Magnetic Resonance Imaging

BackgroundDiffusion-weighted MRI is central to prostate cancer detection, but apparent diffusion coefficient (ADC) has limited reproducibility across scanners and sites. Restriction Spectrum Imaging restriction score maximum value (RSIrs-max) may provide a more reproducible biomarker. PurposeTo evaluate cross-session reproducibility of within-lesion mean ADC and RSIrs-max on prostate MRI, including same-vendor and cross-vendor comparisons, and in unfavorable-histology prostate cancer (uhPC) and different interpolation settings. Materials and MethodsIn this prospective study, participants with suspected or known prostate cancer enrolled from August 2022 to January 2026 underwent two MRI examinations including an RSI protocol. MRI-visible lesions were contoured on T2-weighted MRI; in participants with multiple lesions, the index lesion was selected. Mean ADC and RSIrs-max were measured within MRI-visible lesions. Analyses included all visible lesions, same-vendor and cross-vendor subgroups, participants with uhPC, and 20 participants with scans reconstructed with and without zero-filled interpolation (a setting with different defaults across vendors). Pearson correlation coefficients with 10,000 bootstrap resamples were used to estimate 95% confidence intervals. ResultsSixty-one male participants (median age, 69 years [IQR, 63-74]) were evaluated; 58 of 61 (95%) had MRI-visible lesions, and 26 of 58 (45%) had uhPC. For all MRI-visible lesions, correlations were 0.55 (95% CI: 0.23-0.76) for mean ADC and 0.83 (95% CI: 0.72-0.90) for RSIrs-max. In same-vendor scans, correlations were 0.76 (95% CI: 0.27-0.95) and 0.88 (95% CI: 0.72-0.96); in cross-vendor scans, they were 0.31 (95% CI: -0.07-0.62) and 0.79 (95% CI: 0.65-0.89), respectively. In uhPC, correlations were 0.42 (95% CI: -0.02-0.83) for mean ADC and 0.90 (95% CI: 0.77-0.96) for RSIrs-max. With inconsistent versus consistent interpolation, RSIrs-max correlation increased from 0.73 (95% CI: 0.48-0.89) to 0.89 (95% CI: 0.78-0.96). ConclusionADC showed limited reproducibility, particularly across vendors. RSIrs-max has stronger between-session reproducibility across same-vendor, cross-vendor, uhPC, and interpolation analyses.

"Remote palpation" appears to be an oxymoron, but here we demonstrate a non-contacting MRI method to obtain mechanical stiffness parameters of the human brain solely by measuring deformations caused by the pumping action of the heart. Mechanical stiffness is an important tissue property that is highly sensitive to subtle changes in the tissue milieu; MR elastography (MRE) is among a handful of methods used to measure it, typically via an external driver/tamper that introduces mechanical waves into the tissue. Applying MRE in the brain is challenging due to the use of an external actuator/tamper and the mechanical anisotropy of brain tissue, which requires a 4th-order tensor to describe it. In this study, we use the intrinsic deformation of brain tissue caused by periodic cardiac pulsations to measure the 4th-order elasticity tensor throughout the brain while simultaneously estimating the 2nd-order diffusion tensor in each voxel throughout the cardiac cycle which we use as a priori information in the reconstruction of the elasticity tensor. While the DTI-derived mean diffusivity (MD) appears uniform throughout brain parenchyma, stiffness maps obtained at about 1 Hz (i.e., at the fundamental cardiac frequency) show that brain tissue is very soft within gray matter, and within white matter pathways, such as along the corpus callosum, corona radiata, etc. Generally, stiffness differences at internal tissue boundaries are expected to produce local stress concentration there, which may predispose tissues to damage, e.g., in traumatic brain injury (TBI). Therefore, our novel tamperless MRE method has the potential to not only identify such interfaces, but assess and follow changes in tissue stiffness there that might occur following injury.

IntroductionPostmortem human brain magnetic resonance imaging (MRI) offers a unique opportunity to study finer neuroanatomical details and enables direct correlations with gold standard histological and immunohistochemical assessments. However, to prevent tissue decay, postmortem brains are preserved in fixative solutions which can alter tissue properties and exert substantial impacts on the MRI signals. The present study investigates the impact of formalin fixation, the most commonly used solution for postmortem human brain preservation, on different quantitative MRI contrasts. Methods142 intact human brain hemispheres immersed in 10% formalin for a range of fixation durations (between 0 days and 20 years) were imaged in a 3T MRI scanner. A subset of 10 brains were further scanned repeatedly at days 0, 3, 10, 20, 30, 60, 90, and 120 to allow for better characterization of the initial transient effects of fixation. Voxel-wise T1 and T2* relaxation, T1/T2 ratio, and myelin water fraction (MWF) maps were generated for each specimen and timepoint, and linear and nonlinear models were used to examine the spatiotemporal changes associated with progressive fixation. ResultsAll investigated metrics were significantly impacted by formalin fixation, albeit at different rates and with differing regional patterns. T1 and T2* relaxation time decreased as a result of progressive fixation, whereas T1/T2 ratio and MWF measures increased. T1 relaxation and T1/T2 ratio showed nonlinear patterns with initially accelerated changes that decelerate in the first few months, whereas T2* relaxation and MWF changes followed a more linear trend. ConclusionFormaldehyde fixation exerts systematic changes on quantitative MRI signals that can be modeled and adjusted for to allow for harmonized comparisons of MRI metrics across brains fixed for differing durations. The distinct temporal trajectories observed across metrics highlight the need to account for fixation duration in study design and downstream analyses, particularly when integrating datasets acquired under heterogeneous conditions. Our findings provide a quantitative framework for correcting fixation-induced biases, thereby improving the interpretability and reproducibility of postmortem MRI studies.

Introduction: Structural neuroimaging relies on T1-weighted (T1w) magnetic resonance imaging (MRI) for brain morphometry, yet at 7 Tesla (7 T) transmit field (B1+) inhomogeneity remains a major source of bias. Although Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) improves the tissue contrast, residual B1+ effects may persist and may be exacerbated in aging or clinical populations, where anatomical and physiological factors further challenge image quality and preprocessing. The impact of B1+ inhomogeneity on automated quality assessment and morphometric statistical inference remains insufficiently understood. Methods: Submillimeter 7 T MP2RAGE brain acquisitions from carriers of a mitochondrial gene mutation (m.3243A>G) and controls were retrieved from previous studies. Image quality before and after B1+ inhomogeneity correction was assessed by multiple automated pipelines. Case-control morphometric studies, including regional volume and mean cortical thickness, were analyzed in both registration based and deep learning based segmentation frameworks. Changes in image quality metrics (IQMs) and morphometric statistical significance were evaluated to determine the impact of B1+ inhomogeneity correction. Results: Overall image quality rating and metrics sensitive to intensity non-uniformity and topological integrity consistently improved after B1+ inhomogeneity correction. However, its impact on morphometric statistical inferences was strongly method-dependent. Some pipelines showed redistribution of significant regions, whereas others predominantly demonstrated increased effects in sensitivity. Across methods, B1+ inhomogeneity correction altered the findings of morphometric analyses, particularly in cortical regions. Conclusion: Residual B1+ inhomogeneity at 7 T substantially influences both image quality control and morphometric evaluations. Current automated quality control approaches can hardly capture these effects reliably. B1+ inhomogeneity correction will not only improve intensity uniformity, but also change sensitivity of morphometric statistical inferences. To establish reliable morphometric biomarkers at UHF strengths, explicit B1+ correction and customized preprocessing are practically necessary and highly recommended.

Purpose: To quantify the contributions of signal-to-noise ratio (SNR) and velocity-to-encoding ratio (v/VENC) to velocity uncertainty in phase-contrast (PC) MRI and to develop a framework for in vivo voxel-wise uncertainty estimation. Methods: Through-plane 2D PC-MRI of the ascending aorta was acquired using multiple velocity encodings (150, 200, 300 cm/s) and flip angles (0, 5, 15, 20 degrees) to vary v/VENC and SNR. Voxel-wise SNR and velocity uncertainty maps were generated using empirically calibrated phase-noise modeling. Phase-resolved subject-level analyses were performed to quantify the relative contributions of SNR and |v|/VENC to percent velocity uncertainty (%unc). Uncertainty was propagated to flow, stroke volume (SV), and cardiac output (CO). Results: Velocity uncertainty varied substantially across the cardiac cycle and depended on both SNR and |v|/VENC. Across cardiac phases, |v|/VENC accounted for most explained variance in %unc (partial R2=0.666), while SNR provided a smaller but meaningful contribution (partial R2=0.287; full R2=0.909). Near peak systole, SNR contributed more strongly while overall uncertainty remained low. In contrast, diastolic %unc became unstable as velocity approached zero. These effects were most pronounced at low |v|/VENC, where higher VENC settings increased uncertainty despite similar SNR. SV uncertainty ranged from 0.27% to 1.07% across VENCxFA protocols. Conclusion: Velocity uncertainty in PC-MRI depends on both SNR and VENC adequacy in a physiologically phase-dependent manner. Relative uncertainty may become inadequate for precise quantification in low-flow applications, such as diastolic regurgitant jets, despite adequate SNR. Spatiotemporal uncertainty mapping provides a framework for uncertainty-aware PC-MRI acquisition and interpretation.

The aim of this study was to evaluate the contributions of age, sex, and MRI vendor to variance in Diffusion Tensor Imaging (DTI) metrics, with a focus on understanding the impact of these factors in large-scale healthy brain datasets. A dataset of 2,700 DTI scans from healthy controls across multiple sites and MRI vendors was analyzed. The DTI scalar metrics fractional anisotropy (FA) and mean diffusivity (MD) were processed and the influence of age, sex, vendor, and brain atlas selection were determined. A statistical analysis was conducted and revealed significant (p<0.05) age-related differences in DTI metrics, with older participants showing reduced FA and increased MD, in line with known microstructural changes. Sex differences were observed, with females exhibiting slightly higher FA and lower MD in certain brain regions. Vendor variability was also noted, with all three MRI vendors showing significant differences in FA with Siemens machines typically exhibiting higher FA values and GE machines lower FA values (i.e. FASiemens > FAPhilips > FAGE). Atlas selection also highlighted some specific ROI behaviour (e.g. tapetum of the corpus callosum) as one of the most significant regions of interest (ROIs) in the JHU-Tracts atlas that demonstrated a large amount of deterioration with age, particularly in females. These findings emphasize the need to account for biological factors such as age and sex, as well as technical factors like ROI selection and MRI vendor, when interpreting DTI data. The results demonstrate the potential of large-scale, multi-vendor datasets to uncover meaningful biological trends, while also addressing the challenges of scanner-specific variability. Although previous work has shown sex and age differences, this is the first large scale DTI analysis that has included age, sex, and MRI vendor as sources of variance in one model.

PURPOSE: The UK7T travelling head dataset was used to characterise the reproducibility of 7T measurements of the susceptibility of the hippocampal subfields, focusing on the Cornu Ammonis (CA1, CA2 and CA3), dentate gyrus (DG), subiculum (SUB), tail of the hippocampus (TAIL) and entorhinal cortex (ERC). METHODS: Susceptibility maps were created from whole-brain 3D single-echo GRE data (TE=20 ms; 0.7 mm isotropic resolution) using Multi-Scale Dipole Inversion. Automatic Segmentation of Hippocampal Subfields (ASHS) was applied to high resolution T1- and T2-weighted images for segmentation. The mean magnetic susceptibility and volume of hippocampal subfields was evaluated in 50 data sets, comprising 5 repeat acquisitions on 10 healthy participants (age 32 + or -6 years; 3 female). RESULTS: Averaging over subjects, susceptibility values spanned an 18ppb range over the hippocampus (ranging from -13.3ppb in DG to 4.7ppb in ERC). Susceptibility values in the larger hippocampal subfields showed a consistent pattern of variation across subjects, being generally more positive in ERC and SUB than in CA1 and more positive in CA1 than in DG and TAIL. The standard deviation of subfield susceptibilities over subjects ranged from 8.2ppb in the TAIL to 1.7ppb in CA1, and the average standard deviation across repeated measurements, which ranges from 1.7 to 4 ppb, was less than half of the inter-participant standard deviation in all subfields. Susceptibility values in the smaller subfields (CA2 and CA3) were more variable, but ICC(2,k) values for all subfields were >0.82. CONCLUSION: The reported data characterises the variation and reproducibility of hippocampal subfield susceptibility measurements at 7T.

Most high-field MRI scanners conduct imaging using phased-array coils, in which the signals received by an array of coil elements are combined for downstream processing. Optimally combining these signals requires knowledge of each coil's spatial sensitivity profile, which can be acquired from a volume coil with homogeneous sensitivity across the field-of-view. However, this approach is not often used on high-field MRI scanners, especially on non-clinical systems; therefore, this work uses an algorithm based on the singular-value decomposition (SVD), called SVD-B1, to estimate coil sensitivities directly from the array data itself. Images produced by SVD-B1 are devoid of wormhole artifacts and open-ended fringe lines commonly seen in more conventional reconstructions. Quantitative Susceptibility Maps (QSMs) produced using the algorithm were compared to those produced using other combination algorithms across clinically relevant regions of in-vivo and postmortem human brains. As progressive levels of simulated noise were added to the data, SVD-B1's QSMs were up to 3% (in-vivo) and 13% (postmortem) more consistent (as measured by their Intraclass Correlation Coefficient) than those from other algorithms. Additionally, these QSMs were up to 8.5% (in-vivo) and 36% (postmortem) more accurate than other QSMs with respect to a "single-coil" reference. A parallel imaging extension of SVD-B1, called SVD-B1 GRAPPA, achieved similar results for QSMs generated from progressively more accelerated acquisition data. These results show that SVD-B1 can improve the sensitivity of high-resolution QSM to subtle changes in fine-grained tissue structures (e.g., in neurodegenerative disease) and help reduce scan times in clinical settings where shorter scans are imperative.

PurposeTo present a first-of-its-kind artificial intelligence (AI-)integrated MR pulse sequence that detects out-of-voxel (OOV) artifacts in real-time (within-TR) and responds prospectively by updating the crusher gradient scheme. MethodsPer Excitation Real-time Execution & Guided Responses with Integrated Neural-network Evaluation (PEREGRINE), developed for deployment of deep learning models and sequence updates, operated time-domain (TD) and frequency-domain (FD) convolutional autoencoders that detect OOV artifacts. Scans without (AI-off) and with (AI-on) updates were collected from the prefrontal cortex of healthy volunteers using edited MRS. The degree of OOV contamination (OOV score) was quantified per transient based upon the prevalence of OOV signals in the TD and FD data. OOV scores above a user-defined threshold triggered an update of the gradient scheme, iterating through 48 permutations (6 axis transpositions x 8 polarity flips). ResultsWithin each 2-second TR, PEREGRINE successfully provided single-transient OOV scores and updated gradients accordingly. No difference was observed between the OOV scores from the full ("Full" condition) AI-on and AI-off sessions due to the AI-on scan cycling over better and worse gradient permutations relative to the AI-off scan. However, the AI-on scan had significantly lower OOV scores than the AI-off scan when selecting the transients where PEREGRINE persisted ("Dwell" condition) on a given gradient permutation. Ultimately, Fit Quality Number (FQN), from linear combination modeling, improved significantly for the AI-on compared to the AI-off scan. ConclusionPEREGRINE enabled an AI-integrated sequence allowing for real-time evaluation and reduction of OOV artifacts, identifying gradient modifications that produced less OOV contamination.

IntroductionR2* is the transverse relaxation rate of tissue, influenced by local magnetic field inhomogeneities arising from susceptibility differences. It decomposes into R2 and R2', where R2' is the reversible component most sensitive to blood oxygenation and forms the basis of the BOLD fMRI signal. Although orientation dependence of R2 and R2* in white matter (WM) has been attributed primarily to myelin, the vascular contribution to R2' has not been systematically characterized. This study investigated the orientation dependence of WM R2', assessed existing models, and developed an improved model incorporating both myelin and vascular effects. MethodsMonte Carlo simulations were performed using BOLDswimsuite with 2D WM voxels generated from 5,000 fibres. Spin-echo (TE = 70 ms) and gradient-echo (TE = 35 ms) signals were simulated across 30 fibre orientations (0{degrees}-90{degrees}), and R2' was calculated as R2* - R2. Oxygenation, cerebral blood volume (CBV), vessel size, and vessel geometry were systematically varied. Four published models and a novel Myelin-Blood model were fitted to simulated R2' data and compared using R2 and RMSE. ResultsStrong orientation dependence was observed for R2 and R2*. Parallel and mixed vessel geometries produced greater R2' amplitude and orientation dependence than random geometries. Decreasing oxygenation and increasing CBV amplified orientation effects; vessel size altered peak locations. Existing vascular models performed poorly, and the empirical myelin model showed substantial errors near the magic angle. The Myelin-Blood model provided near-perfect fits (mean R2 = 0.999, RMSE = 0.007 Hz), reducing RMSE by approximately 74%. DiscussionWM R2' cannot be adequately described by vascular or myelin effects alone. Myelin is the primary determinant of orientation dependence, but systematic vascular contributions were observed, particularly near the magic angle and under low oxygenation. The Myelin-Blood model improves characterisation of WM R2' and may reduce orientation-dependent bias in qBOLD OEF estimation while improving interpretation of WM BOLD fMRI signals.

Purpose: To develop and evaluate a flexible, software-defined radar platform for contactless, vendor-independent motion detection and correction in MRI. Methods: A continuous-wave (CW) Doppler radar was implemented using a software-defined radio and the open-source GNU Radio framework. The system was deployed inside a 1.5T MRI scanner and synchronized with MRI acquisitions. We evaluated the performance in a custom-developed internal motion phantom and in healthy volunteers to track respiration and bulk motion. The radar-derived signal was validated against cine MRI and used to demonstrate both retrospective and prospective motion management techniques in phantom and in healthy volunteers. Results: The radar provided robust motion signals that correlated strongly with image-based ground truth signals in both phantom and volunteer experiments. Signal characteristics were found to be frequency-dependent, enabling optimization for different motion regimes. Retrospective correction of free-breathing abdominal data using the radar signal effectively suppressed respiratory artifacts, achieving image quality comparable to a self-gating approach. Prospective triggering successfully reduced motion artifacts in the phantom study. The system also reliably detected sporadic events such as swallowing during neck imaging. Conclusion: Software-defined radar was demonstrated to be an effective platform for both prospective and retrospective motion correction. Its independence from the MRI system, ultra-wide band capabilities, and body-region versatility enable the adaptation of the technique for a wide range of imaging applications and protocols.

Ex vivo diffusion weighted imaging (DWI) enables high-resolution characterization of brain connectivity and is increasingly applied in comparative and evolutionary neuroscience. However, variability in tissue preparation and contrast agent exposure can substantially affect relaxation properties and compromise reproducibility, particularly in non-mammalian species. Here, we systematically assess the impact of different gadolinium-based contrast agent exposure protocols on relaxation stability and DWI compatibility in fixed pigeon brains. Brains were perfusion-fixed with 2% paraformaldehyde and assigned to four preparation protocols: (i) contrast agent exposure during perfusion, post-fixation, and rehydration; (ii) post-fixation and rehydration only; (iii) rehydration only; (iv) no contrast agent. Quantitative T1, T2, T2*, and DWI data were acquired at five time points over 70 days using a 7T MRI system. Protocols involving contrast agent during perfusion or post-fixation produced comparable relaxation trajectories, with T1, T2, and T2* stabilizing by Day 13. On day 13 the T1 values of tissue that was exposed to contrast agent, regardless of the application protocol were between 230.86 ms and 266.89 ms, while the T1 values of the control group were over 1100 ms at this point in time. T2 values of the experimental groups were between 39.97 ms and 56.17 ms while T2 values of the control group were between 58.68 ms and 77.82 ms. T2* values of the experimental groups were between 27.27 ms and 43.33 ms while T2* values of the control group were between 46.16 ms and 65.93 ms. Importantly, contrast agent exposure during rehydration alone resulted in equivalent stabilization after two weeks, reflecting gradual contrast agent diffusion into the tissue. In contrast, control samples without contrast agent exhibited significantly elevated T2 and T2* at later time points. These results demonstrate that post-fixation contrast agent exposure during rehydration is sufficient to achieve stable relaxation parameters and DWI compatibility, assessed via fractional anisotropy (FA) and mean diffusivity (MD) in ex vivo avian brain tissue. This minimal preparation protocol enhances reproducibility, reduces handling complexity, and supports standardized cross-species neuroimaging of brain connectivity.

PurposeTo develop a data-driven technique, Scout-based Multi-Echo NAvigator (SMENA), for joint estimation of motion and B0 inhomogeneity ({delta}B0) at a temporal resolution of [~]200 ms with minimal additional scan time for gradient-echo acquisition. MethodsSMENA consists of two key acquisition components: SMENA-scout and SMENA-nav. SMENA-scout is a rapid 3D 4-mm multi-echo acquisition completed in less than 8 seconds, providing images with matched contrast and phase at multiple echo times. SMENA-nav captures signal variations induced by motion and{delta} B0 during the scan using compact multi-echo navigator trajectories (3.5 ms) embedded within each TR. Motion and{delta} B0 maps were jointly estimated every [~]200 ms through a model-based optimization framework relating SMENA-scout to SMENA-nav. The estimation accuracy and correction performance of SMENA were evaluated in simulations and in vivo using multi-echo GRE and GRE-EPTI acquisitions. Multiple prospective motion experiments, including large continuous movement and deep breathing, were investigated. ResultsIn both simulations and in vivo experiments, accurate motion and{delta} B0 estimation were achieved. Compared with motion-only estimation, joint estimation reduced rotation and translation errors. Joint motion and{delta} B0 correction resulted in substantial improvements in image quality, particularly at longer echo times, producing an NRMSE of 10.4% compared to 31.6% with motion-only correction. High-temporal-resolution tracking of motion and{delta} B0 enabled improved reconstruction quality in scenarios involving continuous motion and deep breathing. ConclusionSMENA enables high-temporal-resolution joint estimation of motion and{delta} B0 with minimal additional acquisition cost, providing a practical solution for motion- and{delta} B0-robust MRI.

PurposeThe motion of the brain tissue within the skull is thought to be induced by cardiac pulsations and other hemodynamic processes and may influence CSF flow, but its precise drivers and downstream effects are unclear. Understanding these phenomena requires an accurate and precise method of tissue motion quantification that can be extended to investigate multiple potential drivers of tissue motion in vivo. MethodsHere, a version of the Displacement ENcoding with Stimulated Echoes (DENSE) pulse sequence was implemented to measure cardiac-locked velocity responses in the pons and midbrain in eight healthy volunteers. The method featured retrospective cardiac gating, a single mixing time, and measured multiple voxel sizes. ResultsA previously undescribed double-peak pattern of cardiac-locked longitudinally directed brainstem velocity was identified that appears to reflect both nonrigid and rigid motion components. This pattern was only visible when estimating the cardiac response using absolute time after systole instead of the percentage of the cardiac cycle. Measurements were performed in a custom-built slow-flow phantom, and repeat sessions were acquired in two volunteers to assess accuracy and precision. Despite potential increased influence of CSF motion with larger voxel sizes, no voxel-size-dependent bias was found in the velocity estimations. Lack of voxel size bias was attributed to the complexities of partial-volume effects between CSF and tissue in phase-valued data that were evaluated using numerical simulations. ConclusionIn sum, a method to measure brain tissue motion with high spatiotemporal precision is presented that can be extended to applications beyond measuring cardiac-locked motion.

The white matter (WM) cerebrovascular response remains poorly understood compared with grey matter (GM), partly due to technical challenges in perfusion quantification. Previous hypercapnia studies using BOLD MRI have reported reduced or negative WM cerebrovascular reactivity, but whether these findings reflect true reductions in cerebral blood flow (CBF) remains unclear. We used multi-delay pseudo-continuous arterial spin labeling (pCASL) to quantify hypercapnia-induced CBF changes ({Delta}CBF) while accounting for regional variability in arterial transit time. Twenty-five healthy young adults underwent MRI during normocapnia and hypercapnia (inhalation of a 4% CO2 gas mixture). Hypercapnia induced robust positive {Delta}CBF in cortical GM (26.7 {+/-} 13.5%), superficial WM (17.2 {+/-} 12.6%), periventricular regions (13.6 {+/-} 10.6%), and subcortical GM (25.7 {+/-} 14.1%) (all p < 0.0001). In contrast, deep WM exhibited a near-zero group-mean CBF response (1.0 {+/-} 8.9%, p = 0.57), with 10 of 25 participants demonstrating negative {Delta}CBF. Negative responses were consistently localized to the corona radiata, centrum semiovale, and optic radiation. Quality-control analyses showed that deep-WM {Delta}CBF estimates are robust at our long post-labeling delays, supporting the reliability of these findings. Across tissue compartments, higher baseline CBF was associated with reduced hypercapnic responsiveness, and deep-WM responses were strongly coupled with cortical GM responses across individuals. These results demonstrate that hypercapnia-induced perfusion responses are highly heterogeneous across tissue depths and provide evidence that negative CBF responses can occur in healthy deep WM. The findings challenge the assumption of uniformly positive cerebrovascular responses during hypercapnia and support a potential role for flow redistribution arising from regional differences in vascular resistance and reserve capacity.

Low-cost portable MRI has the potential to improve the accessibility of MRI, but new acquisition methods and protocols must be developed and evaluated to accommodate the reduction in SNR and greater impact of system imperfections. Diffusion tensor imaging (DTI) is a candidate tool for monitoring population health, but the bias and variance of quantitative diffusion tensor-derived metrics must be evaluated prior to designing such studies. DTI of the corpus callosum was performed on an in-house, portable 100 mT MRI system using a slab diffusion weighted Fast Spin Echo with radiofrequency (RF) encoding. Slice coverage was restricted to the corpus callosum to shorten scan time and reduce sensitivity to large rigid motion. In vivo DTI images were obtained in two healthy volunteers with nominal voxel size 50 mm3, scan time 25 minutes, and two different volunteers using nominal voxel size 25 mm3, scan time 35 minutes. Mean diffusivity (MD) and fractional anisotropy (FA) coefficients of variation were estimated in the 50 mm3 acquisition using a bootstrap approach and compared to resolution-matched data obtained on a conventional 1.5T system. MD / FA maps were compared quantitatively and qualitatively. Mean MD values in the corpus callosum obtained on the 100 mT system were within 10% of the reference 1.5T acquisition, but FAs were underestimated by 20-30%. The corpus callosum median MD coefficient of variation was 3.7%, and the median FA coefficient of variation was 7.5%. FA maps obtained at 100 mT had an elevated FA noise floor and color FA maps had lower apparent resolution but some white matter tracts were still distinguishable. HighlightsO_LIDiffusion Tensor Imaging (DTI) of the corpus callosum was performed on a portable 100 mT MRI scanner with 50 mm3 voxels in 25 minutes scan time. C_LIO_LIMean Diffusivity estimates in the corpus callosum obtained at 100 mT and 1.5T differed by less than 0.1 x 10-3 mm2/s. C_LIO_LISome white matter tracts were visible in color Fractional Anisotropy maps obtained at 100 mT but FA maps were underestimated by 20- 30% when compared to a resolution-matched 1.5T acquisition, and had lower apparent resolution. C_LI

Understanding vascular contributions to disease is a major unmet need. White matter lesions (WML) are an accepted imaging marker of cerebral small vessel disease, giving insights into its related pathologies. A unified approach for WML analyses in large multi-site data is lacking despite the need for pooling of data to overcome the limitations of often small heterogenous MRI studies which make subtyping and identifying patterns within disease groups difficult. Our ENIGMA-PD-WML pipeline is an open-source containerized pipeline containing all the code and packages required for pre-processing, processing and post-processing of T1-weighted and FLAIR data, outputting accurate and reproducible binary WML maps using a UNet approach. The pipeline provides a standardized image analysis approach for WML and outputs data in both native and MNI space to allow for sharing and pooling of data from multiple sites for large-data analysis. In addition to a reliable standardized approach for WML segmentation, key priorities when developing the pipeline included: usability, i.e., requiring minimal manual input and technical expertise to use, and suitability to run on various MRI scanners and acquisition parameters as is common in multi-site data. This paper describes the pipeline in detail, with rationale for each step, providing transparency and facilitating its usage to overcome reproducibility issues in large-scale WML analyses.

Cerebral small vessel disease and neurodegenerative disorders have been associated with increased cerebrovascular pulsatility. Recently, BOLD fMRI-based methods have emerged for assessing pulsatility, however their interpretability is limited because the relation between estimated pulsatility indices (PI) and vascular anatomy and physiology remains poorly understood. To improve interpretability, we introduce a cardiac-specific BOLD fMRI- based PI, investigate its relationship to the cortical vasculature, and validate its sensitivity by introducing the known physiological vascular modulation of hypercapnia. Using high-resolution 7T BOLD fMRI with gradient-echo (GE) and spin-echo (SE) sequences, we disentangled macro- and microvascular contributions to the PI and quantified it across cortical depth. PI maps revealed anatomically plausible patterns, with elevated GE-PI near large veins and in white matter while SE-PI remained largely constant across cortical depth. GE-PI decreased during hypercapnia consistent with altered vascular tone, SE-PI on the other hand did not. PI correlated with cerebrovascular reactivity and venous blood volume suggesting sensitivity to vascular density and vessel mechanics. Our findings demonstrate that BOLD-derived PI provides a spatially and physiologically specific measure of vascular pulsatility. The BOLD fMRI-based PI method is readily applicable to existing datasets and has potential for assessing potential microvascular damage in cerebrovascular and neurodegenerative disease.

Diffusion-weighted magnetic resonance imaging (dMRI)-based tractometry enables the quantification of white matter tissue properties in living humans while preserving anatomical specificity. Although tractometry is highly reproducible when the same scanner and acquisition protocol are used, its generalizability across scanners and protocols remains unclear. To address this gap, we performed a traveling-head experiment involving five subjects to evaluate tractometry across progressively different acquisition conditions, including multiple scanners, different scanner models, and two distinct protocols. Tractometry was performed for 20 major white matter tracts using diffusion tensor imaging metrics, neurite orientation dispersion and density imaging (NODDI) metrics, and a semi-quantitative ratio metric (T1w/b0). Generalizability across dataset pairs was quantified using the intraclass correlation coefficient (ICC). Tractometry showed consistently high ICCs when the scanner and protocol were identical; however, ICCs declined as differences in scanner model and acquisition protocol increased. Fractional anisotropy and orientation dispersion index retained relatively high ICCs across these comparisons, whereas other metrics showed marked declines when scanners or protocols differed. ComBat harmonization partially mitigated these declines, but ICCs did not reach the levels observed for datasets acquired using identical scanners and protocols. Finally, the minimum detectable change (MDC) for tractometry in datasets pooled across scanners and protocols varied by tract; for example, the optic radiation showed a lower MDC than the cingulum hippocampus. These findings highlight both the strengths and limitations of tractometry in multisite studies and highlight the importance of quantifying scanner- and protocol-dependent effects for specific metrics and tracts when interpreting measurements from heterogeneous datasets.

IntroductionCoronary artery disease (CAD) increases the risk of cerebrovascular events, yet early brain injury in this population remains poorly characterized. White matter hyperintensities (WMHs), a biomarker of cerebrovascular lesions, are prevalent in CAD and are linked to risk of stroke. Beyond total burden, spatial distribution of WMHs carries pathological significance and is critical for understanding CAD-related injury. While clinical outcomes including coronary revascularization procedure and myocardial infarction influence CAD prognosis, their impact on WMH burden remains unclear. MethodsThis study investigated regional WMH burden in CAD and its relationship with clinical characteristics. 82 adults over 50 years participated, including 44 individuals with CAD and 38 controls. WMHs were segmented from fluid attenuated inversion recovery and T1-weighted MRI and categorized as total, periventricular, deep, and superficial regions. History of myocardial infarction and coronary revascularization (coronary artery bypass grafting (CABG) and percutaneous coronary intervention (PCI)), was obtained from medical files. ResultsIndividuals with CAD exhibited higher total, periventricular, and deep WMH volumes than controls. Participants who underwent CABG had higher superficial WMH volumes than those with PCI, suggesting greater disease severity influences WMH burden. ConclusionCAD is characterized by a distinct pattern of cerebrovascular vulnerability, with revascularization procedures influencing WMH burden