Magnetic Resonance Imaging

Purpose: to introduce a novel magnetic resonance imaging (MRI) technique, MUSLIS MRI (muscle and lipid suppression), that generates two co-registered 3D sets of images in a single acquisition: one set with lipid signal suppression (MUSLIS1) and a second set with muscle signal suppression (MUSLIS2). Materials and Methods: Numerical simulations based on Bloch equations were performed to optimize the sequence parameters; the MP2RAGE sequence was employed for the implementation of MUSLIS imaging. MRI experiments were performed in vivo at 3 T. Quantitative measurements of signal contrast were carried out on MUSLIS1 and MUSLIS2. Various combination images and T1 parametric maps of the thighs were generated from the two co-registered 3D sets. Results and Discussion: Optimal sequence parameters to achieve lipid suppression in one dataset (MUSLIS1) and muscle suppression in the other dataset (MUSLIS2) were determined by numerical simulations. MUSLIS imaging was validated in vivo at 3 T: MUSLIS1 images and MUSLIS2 images displayed excellent lipid and muscle suppression, respectively. The values of the signal contrast between subcutaneous lipids and muscle, as well as between bone marrow lipids and muscle reached values close to 0.8. Thus, MUSLIS would allow for tissue characterization of lesion/masses and direct visualization of the adipose tissue. Combination images, generated from these two intrinsically co-registered sets of images (MUSLIS1 and MUSLIS2), displayed different contrasts and improved signal characteristics, such as mitigation of signal inhomogeneities. The development of MUSLIS imaging for musculoskeletal imaging followed the concept introduced with the FLAWS, an MRI sequence designed for brain MR imaging to generate two 3D sets of images, one with white matter suppression and the other with cerebrospinal fluid suppression. Similarly to FLAWS, which provides T1 maps of the brain, MUSLIS imaging yields T1 parametric maps of the musculoskeletal system. Furthermore, the unique characteristics of the combination images make MUSLIS imaging a potentially attractive technique at field strengths higher than 3 T. Conclusion: The proof-of-concept of a novel approach to musculoskeletal imaging, MUSLIS MRI, is proposed here and demonstrated by numerical simulations and MRI experiments performed in vivo at 3 T. Keywords: MRI, muscle, lipids, musculoskeletal imaging, high field MRI, fat suppression, FLAWS MRI, MP2RAGE, MUSLIS, inversion recovery, adipose tissue, bone marrow, subcutaneous fat, T1 mapping, 3 T, 7 T, neuromuscular disorders, metabolic diseases, muscular dystrophy.

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.

PurposeWith growing interest in pediatric MRS to understand the neurochemistry of brain development, it is essential to establish pediatric-specific metabolite T1 and T2 relaxation times for reliable quantification of metabolites. Furthermore, characterization of age-related changes in relaxation times may provide insight into developmental differences in tissue microstructure and cellular composition. Therefore, the goals of this study were two-fold: (1) to estimate metabolite T1 and T2 relaxation times in a pediatric cohort; and (2) to assess linear relationships between age and relaxation times. MethodsTI- and TE-series data were acquired from the bilateral thalamus in 20 and 25 participants aged between 0 and 7 years, respectively. Data were processed and modelled in Osprey 2.5.0. Metabolite T1 and T2 relaxation times of total N-acetylaspartate at 2.0 ppm (tNAA2.0), total creatine at 3.0 ppm (tCr3.0), total creatine at 3.9 ppm (tCr3.9), total choline (tCho), myo-inositol (mI) and the combined glutamate and glutamine (Glx) were estimated by fitting the modelled signal amplitudes to TI-series inversion recovery signal and TE-series exponential decay equations, respectively. Linear relationships with age were tested for metabolite T1 and T2 relaxation times using Spearman correlations. ResultstNAA2.0 and Glx showed significant changes in T1 with age. tNAA2.0, tCr3.0, tCho and mI showed significant decrease in T2 with age. ConclusionSeveral metabolites showed significant changes in relaxation times with age in the thalamus region. Therefore, use of age-specific relaxation correction for pediatric studies using the linear equations provided is encouraged for reliable quantification of metabolites.

Tensor-valued diffusion encoding (TDE) is an emerging diffusion MRI technique that uses advanced diffusion-encoding waveforms and modeling to provide enhanced specificity to brain microstructure compared with conventional Stejskal-Tanner pulsed-gradient encoding. However, the diffusion encoding duration to achieve the same b-value is substantially increased in TDE, resulting in relatively low SNR and spatial resolution (>2 mm isotropic). In this study, we developed an SNR-efficient, high-resolution TDE protocol with 1.8-mm isotropic resolution and clinically feasible scan time and evaluated its reproducibility and sensitivity to age-related microstructural differences. Eleven cognitively normal older adults (5F/6M, 62-71 years) and seven younger adults (3F/4M, 22-31 years) were scanned at 3T using an in-house TDE sequence incorporating an SNR-efficient multi-band multi-shot EPI readout and reconstruction. Quantitative diffusion metrics, including mean diffusivity (MD), fractional anisotropy (FA), microscopic FA (FA), anisotropic mean kurtosis (MKA), isotropic mean kurtosis (MKI), and total mean kurtosis (MKT), were evaluated in the bilateral temporal parts of the cingulum bundles (CBT), bilateral fornix (FX), and global white matter (WM). Older participants underwent two scans to assess reproducibility using intraclass correlation coefficients (ICCs) and Bland-Altman analysis, and diffusion metrics were compared between younger and older groups using Wilcoxon signed-rank tests with false discovery rate (FDR) correction. All metrics except MKI demonstrate moderate-to-excellent reproducibility across all WM regions (ICCs[&ge;]0.64). Exploratory comparisons show age-related differences in FA, FA, and MKA with p<0.05 before FDR correction, particularly in the fornix, although none remains significant after correction. These findings demonstrate the feasibility of SNR-efficient TDE at 1.8-mm isotropic resolution with approximately 70-mm brain coverage in approximately 12 minutes and establish its reproducibility for high-resolution brain microstructural imaging.

1.IntroductionUnderstanding mid-term test-retest reliability and within-subject variability is important for interpreting changes observed in longitudinal and intervention studies. The reliability of resting-state functional magnetic resonance imaging (rs-fMRI) is known to vary across measures and brain regions. However, how reliability differs across functional networks and connectivity-and amplitude-based measures, and whether multi-echo acquisition and processing modify these patterns, remain incompletely characterized. MethodsTwenty-two healthy volunteers underwent two rs-fMRI sessions 15.7 {+/-} 4.0 days apart on a Compact 3T scanner. Multi-echo, middle-echo, and independently acquired single-echo datasets were compared, with multi-echo independent component analysis additionally evaluated as a denoising approach. Functional connectivity (FC) and three amplitude-based measures were evaluated using the Schaefer 400 parcellation. Reliability was systematically assessed using intraclass correlation coefficient (ICC), within-subject standard deviation (wSD), and systematic bias at edge or regional, and network levels. ResultsAcquisition-dependent differences in reliability were generally modest. Multi-echo acquisition and processing increased functional connectivity strength and the magnitude of amplitude-based measures and improved inferior cortical coverage, but these enhancements did not consistently translate into substantially higher ICC or lower wSD. In contrast, reliability showed clear network-dependent differences. FC reliability varied markedly across network pairs and was not explained by connectivity strength alone; pairs involving the default mode and control networks generally showed more favorable profiles than several somatomotor and visual network pairs. Fractional amplitude of low-frequency fluctuations (fALFF) also showed network-dependent reliability, with the most favorable regional reproducibility observed in the default mode and control networks and lower reproducibility in the somatomotor and visual networks. ConclusionThese findings provide practical mid-term reliability benchmarks for rs-fMRI on a Compact 3T scanner and show that measurement stability varies more clearly across measures and functional networks than across acquisition approaches. Key pointsO_LIMid-term test-retest reliability varied more clearly across resting-state measures and functional networks than across acquisition and processing approaches. C_LIO_LIMulti-echo acquisition and processing enhanced functional connectivity strength, amplitude-based signal magnitude, and inferior cortical coverage but did not consistently improve reliability. C_LIO_LIFunctional connectivity strength and fractional amplitude of low-frequency fluctuations showed distinct network-specific reliability profiles, with more favorable reproducibility in default mode and control networks than in several somatomotor and visual networks. C_LI

Background: Automated brain volumetry is increasingly used for clinical and research assessment, but its outputs depend on acquisition and reconstruction settings. We evaluated how parallel-imaging acceleration and vendor deep-learning reconstruction (Siemens Deep Resolve) affect automated volumetry on T2-weighted turbo spin-echo MRI, and whether deep-learning reconstruction stabilises measurements across protocols. Methods: Four healthy volunteers each underwent 14 T2-TSE-TRA acquisitions on a 1.5 T scanner, covering seven protocols (baseline, GRAPPA R = 2, 3, 4, and SMS x2,x3, x4), with each protocol acquired separately once with Deep Resolve off and once with Deep Resolve on, for 56 acquisitions in total. Six pipelines were applied: SynthSeg, OpenMAP-T2, and GOUHFI 2.0 for subcortical parcellation, and TotalSegmentator MRI, HD-BET, and NV-Segment-CTMR for whole-brain masking. A three-rater STAPLE consensus of six subcortical structures on the baseline acquisition served as the expert reference. Reference-relative accuracy (absolute percentage error), cross-protocol dispersion (coefficient of variation), and spatial reproducibility (Dice coefficient and 95th-percentile surface distance) were compared between reconstruction states. Results: Deep Resolve improved reference-relative accuracy most for GOUHFI 2.0 (pooled median absolute percentage error 28.4% to 21.9%), modestly for SynthSeg (16.2% to 15.5%), and had a mixed, structure-dependent effect for OpenMAP-T2. It approximately halved cross-protocol dispersion for all three parcellation tools (coefficient of variation 2.62% to 1.39% for SynthSeg, 4.42% to 2.14% for OpenMAP-T2, and 21.75% to 7.87% for GOUHFI) and improved spatial reproducibility in parallel. It eliminated the systematic GOUHFI under-segmentation seen at high acceleration under conventional reconstruction (median deviation -31.6% at GRAPPA R = 4; 14 failure events under conventional reconstruction, none with Deep Resolve). Whole-brain masking was robust in both states. Conclusions: Deep Resolve improved the robustness of automated brain segmentation in accelerated T2-weighted MRI, reducing cross-protocol variability and improving spatial reproducibility, with the largest reduction in reference-relative error in the most acceleration-sensitive pipeline. The elimination of severe segmentation failures observed under conventional reconstruction highlights its potential to support reliable volumetry at higher acceleration. Reconstruction optimization therefore offers a practical route to more consistent quantitative measurements across acquisition protocols.

BackgroundSingle-voxel magnetic resonance spectroscopy (MRS) is a non-invasive method for measuring clinically and cognitively relevant metabolites. Reliable measurements require precise voxel placement across sessions and participants. We developed a scanner-console-based approach to improve voxel placement precision. MethodsIn a crossover design (n=7; six sessions each), we compared test-retest reliability of three voxel placement methods in a reference benchmark (left parietal cortex) and a technically challenging region (left ventromedial prefrontal cortex). Methods included (1) conventional anatomy-based placement, (2) mask-guided real-time positioning (MGRP), and (3) semiautomated session-locked voxel repositioning (SSVR). Resting-state MRS data were acquired using PRESS and MEGA-PRESS. Within-subject reliability of voxel placement and metabolite concentrations, namely, total N-acetylaspartate (tNAA), total Creatine (tCr), GABA (gamma-aminobutyric acid), and Glx (glutamate + glutamine) are reported using the coefficient of variation (CV), the intraclass correlation coefficient (ICC), minimal detectable change (MDC), and the spatial overlap. ResultsSSVR markedly improved voxel placement reliability, increasing spatial overlap (up to 88%) and achieving near-perfect geometric reproducibility (ICC = 0.99) compared to conventional anatomy-based placement and MGRP. SSVR improved tissue composition consistency and reduced metabolite variability in the technically challenging region (variability reduction of [~]70% tCr, [~]59% tNAA, and [~]51% Glx) while further refining already stable measurements in the benchmark region (tNAA from [~]15% to [~]10%). ConclusionBoth MGRP and SSVR improved voxel placement and metabolite measurement reproducibility compared with conventional anatomy-based placement. SSVR further enhanced within-subject reproducibility across repeated sessions, particularly in the technically challenging region, providing a robust approach for longitudinal single-voxel MRS studies.

Multisite functional Magnetic Resonance Imaging (fMRI) studies are rapidly becoming the norm to allow the acquisition of large samples required to perform advanced statistical techniques, e.g. machine learning. However, acquiring data at multiple sites includes methodological and technical challenges due to the difference in the facilities of each site, such as scanner manufacturer and model. These new challenges add to the already well-known challenges of fMRI data, including scanner-related noise, participant movement, and physiological confounds. To ensure the optimal quality of data, a balance needs to be achieved between standardizing data preprocessing across sites and optimizing within-site data preprocessing. To define the optimal preprocessing pipeline including standard steps and additional denoising technique for our multisite dataset, we first tested 3 commonly used preprocessing pipelines, i.e. fMRIPrep, FSL, and CONN. These pipelines all include standard steps, e.g. motion correction, temporal filter, etc. In a second step, to further improve signal quality, we tested the efficiency of 3 additional denoising techniques on the output of the data preprocessed with the previously defined pipeline. These techniques included aCompCor, FSL FIX and ICA-AROMA. Signal quality was quantified as temporal signal-to-noise ratio (tSNR) across and within sites. Our results show that the performance of the preprocessing pipeline and denoising technique varies between sites. FSL yielded the highest tSNR at two sites and fMRIPrep at the third, revealing a significant site x pipeline interaction. An FSL-based pipeline with minimal adjustment to accommodate site-specific parameters and followed by denoising using a single FSL FIX classifier, which was custom-trained across sites, achieved the highest quality of signal in our data and yielded the greatest consistency in improved data quality across sites. Because of the potential of this pipeline to adequately identify and address site-specific noise, while remaining constant across sites, we selected it as optimal preprocessing pipeline for our dataset.

Abstract Introduction: Myelin water imaging provides a non-invasive approach for indirectly investigating myelin content of the brain by magnetic resonance Imaging (MRI). Myelin water fraction (MWF) reflects the fraction of water signal associated with water trapped between myelin layers. Postmortem imaging provides a unique opportunity to validate MWF metrics against gold-standard postmortem neuropathology. However, there is a need for validated sequences that would be applicable in postmortem settings. In this study, we validated and compared the applicability of turbo spin echo (TSE) and gradient-and-spin-echo (GRASE) techniques for myelin water imaging in formaldehyde fixed postmortem human brains. Methods: 40 postmortem human brain hemispheres were scanned with TSE and GRASE sequences. Acquired data were reconstructed using a non-parametric multicomponent T2 relaxometry approach using a consistent framework to support comparison between GRASE- and TSE-derived measures. Agreement between derived measures was quantified at both voxel and regional level. Maps derived from the full 32-echo GRASE reconstruction were compared against a truncated 14-echo reconstruction to examine the contribution of the longer echoes to the derived maps. Finally, a random forest regression model was trained based on TSE data to predict the GRASE-derived MWF maps. Results: Both sequences provided robust multicomponent T2 characterization across the brain, and the derived measures showed anatomical patterns consistent with expected differences across tissue types. TSE and GRASE derived maps had moderate to strong agreement at voxel (0.41< {rho} < 0.82, all pFDR<0.001) and regional levels (0.87< {rho} < 0.98, all pFDR<0.001), with consistently stronger agreement observed for regional metrics. Echo reduction comparisons between the full 32-echo GRASE reconstruction and a truncated 14-echo reconstruction showed minimal impact of echo truncation (all {rho} > 0.98, pFDR < 0.001). The nonlinear random forest regression model trained on TSE maps was able to accurately reproduce the MWF maps derived from GRASE (r = 0.91, RMSE = 8.87). Discussion: TSE and GRASE32 capture strongly related multicomponent T2 information in fixed postmortem human brain tissue, but with systematic and tissue-dependent quantitative differences across derived measures. These differences suggest that direct interchangeability should not be assumed and that cross-sequence mapping or calibration is required when quantitative equivalence is desired.

Transparent assessment of diffusion magnetic resonance imaging (dMRI) techniques with empirical verification of confounding factors requires adequately designed protocols and collected datasets. Publicly available diffusion-weighted MR datasets often provide limited sampling across b-values, making it difficult to study optimal acquisition protocols or the relationships between different processes occurring in brain tissue. In this work, we introduce a new densely sampled longitudinal test-retest diffusion-weighted MR dataset of the brain. Our dataset was collected from eleven healthy volunteers, each scanned four times: two sessions on consecutive days, which form the test data, followed by two additional sessions completed one week later (retest data). The data were acquired using twenty-two b-values ranging from 10 to 3000 s/mm2, along with structural T1-weighted scans. Potential applications of the dataset include, but are not limited to, assessing longitudinal reproducibility and reliability of quantitative metrics, evaluating robust and outlier-resistant estimation techniques, investigating experimental factors affecting estimation procedures, and verifying optimal acquisition protocols for different signal models. The dataset is publicly available in raw and fully preprocessed variants.

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.

Objectives: To evaluate the impact of an accelerated whole-body diffusion-weighted MRI (WB-DWI) protocol using deep learning (DL) reconstruction on image quality and apparent diffusion coefficient (ADC) quantification in patients with metastatic prostate cancer. Methods: This single-centre prospective study involved two patient cohorts undergoing standard-of-care WB-MRI at 1.5T. The DL WB-DWI acquisition used fewer signal averages and higher parallel imaging acceleration, reconstructed with a research application DL package based on a variational network. In Cohort 1 (n = 10), qualitative assessment of image quality was performed across four anatomical stations using a 4-point Likert scale by two radiologists, blinded to the DWI protocol. In Cohort 2 (n = 20), ADC values were evaluated in hypercellular focal bone metastases (as determined by a radiologist) and compared between the standard and accelerated DL WB-DWI protocols. Image quality scores were analysed using Wilcoxon signed-rank tests (Bonferroni-corrected), and ADC agreement was evaluated via Bland-Altman analysis. Results: Accelerated DL WB-DWI reduced WB-DWI acquisition time by 37% without compromising image quality. Radiologists consistently rated both protocols as good-to-excellent across all image quality metrics. Median ADC estimates in lesions showed no significant difference between protocols; interquartile range of ADC estimates also showed no significant difference. Conclusions: Accelerated DL WB-DWI enables 37% scan time reduction without degrading image quality or affecting ADC estimates of lesions in patients with metastatic prostate cancer. Advances in knowledge: DL-based reconstruction enables faster WB-DWI acquisition without compromising image quality or ADC quantification, supporting clinical implementation in metastatic prostate cancer staging and therapy assessment.

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.

Objectives: This study aimed to identify reproducible and repeatable radiomics features (RF) of gadoxetic acid-enhanced MRI that correlate with liver function. Methods: This study included 10 patients with colorectal liver metastasis who received a right hepatectomy and were prospectively recruited. Before surgery, all patients underwent indocyanine green (ICG) retention test, hepatobiliary scintigraphy (HBS) exam and gadoxetic acid-enhanced MRI. RF were extracted from the non-tumoral liver parenchyma at the MRI images before contrast injection and the hepatobiliary phase. Delta-RF were calculated by RFs of hepatobiliary phase subtract those before contrast injection. ICG-R15, HBS results, and kinetic growth rate (KGR) of the future liver remnant volume were used as quantitative indices of liver function. Reproducible and repeatable RF were identified via inter- and intraclass correlation analysis with a coefficient threshold of 0.80. Correlations between RF/delta-RF and the three liver function indices (ICG-R15, HBS and KGR) were evaluated by Spearman correlation analysis. Results: One hundred and seven RFs were extracted from each MRI phase (214 in total and 107 derived delta RFs). With inter- and intraclass correlation analysis, 60 reproducible and repeatable RFs were identified. After removing RFs with higher correlation coefficients, 12 RF from images before contrast injection, 23 RF from hepatobiliary phase, 29 delta-RF were identified significantly correlated with the liver function metrics (ICG-R15, HBS and KGR). Conclusions: A set of reproducible and repeatable radiomics features of gadoxetic acidenhanced MRI which were associated with liver function were identified. These features can be adopted for future liver function-related radiomics research.

Purpose: Post-mortem Diffusion Magnetic Resonance Imaging (dMRI) findings on fixation-related changes in diffusion tensor imaging (DTI) parameters remain inconsistent, partly due to lower diffusion weighting and sparse early-fixation sampling. This study investigated how post-mortem tissue condition, fixation progression, and diffusion weighting shape DTI parameters in whole human brains, including whether an ex-vivo-adjusted protocol also suits in-situ and early fixation measurements. Methods: Six neurologically healthy whole human brains (post-mortem interval (PMI) 12 - 24 h) were scanned across tissue conditions, including an in-vivo reference cohort. Five brains were scanned longitudinally through immersion fixation (0 - 150 days), yielding 175 datasets with dense early sampling. Diffusion measurements at b-values 1000 - 4000 s/mm2 assessed the influence of diffusion weighting on DTI parameters. Analyses covered multiple white- and gray-matter regions and within white matter stratification by fiber orientation dispersion ({kappa}). Results: The largest mean diffusivity (MD) shift occurred between in-vivo and in-situ, whereas fractional anisotropy (FA) changed most strongly during early fixation. During immersion fixation, MD showed reproducible monoexponential saturation, while FA was heterogeneous across brains. PMI, {kappa}, and regional anatomy explained substantial FA heterogeneity across specimens. Qualitatively, the fixationrelated changes matched across diffusion weightings, but precision across specimens was highest at b-value 4000 s/mm2. Conclusion: During fixation, MD is a robust temporal marker, whereas FA requires microstructureaware interpretation. Diffusion weighting modulates how clearly fixation effects can be resolved, but the same qualitative fixation dynamics remained detectable at in-vivo-like weighting with reduced precision.

There is increasing demand for quantitative measurements in neuroimaging research, including absolute quantification of regional myelin density. Fast whole-brain macromolecular proton fraction (MPF) imaging enables acquisition and reconstruction of quantitative maps of MPF, a biomarker of myelin, and has been used in many research applications. To date, no standard protocols have been published for parcellating MPF maps. FreeSurfer is a widely used software package for automated brain parcellation. It is designed for use with high-resolution T1-weighted anatomical images, typically acquired with an MPRAGE sequence. MPF maps inherently provide high contrast between white and gray matter and can potentially be used as input images for parcellation, enabling simultaneous quantitative tissue characterization and volumetric assessment. However, it is unclear how the use of MPF maps as input images with FreeSurfer software affect reliability and repeatability of regional MPF and volume estimates. We evaluated three FreeSurfer parcellation workflows for this purpose. The workflows differed in the amount of neuroimaging data required and computational intensity. MPF and MPRAGE data were acquired in two separate sessions for 11 adults. Reliability was evaluated using mean relative differences in estimates, Bland-Altman analysis, and intra-class correlation coefficients (ICCs). Repeatability was assessed using within-subject coefficients of variation (CVws). All three workflows produced similarly high within-subject repeatability across scans. However, reliability in regional MPF estimates was lower in gray matter than in white matter for workflows that used MPF maps as input. Workflows that use MPF maps as FreeSurfer input may be adequate for estimating mean MPF in GM and WM of cortical parcels and in subcortical regions. However, the MPRAGE-based workflow is recommended when reliable individual-level estimates or volume estimates are required.

Objective: To identify common photon-counting CT (PCCT) virtual monochromatic imaging (VMI) settings for accurate hepatic fat fraction (FF) quantification across different body sizes, including large body habitus. Methods: Six non-iodinated fat lesions (FF 5%-40%) were embedded in anthropomorphic liver phantoms representing medium-sized (25x32.5 cm^2) and large (31x39 cm^2) abdomens. Phantoms were scanned on a PCCT system (NAEOTOM Alpha) at 120 and 140 kV. CT numbers were measured in VMIs at 40-190 keV in 1-keV increments. Linear regression between the ground-truth FF and measured Hounsfield units (HU) was used to estimate FF. Common optimal VMI settings yielding the minimum relative root-mean-square error (rRMSE) in both phantoms were identified. Results: A single VMI setting of 70 keV at 140 kV demonstrated the best overall performance across body sizes, with FF (%) = -0.689HU + 36.51 (R^2 > 0.996), achieving rRMSE [&le;]3.4% and absolute RMSE [&le;]0.7% in both phantoms. Robust performance (rRMSE [&le;] 5%) was consistently maintained across 69-71 keV using identical calibration parameters for both phantoms. These results represented a substantial improvement over previously reported dual-energy CT (DECT) performance, while enabling accurate quantification on PCCT at radiation doses approximately 40% lower than those used in prior DECT protocols. Conclusion: PCCT enables accurate and robust hepatic fat fraction quantification independent of body size. A single protocol at 140 kV with VMIs of 69-71 keV consistently achieved low quantification errors, demonstrating strong potential for opportunistic liver fat assessment using PCCT, especially in obese patients.

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

IntroductionWhile placental perfusion and pathology jointly affect pregnancy outcomes, cotyledon-specific perfusion across gestation and its correlation with local injury is not yet well understood. Ferumoxytol dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) offers a promising way to noninvasively identify cotyledons across gestation and quantify longitudinal cotyledon-specific perfusion changes. Additionally, intraplacental injection of bioactive fibrin sealant allows us to model thrombotic placental injury and further assess cotyledon-level relationships between perfusion and significant injury. MethodsPregnant rhesus macaques (N=13) received intrauterine saline or fibrin sealant injections at gestational day (GD) [~]101 and underwent ferumoxytol DCE-MRI at GDs [~]100, 115, and 145. Placental perfusion domains derived from contrast arrival time were segmented at each imaging time point and matched to cotyledons identified following tissue collection by cesarean section, with cotyledon perfusion quantified longitudinally and correlated with cotyledon-specific quantitative histopathology. ResultsAll pregnancies were successfully carried to term. Fibrin sealant injections induced significantly higher levels of placental pathology compared to saline controls. MRI-derived perfusion domains were largely consistent across gestation and showed predominantly one-to-one correspondence with term cotyledons, with successful perfusion-pathology pairing achieved in 153 cotyledons. Longitudinal cotyledon perfusion changes showed significant positive correlations with villous agglutination injuries. ConclusionsFeasibility of noninvasively tracking placental cotyledon perfusion using ferumoxytol DCE-MRI was demonstrated, and the efficacy of the rhesus macaque thrombotic injury model was confirmed. The positive perfusion-pathology correlations suggested intrinsic placental regulatory mechanisms and functional plasticity. This new framework is promising for future translational studies and validation of ex vivo cotyledon perfusion models. HighlightsO_LILongitudinal tracking of placental perfusion domains with ferumoxytol MRI C_LIO_LISuccessful matching of cotyledons and MRI-derived perfusion domains C_LIO_LIConfirmed thrombotic injury-model induced cotyledon pathology C_LIO_LIMaternal perfusion compensation in presence of villous pathology C_LI

Multiple sclerosis (MS) white matter lesion (WML) automated segmentation on ultra-high-field 7T MRI remains challenging due to the domain shift from lower-field acquisitions, limited annotated data, and specific imaging artifacts. Fine-tuning is an effective and practical strategy for adapting deep learning WML segmentation algorithms to 7T MRI, even with limited annotated data. This study evaluates fine-tuning as a domain adaptation strategy to leverage a pre-trained deep learning model for automated WML segmentation on 7T MRI. We fine-tuned a U-Net-based model, originally trained on approximately 35,000 heterogeneous lower-field (1T, 1.5T, 3T) multi-contrast MRI scans of people with MS for T2-hyperintense WML (T2-WML) segmentation, using a 7T dataset. Multiple approaches were evaluated, including standard fine-tuning on 3D FLAIR images, low-rank adaptation (LoRA) and training from scratch (nnU-Net). Models were evaluated on an external multi-center 7T test dataset. Additionally, a separate model was fine-tuned for T1-hypointense WML (T1-WML) segmentation on 7T MP2RAGE images. The original model showed substantial performance degradation on 7T data compared to 3T (Dice score decreased from 0.69 to 0.31), confirming the need for domain adaptation. Fine-tuning markedly improved T2-WML segmentation, with the fine-tuned model achieving a median Dice score of 0.57. Lesion-wise sensitivity and F1-score were 0.78 and 0.75, respectively, and the lesion volume agreement with manual segmentation was 0.93. For T1-hypointense WML, fine-tuning showed lower lesion detection performance compared to the T2-hyperintense WML segmentation (sensitivity and F1 of 0.58 and 0.50, respectively); however, incorporating multi-center data into the training set substantially reduced false positives by 23% and improved lesion detection by 14%. Multi-center fine-tuning further improved performance, particularly for the more challenging task of T1-WML segmentation. The models presented here may be included in MS research workflows to facilitate multi-center collaborations with 7T MRI.