalization.

Purpose: To evaluate, within a synthetic pulmonary-artery benchmark, whether biomarker-aware super-resolution improves hemodynamic endpoint fidelity in 4D flow MRI. Methods: A 3D residual channel attention network (RCAN) was trained on 22 CFD simulations from 11 pulmonary-artery geometries with 2x spatial degradation. The original primary comparison evaluated full biomarker-aware training against a combined divergence-temporal-regularization control across 14 matched seeds on a fixed six-case test set. Additional matched analyses subsequently separated divergence and adjacent-frame temporal-difference regularization and evaluated biomarker supervision in a controlled 2x2 design with a common reconstruction schedule. Results: In the original primary comparison, biomarker-aware training reduced PI error relative to the matched combined-regularization control (12.76 +/- 2.34% vs. 17.12 +/- 1.23%; paired difference -4.35 percentage points [95% CI -5.58, -3.09]; p = 0.0015). In the matched factorial analysis, biomarker supervision reduced PI error without (-2.235 percentage points [95% CI -3.514, -0.849]) and with (-4.125 percentage points [95% CI -5.392, -2.732]) combined regularization. PI improvement was not mirrored by uniformly improved reconstruction metrics: PSNR changed by -0.058 (p = 0.50) and +0.144 dB (p = 0.28), while SSIM decreased by 0.009 (p = 0.004) and 0.007 (p = 0.009), respectively. Divergence alone changed PI by +0.034 percentage points [95% CI -0.705, 0.809] while reducing DivRMS by 1.869 percentage points; temporal-difference regularization increased PI error by 1.562 percentage points [95% CI 0.594, 2.439]. Conclusion: Within this synthetic, fixed-case RCAN benchmark, biomarker supervision improved PI under both regularization conditions. PI improvement was not mirrored by uniformly improved reconstruction metrics. Temporal-difference - not divergence - regularization was the only isolated component associated with reproducible PI deterioration. These findings are configuration-specific and require geometry-independent and in vivo validation.

Purpose: Magnetic Resonance Acoustic Radiation Force Imaging (MR-ARFI) encodes focused ultrasound (FUS)-induced tissue displacements to MR phase shifts. Because these phase shifts are small, optimizing displacement sensitivity and minimizing motion artifacts requires characterization of the temporal dynamics of acoustic radiation force (ARF). Beyond sequence design, such characterization enables MR-ARFI to serve as a probe of brain viscoelastic behavior and can inform the design of therapeutic ultrasound pulsing schemes by quantifying how rapidly tissue deforms and relaxes. Methods: We developed a dynamic model assuming a gamma-variate impulse response for ARF(t), with time constant ({tau}) to be estimated. A single-shot spin echo ARFI acquisition with a bipolar motion encoding gradient (MEG) was implemented, incorporating retrospective corrections for head motion and physiological noise. A 500 kHz FUS pulse was applied in on/off blocks while systematically varying the timing between the MEG and sonication. The resulting phase time series were fit to the model to estimate time constant in five human participants. To assess sensitivity to tissue stiffness, {tau} was additionally measured in two customized gelatin phantoms of differing stiffness, characterized independently by MR elastography (MRE). Results: The average measured time constant in human participants was {tau} 3.7 {+/-} 0.27 ms. Comparing the two phantoms, the stiffer phantom exhibited a shorter {tau} (3.0-3.5 ms) than the softer phantom (4.0-4.5 ms), which tracked the MRE-derived Kelvin-Voigt viscoelastic time constants. In addition, the peak ARFI displacement was lower in the stiffer phantom (0.17 vs 0.74 m for the less stiff phantom), and a broader displacement focal spot size (FWHM 22 mm vs 15 mm). Conclusion: This time series approach with motion and physiological noise corrections validates an MR-based technique capable of characterizing the brain's temporal response to sonication-induced mechanical forces, providing a foundation for optimizing the design of ultrasound pulsing schemes for ARFI, as well as for future studies of brain biomechanics.

Background and ObjectiveSimulation of diffusion MRI signals from tissue microstructure is a fundamental problem in quantitative imaging, as it enables controlled study of how cellular architecture influences measured signals. However, physics-based simulations at clinically relevant scales are challenging due to a scale mismatch between imaging and histology: clinical diffusion MRI spans centimeter-scale fields of view with millimeter-scale voxels, whereas histology resolves structure at micrometer scales. Capturing voxel-wise signal formation therefore requires repeated simulations over heterogeneous microstructure, which becomes computationally and memory intensive in classical solvers. We propose a neural operator framework that amortizes this cost by learning local microstruc-ture-signal mappings once and applying them across large tissue regions. MethodsWe train a Fourier Neural Operator on finite-element simulations of histology-derived cell segmentations to predict magnetization fields from diffusivity and permeability maps. The model is embedded in a subdomain tiling strategy that enables scalable inference over whole-slide histology images. Unlike most conventional simulation pipelines, inference operates directly on regular grids derived from cell segmentations and does not require meshing. ResultsThe proposed framework enables simulation of apparent diffusion coefficient maps over 2D liver histology spanning 28.224 mm x 18.144 mm. It achieves over 2,600-fold acceleration compared with CPU-based finite-element simulation, reducing runtime from an estimated 217 days to under 2 hours. The network yields mean relative signal errors of 0.34%-0.43% at high diffusion weighting and 0.03% at low diffusion weighting, with maximum errors below 5%. On manually segmented datasets with greater morphological variability, mean errors increased slightly to 1.37%-1.79%. ConclusionsNeural operators enable computationally practical, mesh-free diffusion MRI simulation by amortizing expensive physics-based computation into a reusable operator applied across local sub-domains. This makes large-scale histology-based diffusion MRI modeling feasible while preserving high accuracy.

Purpose: Magnetic resonance spectroscopic imaging (MRSI) is a versatile technique to investigate the spatial distribution of in vivo metabolism. However, processing MRSI data is demanding, and only a few software packages support end-to-end analysis. The goal of this study was to implement fully automated, end-to-end MRSI analysis into the open-source 'Osprey-MRSI' software package. Methods: MRSI-specific analysis and visualization capabilities were implemented, building on the existing Osprey workflow. Modifications included spatial transformation and filtering operations, automated brain masking and tissue segmentation of the MRSI data, improved lipid filtering, rapid integral maps, linear-combination modeling with explicit B0 frequency-shift correction, and generation of quality-control maps and metabolic images. A fully interactive GUI and semi-interactive HTML reports provide a user-friendly way to inspect each step of the analysis. All analysis derivatives are also exported in NIfTI and NIfTI-MRS format for easy visualization and synergies with other toolboxes and modalities. Results: The automated MRSI workflow was successfully used to analyze short- and medium-TE 3T in vivo MRSI datasets from all major vendors (Philips, GE, Siemens) across multiple sites. Correct coregistration of MRSI data and MR images was validated using phantom data from each vendor and existing MRSI processing tools. Conclusion: Osprey-MRSI offers state-of-the-art methods with minimal user interaction available for non-expert users. The modularity of the workflow and the modeling algorithm will foster innovation and development of novel MRSI-specific analysis methods.

Purpose: To quantify, in a computational fluid dynamics (CFD) benchmark, how spatial degradation, temporal acquisition and noise change the error of plane-based pulmonary-artery flow endpoints. Methods: Twenty-two historical, non-converged CFD simulations (11 geometry families) were voxelised at 2 mm with 80 frames per cycle. Simulations with numerical divergence were excluded, leaving 13 (7 families). Fields were degraded to 4 and 8 mm and with noise, plane waveforms were sampled at 20 phases with one-interval averaging, and errors were computed against the noise-free reference waveform on 66 geometric planes. Family-mean paired differences are reported with t-based 95% confidence intervals under an analysis plan fixed in advance. Results: Spatial degradation from 2 to 4 mm increased full-waveform nRMSE by 0.0032 [0.0021, 0.0043], in 7 of 7 families; cycle-mean flow bias accounted for 23% of the squared error, against 60% at 8 mm. With SNR scaled to voxel volume, the increase was not resolved (0.0002 [-0.0012, 0.0015]). Twenty-phase acquisition increased PI error (0.0223 [0.0214, 0.0233]) and diastolic-trough timing error (0.63 [0.52, 0.74] frames) more than spatial degradation, and its point estimates for these endpoints were larger with every recovery method; the nRMSE ordering and peak timing depended on the recovery method. The implementation of the downsampling operator mattered: an alternative, corner-aligned implementation of the same 4 mm degradation gave a larger spatial effect (nRMSE difference 0.0010 [0.0002, 0.0017]). Conclusion: In this benchmark, a 2 to 4 mm degradation produced a small, consistent, mostly shape-related nRMSE increase that depended on the noise model, whereas 20-phase acquisition changed PI error and trough timing more. The results describe benchmark behaviour, not clinical acquisition requirements.