Magnetic Resonance in Medicine

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.

Objective. Quantitative T2 mapping plays a critical role in brain imaging for assessing a range of neurological conditions, including neurodegenerative diseases, demyelinating disorders, and cerebrovascular pathologies. Despite its diagnostic potential, implementing quantitative T2 mapping at ultra-high magnetic field strengths ([≥]7T) poses significant challenges. These include elevated specific absorption rate (SAR) and radiofrequency (RF) field inhomogeneities, which can lead to prolonged scan durations and inaccuracies in quantification. Materials and Methods. Phase-based gradient-recalled echo (GRE) techniques have recently emerged as promising rapid acquisition with enhanced sensitivity to T2-related contrast. In this study, we introduce TWISTARE (TWo Interleaved Steady-states for T2 and RF Estimation), a novel dual steady-state 3D-GRE approach that employs interleaved flip angles and small RF phase increments to jointly estimate T2 and B1 maps. By combining two dual-steady-state scans, TWISTARE enables fast, whole-brain quantitative T2 mapping while reducing scan time and mitigating B1-related bias at ultra-high field. Results. Validation experiments included Bloch simulations, phantom studies and in-vivo imaging. The results demonstrated high precision in phantom experiments, achieving up to a two-fold reduction in acquisition time and achieved precision comparable to the gold-standard method in vivo within a similar scan duration. Discussion. TWISTARE establishes a fast steady-state framework for quantitative neuroimaging at ultrahigh field, offering potential benefits for both clinical and research applications, especially in longitudinal and dynamic studies of brain tissue.

Background: Brain magnetic resonance elastography (MRE) is an emerging quantitative neuroimaging technique that provides noninvasive maps of brain tissue viscoelasticity. For multi-center applications, robust cross-site reproducibility across scanner platforms is essential but remains insufficiently characterized. Purpose: To evaluate cross-site reproducibility of brain multifrequency MRE measurements between two MRI scanner platforms using harmonized protocols. Study Type: Prospective cross-site test-retest reproducibility study. Study Population: Sixteen healthy adult volunteers (7 men, 9 women; mean age 32.2 +/- 8.0 years). Field Strength/Sequence: 3 T systems (Siemens MAGNETOM Cima.X and MAGNETOM Vida at two sites) with identical brain multifrequency MRE sequences, echo-planar imaging (EPI) readout, and standardized driver configuration. Assessment: Each participant underwent one MRE acquisition at each site. Shear wave speed (SWS) and penetration rate (PR) were quantified in whole brain, white matter, subcortical gray matter, and cortical gray matter regions using atlas-based region-of-interest (ROI) analysis in MNI152 space. Statistical Tests: Absolute relative difference (ARD), reproducibility coefficient (RDC), coefficient of variation (CV), intraclass correlation coefficient (ICC), and Bland-Altman plots were calculated to determine cross-site reproducibility. Results: Cross-site reproducibility was robust for major brain regions, with region-averaged ARD values for SWS ranging from 1.38 % to 3.43 % and for PR from 3.20 % to 7.25 % across tissues. RDCs for SWS ranged from 0.02 m.s^-1 to 0.07 m.s^-1 , and for PR from 0.03 m.s^-1 to 0.08 m.s^-1. Coefficients of variation for SWS ranged from 0.82 % to 1.93 %, and for PR from 2.21 % to 4.09 %. ICC values for SWS ranged from 0.66 to 0.84 and for PR from 0.67 to 0.88. Bland-Altman analysis showed minimal systematic bias and tight limits of agreement. Conclusion: Brain multifrequency MRE demonstrates robust reproducibility across distinct 3 T platforms when using harmonized acquisition and reconstruction. These results support the use of brain MRE as a quantitative biomarker and provide benchmark reproducibility metrics for future research.

Quantitative microstructural magnetic resonance imaging (MRI) can noninvasively characterize tissue configuration at micrometer scales, but clinical uptake is limited by validation and optimization in human-relevant scenarios. Organoids are powerful human-relevant tissue models, yet translation is hampered by lack of non-destructive, longitudinal microstructural assessment. Bridging these gaps, microstructural MRI of living organoids can accelerate MRI biomarker and organoid development and validation. Here, we address key obstacles to enable organoid microstructural MRI. First, we use a unique 28.2 T MRI system to achieve spatial resolution with adequate signal-to-noise ratio and feasible scan times. Second, we implement flexible acquisitions with fast readouts to expand multivariate experimental capacity. Third, we develop a workflow combining 3D MRI and 3D lightsheet microscopy for cross-modality anatomical comparison beyond 2D. Using this platform, we demonstrate microstructural MRI of cortical organoids with resolutions down to (20 {micro}m)3, revealing anisotropy, heterogeneity, maturation-dependent differences, and temporal changes in cortical organoids. Correlative lightsheet microscopy confirms correspondence to axonal and nuclear architecture. This platform enables live-organoid MRI as a complementary tool to human- and animal imaging for robust microstructural assessment.

PURPOSEGlycolytic production of HDO from the metabolism of perdeuterated glucose provides a means for metabolic imaging with 2H MRI. The present study compared HDO production from a cost-efficient [2,3,4,6,6-2H5]glucose with [2H7]glucose in vitro and in vivo. METHODS2H NMR spectroscopy was performed to measure glucose consumption, lactate, and HDO production in the SFxL glioblastoma cell line. In vivo studies in healthy mice using 2H magnetic resonance spectroscopy were performed at 11.1 T after administering a bolus of either metabolic contrast agent. In vivo metabolite levels were quantified using unlocalized and slice-selective localized spectra. RESULTSOur in vitro results demonstrated similar glucose consumption and HDO production kinetics, although significant differences in lactate labeling were observed. The in vivo study showed comparable glucose consumption and HDO production kinetics following tail-vein bolus administration of either metabolic contrast agent, while lactate was not detected in the brain. CONCLUSION[2,3,4,6,6-2H5]glucose shows comparable HDO production to [2H7]glucose, while offering lower cost and reduced spectral complexity. These findings place [2,3,4,6,6-2H5]glucose as an alternative to [2H7]glucose for HDO-based DMI studies.

Sodium magnetic resonance imaging (23Na MRI) provides a unique opportunity to probe ionic microenvironments in neural tissue because sodium ions play central roles in membrane electrophysiology, ion transport, and cellular homeostasis. Unlike conventional proton ({superscript 1}H) MRI, which primarily reflects water distribution and tissue structure, {superscript 2}3Na MRI is sensitive to ionic compartmentation and quadrupolar interactions arising from the spin-3/2 nature of the sodium nucleus. However, sodium MRI remains technically challenging due to intrinsically low signal sensitivity and rapid biexponential relaxation, particularly when imaging small biological systems. Here, we establish a high-field multinuclear MRI platform for imaging human cerebral organoids at 14 Tesla. Cerebral organoids derived from human induced pluripotent stem cells provide a simplified three-dimensional neural tissue model that enables investigation of ionic microenvironments without vascular or systemic confounds. Using a dual-tuned {superscript 1}H/{superscript 2}3Na radiofrequency coil, we performed co-registered structural, diffusion, and sodium imaging of individual fixed organoids. High-resolution {superscript 1}H MRI (33-100 m) revealed pronounced microstructural heterogeneity, while multi-echo {superscript 2}3Na MRI (300-400 m) enabled voxel-wise characterization of quadrupolar relaxation behavior. Bi-exponential analysis of the sodium signal decay identified distinct relaxation components (T2*short {approx} 1 ms and T2*long {approx} 12 ms) and revealed spatial heterogeneity in sodium microenvironments across the organoid tissue. These results demonstrate the feasibility of quantitative sodium relaxometry in cortical organoids and establish a multinuclear imaging platform for investigating ionic microenvironment dynamics in three-dimensional neural tissue models.

Cerebrovascular reactivity (CVR), the ability of cerebral blood vessels to dilate or constrict in response to a vasoactive stimulus, is an important measure of cerebrovascular health. Accurate CVR estimation requires accounting for the time required for the vasoactive stimulus to reach each brain region and the time it takes for local arterioles to modulate cerebral blood flow. The temporal search range used to calculate this spatially varying offset can substantially impact CVR estimates, and the appropriate search range may vary across populations, acquisition protocols, and even brain regions. Here, we present an iterative approach for automatically determining the appropriate maximum shift, using breath-hold fMRI data acquired in a cohort of stroke survivors. This approach selectively expands the delay search range only for voxels with estimated delays at the boundary (i.e., near the minimum or maximum shift) until the estimated delay is no longer constrained or a predefined value is reached. In the context of stroke, this approach significantly increased the number of voxels with statistically significant CVR among those initially at the boundary. It also resulted in CVR polarity reversals in voxels originally at the early-response boundary and amplified negative CVR values in voxels originally at the late-response boundary, suggesting that using an iterative maximum shift can critically impact CVR interpretation. This approach is broadly applicable beyond stroke, but careful parameter tuning is required, as illustrated by our demonstration of the parameter tuning process for a participant with Moyamoya disease. Together, these findings suggest that iterative delay correction allows for improved CVR assessments in clinical populations.

The majority of MR-based brain imaging methods provides macroscopic information averaged over the entire imaging voxel. Yet tissue composition and microstructure are heterogeneous within the cubic millimeter-sized MRI voxel that contains numerous distinct water pools at mesoscopic, microscopic, and nanoscopic length scales. Accurately measuring their individual characteristics in live human brain has the potential to reveal hidden salient meso/micro-structural features and uncover subtle changes that may occur in development, neurological disorders, trauma, etc. Nevertheless, because of many technical and scientific challenges, there is a dearth of robust, quantitative methods to probe tissue water dynamics at these subvoxel length scales. Here we present a novel empirical spectroscopic diffusion MRI method that estimates the probability density function (pdf) of diffusion tensors, i.e., the diffusion tensor distribution (DTD) in the human brain in-vivo. Our method entails performing a multi-dimensional Inverse Laplace Transform (ILT) which is generally an ill-posed and ill-conditioned problem. However, we overcome these obstacles using a hierarchy of lower-dimensional marginal distributions of the DTD estimated from diffusion weighted (DW) signals obtained from single, double, and triple pulsed-field gradient (PFG) experiments. Iteratively applying this hierarchy of marginal distribution progressively shrinks the space of admissible solutions. We extensively vet this framework with simulated DWI data obtained from realistic DTD motifs that mimic different cell and tissue properties seen in the brain. We then experimentally test our approach in vivo in brains of healthy normal human subjects. We segment the reconstructed DTD within a voxel to identify signatures of different tissue and cell types, and cluster these DTDs to identify various water pools. We use the high dimensional spectrum to robustly remove the free water compartment that often confounds tissue microstructure. We take ensemble averages of invariants of the micro-diffusion tensors, and measure and map their distributions to visualize salient intrinsic mesoscopic features. Since DTD MRI subsumes DTI, we also compute the family of DTI-derived quantitative imaging biomarkers from the moments of the distributions of the mean diffusivity and FA derived from the DTD. Our approach has great translational potential, revealing new microstructural features not observed previously observed in in vivo MRI.

Heat-related illnesses pose a significant public health challenge in Europe, resulting in increased mortality. Although cold water immersion (CWI) is the most effective treatment for heat stroke, its clinical use is limited. A better understanding of temperature changes in the peripheral body regions can lead to more effective CWI application. Nevertheless, most muscle temperature measurement techniques are invasive. This study evaluated magnetic resonance spectroscopy (MRS) for non-invasive assessment of intramuscular temperature during cold stress and rewarming. Nine healthy volunteers (7 men, 2 women) participated in three 3T MRI sessions: baseline (PRE), immediately after 15 minutes of CWI at 10 degrees to the iliac crest (POST-CWI), and following 100-Watt cycling (POST-cycling). Each scan session included T1w and localized spectroscopy acquisitions in the right thigh. Absolute temperature was estimated from the proton resonance frequency shift between water and creatine peaks. The measurements were split into three groups of voxels, defined as follows: close to the top (TL), bottom (BL), or central (DL) thigh positions. Measurement depth showed a location main effect (p<0.001, p^2=0.40), with DL (35.4[5.9] mm) significantly deeper than TL (22.5[4.2] mm) and BL (25.3[5.1] mm), remaining constant across phases. Temperature decreased significantly from PRE to POST-CWI across all locations (TL: p<0.001, d=2.74; BL: p<0.001, d=1.84; DL: p<0.005, d=1.14). Post-cycling temperature increased at all sites compared to POST-CWI (DL: p=0.040, d=1.06; TL: p<0.001, d=1.7; BL: p<0.001, d=1.80), though TL remained lower than PRE (p<0.017, d=1.48). During POST-CWI, DL showed a significantly higher temperature than TL (p<0.001, d=2.13) and BL (p<0.001, d=2.06). These findings demonstrate that MRS-based temperature mapping provides unique anatomical and thermal characterization of muscle during thermoregulatory stress. While results are promising for understanding CWI mechanisms, validation in larger cohorts is necessary to establish clinical reliability and reproducibility for heat illness management.

The photon measurement density function (PMDF) plays a fundamental role in both pre-experimental optode arrangement and post-experimental data analysis in functional near-infrared spectroscopy (fNIRS). Conventionally, PMDFs are derived from structural MR images through tissue segmentation and photon propagation modeling (PPM), which are computationally demanding and time-consuming, thereby limiting their practical use. In this study, we propose a novel deep learning-based framework to estimate PMDFs directly from MR images and channel configurations. The proposed method supports flexible source-detector distances and eliminates the need for explicit tissue segmentation and repeated photon simulations. Specifically, a convolutional neural network is trained to predict photon fluence distributions, from which PMDFs are subsequently derived using the adjoint formulation. The trained model is evaluated on channels placed in both trained and unseen scalp regions across commonly used source-detector distances. The results demonstrate that the proposed method achieves PMDF estimations comparable to those obtained from PPM. Overall, this approach significantly reduces computational cost and has the potential to facilitate broader adoption of PMDF-based methods in the fNIRS community.

The SAFE MRI ECMO (NCT05469139) study established the safety of ultra-low-field 64mT MRI in patients receiving extracorporeal membrane oxygenation (ECMO) in the setting of intensive care and demonstrated that these images were highly sensitive in detecting acquired brain injuries. This retrospective analysis of prospectively collected observational data sought to expand on these findings in light of the crucial need for neurological monitoring while patients receive ECMO by evaluating the feasibility of volumetric analyses derived from ultra-low-field MR images. T2-weighted scans from thirty patients who received ultra-low-field MRI while undergoing ECMO at Johns Hopkins Hospital were analyzed using a volumetric pipeline to determine whole brain volume and volumes of total grey matter, total white matter, subcortical grey matter, ventricles, left hemisphere, right hemisphere, telencephalon, left and right lateral ventricles, the total intracranial volume, and the cerebellum. Segmented brain volumes in patients undergoing ECMO were comparable to measurements obtained using conventional field and ultra-low-field MRI in the absence of ECMO instrumentation. The subgroup analysis demonstrated subtle volumetric differences between patients supported with venoarterial ECMO and those receiving venovenous ECMO. These data provide the first evidence that ultra-low-field MRI provides volumetric measurements comparable to conventional field-strength MRI, even in the presence of ECMO circuitry, supporting its feasibility for neuroimaging in critically ill patients.

Null Subtraction Imaging (NSI) is a nonlinear beamforming approach that combines multiple receive apodizations and subtraction to improve spatial resolution in ultrasound imaging. In NSI, a DC offset parameter is introduced in the apodization design to control the sharpening of the effective beam pattern and, therefore, the degree of spatial-resolution enhancement. Here, we investigate the use of NSI in functional ultrasound (fUS) imaging of the mouse brain and compare its performance with conventional delay-and-sum (DAS) beamforming across a range of DC offset values. fUS acquisitions were performed in three anesthetized wild-type mice during periodic vibrissae stimulation. Activation maps were computed by correlating cerebral blood volume (CBV) signals with the stimulation pattern. Activation area, edge gradient, Dice similarity coefficient, and signal-to-noise ratio (SNR) were used to evaluate spatial localization, boundary sharpness, vascular alignment and signal stability, respectively. NSI yielded more spatially confined activation maps than DAS and produced sharper activation boundaries. However, for low DC offsets (DC < 0.5), the CBV signal exhibited increased fluctuations, which reduced temporal stability and limited the reliability of the functional maps. As the DC offset increased, temporal SNR improved, while the spatial-resolution gain progressively decreased. In our imaging configuration, intermediate DC values around DC {approx} 0.5 provided the most favorable compromise between improved spatial localization and sufficient temporal stability for reliable functional activation detection. These results demonstrate the feasibility of applying NSI to functional ultrasound imaging and provide a quantitative framework for selecting the DC parameter in fUS studies.

Diffusion-weighted magnetic resonance imaging (dMRI) is used to study white matter microstructure and to delineate pathways by estimating fiber orientation distributions (FODs). Symmetric FODs represent the conventional model assuming antipodal symmetry in water diffusion. However, in complex regions with bending, branching or fanning fibers, this assumption is not guaranteed. To better capture such underlying fibers geometries, asymmetric FODs (A-FODs), derived from neighboring FODs, have been introduced. Here, we propose an Encoder-based Curvature-Aware Regularization (EnCAR) method for estimating A-FODs. Incorporating curvature features into the regularization weight applied to neighboring voxels improves reconstruction of A-FODs. A self-supervised Transformer network, combined with a Spherical Harmonics Semantic Encoder, learns region-specific regularization parameters from this local neighborhood to capture the diversity of fiber geometries across the brain. The EnCAR method was verified on the DiSCo challenge phantom, and applied to in vivo multi-shell Human data. The model estimated sharp, high-angular-resolution A-FODs that were well aligned with local fiber pathway. Compared with established FOD and A-FOD methods, it performed on par in regions dominated by symmetric FODs and outperformed them in complex asymmetric regions. Quantitative evaluation using the Asymmetry Index (ASI) and Model Discrepancy Index (MDI) confirmed improved consistency with the underlying diffusion signals. By ensuring smooth directional transitions, this work enhances the visibility of continuous fiber segments.

Conflicting evidence on scatter correction (SC) methods plagues quantitative myocardial perfusion SPECT (MPI), hindering standardized clinical protocols. This simulation study, utilizing the SIMIND Monte Carlo program and a highly realistic 4D XCAT phantom, systematically evaluates Dual Energy Window (DEW, with k=0.5) and Triple Energy Window (TEW) SC techniques. We uniquely investigate their performance across various photopeak window widths (2, 4, and 6 keV) and novel overlapped/non overlapped configurations specifically for Tc 99m MPI parameters largely unexplored in realistic cardiac models. Images were reconstructed with OSEM under uncorrected (UC), SC, and combined attenuation and scatter corrected (ACSC) conditions. Quantitative analysis focused on signal to noise ratio (SNR), contrast to noise ratio (CNR), defect contrast, and relative noise to background (RNB). Our findings consistently show ACSC's superior performance in CNR, SNR, and defect contrast, confirming its critical role. Interestingly, SC alone reduced noise but compromised defect contrast relative to UC, highlighting a potential trade-off without attenuation correction. Crucially, this study reveals minimal influence of photopeak window width and overlap configuration on image quality, and no significant difference between DEW and TEW across most metrics. These results provide essential evidence for optimizing quantitative MPI protocols, suggesting that for Tc 99m, the choice between DEW and TEW, and specific window settings, may be less critical than ensuring robust attenuation correction.

The human neocortex is organized into six laminae forming the structural basis for feedforward and feedback connections across the brain, yet their functional contributions have remained largely inaccessible for non-invasive imaging methods. Leveraging the ultrahigh field of a 10.5 Tesla scanner, we acquired anatomical and functional MRI data at 0.37mm ([~]0.05 {micro}L) and 0.35mm ([~]0.04 {micro}L) isotropic resolution, respectively, approaching the scale of individual cortical layers in humans. Using the Stria of Gennari as an in-vivo anatomical landmark, we extend our previous finding that feedforward visual activation in layer IV of the primary visual cortex during visual stimulation was resolved in laminar BOLD profiles. These laminar features were reproducible across sessions and were not clearly visible with more typical 0.8 mm resolutions at 7T, underscoring the benefits of further increases in magnetic field strength and resolution. This imaging domain, however, comes with increasing challenges of distortion, alignment, and cortical depth estimation, which must be addressed and mitigated to realize its benefits. In this paper we discuss the promises and challenges of this new regime of high resolutions. Our findings showcase the potential of ultrahigh field, ultrahigh resolution human fMRI to bridge the gap with invasive imaging of cortical layers.

Despite the increased water content in fibrotic livers, numerous studies reported a decrease in ADC (apparent diffusion coefficient) in liver fibrosis. We argue that the ADC decrease in fibrotic livers is due to the T2 shine-through of ADC, as the longer T2 in liver fibrosis leads to less signal decay between the low and high b-value images. The metric slow diffusion coefficient (SDC) was proposed to mitigate the difficulties associated with this T2 shine-through of ADC. This study calculated ADC and SDC of one rat study with liver fibrosis induced by biliary duct ligation (BDL), and three sets of human liver fibrosis data. To tease out the menopausal effect on SDC, only the results of mens livers were analysed for the human datasets. The rat study showed, liver ADC decreased stepwise (in weeks after BDL procedure) following fibrosis induction, SDC increased stepwise. In human studies, all three datasets consistently showed advanced fibrosis had an ADC lower than that of earlier stage fibrosis; advanced fibrosis had a SDC higher than that of earlier stage fibrosis. When each liver SDC datum was normalized by the mean value of the controls without fibrosis, and the three human datasets were summed together, stage-1 liver fibrosis had a normalized SDC value lower than that of the controls, and there was a stepwise increase of SDC value from stage-1 liver fibrosis to stage-4 liver fibrosis. It is known that liver fibrosis is associated with lower perfusion, higher iron/susceptibility, and higher water content, and these three factors all contribute to the lower ADC measure. Higher iron/susceptibility lowers SDC measure, whereas higher water content elevates SDC measure. It is likely that for early-stage fibrosis, the net effect of susceptibility and water leads to a lower SDC, while for advanced fibrosis, the net effect leads to a higher SDC.

BackgroundManganese-enhanced magnetic resonance imaging (MEMRI) is a powerful approach for mapping brain-wide neural activity and axonal projections in vivo. Yet standardized computational frameworks for voxel-wise and atlas-based characterization of brain states across large experimental cohorts remain limited. New methodHere, we present methodological advances for preprocessing and statistical analysis of MEMRI datasets to support scalable, reproducible cohort-level analyses. Quality assurance metrics were developed to evaluate images, cohort-level anatomical alignment, and intensity normalization. Using simulated data, we optimized smoothing, effect-size, and cluster-size thresholds to balance sensitivity and specificity in voxel-wise statistical mapping. We developed InVivoSegment software to apply to our new InVivo Atlas for segmentation of MEMRI data and interpretation of brain-wide activity. ResultsQuality assurance analyses established benchmarks for Mn(II)-induced signal- and contrast-to-noise evaluation, precise cohort-level alignment at 100 m isotropic resolution, and robust intensity normalization. Balanced accuracy and Youdens J statistics were calculated from simulated true positive and noise-only intensities, which defined optimal parameters for smoothing kernel, cluster-size and effect-size thresholds during voxel-wise mapping. Segmentation of simulated data demonstrated reliable transformation of voxel-wise results into regional summaries and identified secondary thresholds that minimize noise-driven artifacts. Comparison with existing methodsApproach to optimize correction parameters for statistical mapping using simulated images improves voxel- and segment-wise sensitivity compared to FDR/FWE-based correction procedures. ConclusionsThese methodological advances enable scalable, reproducible, brain-wide quantification of longitudinal changes in MEMRI studies, strengthen mechanistic investigation of brain-state dynamics relevant to human health, and provide broadly applicable tools for neuroimaging analyses beyond MEMRI applications. HighlightsO_LIQuantitative assurance of image quality complements visual assessment for cohort-level batch processing. C_LIO_LIOptimization of parameters using simulated noise-only images with and without investigator-embedded signal for voxel-wise mapping. C_LIO_LIA new software, "InVivoSegment" together with a labeled atlas, automates reliable user-friendly segmentation of voxel-wise data. C_LIO_LIMethodological advances in MEMRI data processing and computational analyses support scalable voxel- and segment-wise quantification of brain-wide neural activity. C_LI

Different MRI image contrasts are designed to highlight various tissue properties and combining them allows extension of probabilistic segmentation beyond the commonly used "gray-white-CSF" models. This work describes a fully automated method that combines T1-weighted, T2-FLAIR, and conventional T2-weighted images to provide internal consistency across prediction of tissue segmentations including segmentation of superficial and deep gray matter, white matter hyperintensities, and MR-visible perivascular spaces. Results from 773 imaging datasets from 403 participants in the Mayo Clinic Study of Aging and Mayo Clinic Alzheimers Disease Research Center (ADRC) are presented.

Multi-site diffusion MRI studies face scanner-induced variability that can obscure biological signal. Harmonization methods such as ComBat have been developed to address this, but have been evaluated primarily on diffusion scalar metrics. Whether scanner reproducibility differs across fundamentally distinct tract-derived representations has not been systematically compared. Here, we compared the reproducibility of three metric families (diffusion, shape, and connectivity) across 36 association tracts using the MASiVar dataset (5 subjects, 4 scanners, 27 sessions). We assessed intraclass correlation coefficients (ICC) and multivariate subject discrimination at baseline, under dimensionality reduction, and after ComBat harmonization. At baseline, shape metrics showed the highest reproducibility (median ICC 0.69), followed by connectivity (0.49) and diffusion (0.34). Shape and connectivity achieved comparable subject discrimination (both 1.75), significantly exceeding diffusion (1.23). ComBat harmonization improved all families but harmonized diffusion (0.58) remained below unharmonized shape (0.69), indicating that metric family selection remains consequential even after harmonization. Under low-dimensional representation, connectivity showed the largest gains (ICC 0.86, subject discrimination 3.0), exceeding other families at any dimensionality. Analysis of principal component loadings identified a small number of cortical regions per tract (median 6) that capture 95% of the reproducible connectivity signal, providing a per-tract reference for selecting the most informative regions in future multi-site studies. These findings indicate that the choice of which tract-derived metrics to analyze in multi-site studies deserves at least as much consideration as how to harmonize them.

AO_SCPLOWBSTRACTC_SCPLOWCerebrospinal and interstitial fluid transport play a central role in brain metabolic waste clearance, yet non-invasive quantification of deep-brain flow dynamics remains challenging. Magnetic Resonance Artificial Intelligence Velocimetry (MR-AIV) is a physics-informed neural network framework that infers three-dimensional velocity, pressure, and permeability fields from dynamic contrastenhanced MRI by embedding porous-media flow physics into the learning process. Here, we present a methodological refinement and systematic evaluation of MR-AIV. We introduce a universal, anatomically informed, region-of-interest-based permeability initialization that improves anatomical alignment and physical consistency across subjects. We quantify the sensitivity of inferred fields to key modelling choices, including initialization strategies, permeability bounds, diffusivity assumptions, signal-concentration relationships, and measurement noise. Across these conditions, MR-AIV yields stable velocity and permeability estimates with preserved spatial structure. Together, these results establish practical guidelines and identify stable operating regimes for reliable deployment of MR-AIV. By improving robustness and reproducibility, this work strengthens MR-AIV as a minimally invasive approach for mapping brain-wide porous fluid transport and supports its application to studies of neurological health and disease.