Magnetic Resonance Imaging

Accurate quantification of arterial blood T2 can be useful for non-invasive assessment of blood oxygenation and blood-brain barrier (BBB) function. While arterial spin labeling (ASL) combined with multi-echo readouts offers a contrast-agent-free approach to map arterial blood T2, in vivo applications remain challenging due to rapid signal decay and low signal-to-noise ratio (SNR) at longer echo times (TEs), likely leading to overestimation of T2 values. We propose a novel temporal evolution acquisition based ASL (TEA-ASL) sequence incorporating an optimized variable refocusing flip angle (RFA) train to preserve signal across all TEs. Data were acquired on a 5T MRI system combining a pseudo-continuous ASL (pCASL) with the proposed TEA readout with 12 echo times (32-384 ms). The variable RFA scheme significantly improved signal stability across the echo train compared to conventional acquisition with constant RFAs. Accuracy and clinical feasibility of the proposed method was validated by simulations, phantom scans, in-vivo test/retest experiments and in a patient with middle cerebral artery stenosis. The proposed TEA-ASL technique provides robust arterial T2 mapping at ultra-high field, offering a promising tool for probing oxygenation-related hemodynamics and BBB-associated pathophysiology.

PurposeNeonatal imaging is particularly challenging because newborns have a high likelihood of head motion, which can degrade image quality and complicate interpretation. Improving MRI brain image quality may help reduce diagnostic uncertainty and facilitate the nuanced assessment of early myelinating structures in the neonatal brain. Although deep learning reconstruction algorithms designed to improve MRI image quality have been evaluated in pediatric imaging, they have not been specifically studied in exclusively neonatal populations. We sought to evaluate image quality improvement through the employment of a deep learning reconstruction algorithm in neonatal brain imaging. Methods3D T1-weighted brain MRIs were obtained in 15 neonates. A deep-learning reconstruction algorithm was applied to the image sets using low, medium, and high levels of denoising. Three radiologists qualitatively rated image quality (signal-to-noise ratio, presence of artifacts, and overall clarity) on a 4-point scale of eight early myelinating structures. Objective apparent signal-to-noise ratio (aSNR) and apparent contrast-to-noise ratio (aCNR), based on signal intensities of white-and gray-matter, was measured across all three denoising levels. ResultsEvaluation by radiologists indicated an overall increase in all image quality categories and increased conspicuity of the early myelinating structures as the level of denoising increased. Objective aSNR and aCNR values also increased progressively with denoising, with significant differences observed for nearly all pairwise comparisons. ConclusionOur findings suggest that the use of the proposed deep learning reconstruction algorithm improves image quality in 3D T1-weighted neonatal brain MRIs at 3T.

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.

MRI of fixed tissue is an excellent way to study pathological changes caused by different diseases with great anatomical detail. It is, however, known that properties of tissue change with fixation. The aim of this study was to determine the variability of several quantitative MRI (qMRI) parameters in fixed brain tissue obtained from donors unaffected by neurological conditions and investigate the existence of quantitative parameters which vary little between specimens. We introduce a 3D method for high-resolution mapping of water content, T1 and T2* relaxation times and parameters characterising magnetisation transfer and apply it at 3T to 7 whole, fixed human brains (3 male, 4 female, aged between 47 and 79 years, mean age 67 years). The qMRI parameters determined include relaxation rates T1 and T2*, MT ratio and T1 and T2* after MT. From these we can further derive semiquantitative MT parameters such as the exchange rate (ktrans) and bound pool fraction (fbound). Correlations between these parameters are investigated. In addition, truly quantitative water content determined non-invasively with MRI is reported on whole human post mortem brains - to our knowledge, for the first time. Water content was found to have mean values of 73% for WM and 85% for GM with standard deviation below 2.5% over 7 brains, and thus a few percent units higher than in vivo (69% and 81%) and of comparable constancy.

Deep-learning based super-resolution has shown promise for enhancing the spatial resolution of brain magnetic resonance images, which may help visualize small anatomical structures more clearly. However, when only limited training data are available, it remains uncertain which model assessment method provides the most reliable estimate of out-of-sample performance. In this study, three widely used assessment strategies (three-way holdout, k-fold cross-validation, and nested cross-validation) were compared for evaluating the performance of such models in small datasets. Across 30 iterations, we randomly selected subsets of 20 T2-weighted images from the 1,113 scans of the Human Connectome Project. Each subset was used to train a model and estimate performance using the three methods. The ground truth error was computed from the remaining images. The assessment error is the difference between the estimated error and the ground truth error. The median assessment errors were 0.11,- 0.13, - 0.32 for three-way holdout, k-fold cross-validation, and nested cross-validation, respectively, with the cross-validation methods showing considerably smaller dispersions. Nested cross-validation selected fewer epochs, indicating more conservative model selection, but required substantially greater computational time, over three times longer than three-way holdout and more than twenty times longer than k-fold cross-validation. Our findings suggest that k-fold cross-validation offers the most favourable balance between accuracy, stability, and computational feasibility in small datasets. Further research is needed to determine how model complexity, dataset size, and the number of cross-validation folds influence assessment accuracy.

The non-invasive estimation of intra- and extracellular microstructural parameters using biophysical models has been a major focus in brain microstructure imaging with MRI. The Standard Model (SM) of diffusion in white matter (WM) provides a unifying framework for various modelling approaches, representing axons as impermeable narrow cylinders embedded within a locally anisotropic extra-axonal space. However, the SM relies on simplifying assumptions that may not hold in realistic WM tissue, as they do not take into account axonal undulations, beading, the presence of glial cells, or membrane permeability. In this work, we investigate how SM-derived estimates behave when the model is applied to realistic numerical WM substrates generated by the CATERPillar tool. Specifically, we vary (i) axonal morphological features such as beading and undulations, (ii) axonal packing density, (iii) orientation dispersion, (iv) membrane permeability of axons and astrocytes separately, (v) myelin volume fraction, and (vi) diffusion time. In each part of the analysis, different noise levels are introduced. Overall, according to our results, the relative changes in SM estimates show that the intra-axonal volume fraction f increased with stronger beading, higher packing density, and greater myelin volume, and was strongly influenced by axonal and astrocytic permeability. The orientation dispersion index p2 was affected by undulation, but was substantially biased at low packing densities, with stronger beading and when astrocytes were impermeable. The effective intra-axonal diffusivity Da decreased with stronger beading and undulation and tended to be overestimated in most scenarios. The parallel extra-axonal diffusivity De|| was strongly influenced by axonal permeability, as well as packing density, dispersion, and undulation, and was the most noise-sensitive parameter, showing systematic overestimation at low SNR. Finally, the effective perpendicular extra-axonal diffusivity De{perp} was the most stable parameter relative to the effective ground truth across the tested conditions, while remaining sensitive to packing density, axonal permeability, myelin volume fraction, and undulation. These findings enable users to identify potential biases introduced by varying conditions and to adjust their interpretations accordingly.

BackgroundMRI plays an essential role in diagnosing and monitoring neurological diseases. Conventional protocols rely on multiple sequences to obtain complementary contrasts, increasing scan time, cost, and tolerability. Generating multiple contrasts from a single acquisition may streamline workflow while maintaining clinical utility. PurposeTrain attention-based convolutional neural networks (ACNNs) to generate clinical-quality FLAIR, MPRAGE, R2*, and derived contrasts from a single Gradient Echo Plural Contrast Imaging (GEPCI) acquisition, enabling multi-contrast imaging from one scan. Study TypeRetrospective. Population43 MRI scans from individuals with multiple sclerosis (25/18 F/M, 49{+/-}11 years old). Field Strength/Sequence3T MRI was used to obtain 3D GEPCI, MPRAGE, and FLAIR sequences. AssessmentTechnical quality of the AI-generated contrasts was evaluated against directly acquired MRI images using structural similarity index (SSIM). Quantitative accuracy for R2* maps was evaluated using normalized root-mean-square error (NRMSE). Clinical image quality was assessed by expert physicians. Lesion volumes and counts were obtained using automated segmentation. ResultsAI-generated FLAIR and MPRAGE images achieved mean SSIM values of 0.923{+/-}0.028 and 0.935{+/-}0.022, respectively. The generated R2* maps achieved a mean SSIM of 0.996{+/-}0.006, with quantitative accuracy reflected by an NRMSE of 0.031{+/-}0.020. Physicians rated GEPCI-FLAIR images at 4.2 and GEPCI-MPRAGE images at 4.5 (on a 1-to-5 scale), both exceeding the clinically routine standard of 4.0. Lesion volume and count comparisons from automated segmentation showed strong agreement between AI-generated and ground-truth measurements (R{superscript 2}=0.988 and R{superscript 2}=0.933, respectively). ConclusionAI-GEPCI generated multiple clinically relevant MRI contrasts from a single GEPCI acquisition with high similarity to corresponding acquired images. Radiological reviews and quantitative analyses supported the feasibility of producing high-quality, intrinsically co-registered multi-contrasts for comprehensive brain evaluation.

Portable low-field MRI offers an affordable and mobile alternative to conventional high-field scanners, enabling imaging in point-of-care and resource-limited settings. However, its lower signal-to-noise ratio, reduced resolution, and acquisition artifacts raise concerns about the accuracy of standard image registration methods. Reliable registration is critical for a wide range of emerging applications, including frequent brain monitoring, assessment of neurodegenerative disease progression, and evaluation of treatment effects such as those of Alzheimers therapeutics. In this work, we systematically evaluated state-of-the-art registration approaches on simulated low-field scans (obtained by downsampling high-field images) and on real low-field brain MRI data. We compared three representative approaches: classical optimization (NiftyReg), learning-based registration (SynthMorph), and synthesis-based registration (SynthSR+NiftyReg). Using downsampled high-field scans, all methods performed well, achieving high Dice scores and smooth deformation fields, indicating that reduced resolution alone does not hinder registration. In contrast, real low-field data exhibited lower accuracy, primarily due to geometric distortion and other acquisition-specific artifacts. Among the tested approaches, the synthesis-based pipeline achieved the most robust performance across subjects and modalities. Overall, existing algorithms can accommodate resolution limitations, however, future methods could further enhance coregistration by explicitly addressing the distortions present in low-field MRI scans.

Accurate vessel segmentation is essential for reliable hemodynamic quantification in 4D Flow MRI. Automated segmentation with deep learning offers a promising alternative to the time-consuming, operator-dependent manual segmentation, but its application is often hindered by the scarcity of labeled datasets. Moreover, the impact on downstream hemodynamic quantification remains to be investigated. We developed a transfer learning-based intracranial artery segmentation model using a 3D full-resolution nnU-Net, pretrained on 355 TOF-MRA scans and fine-tuned on 11 7T 4D Flow MRI scans. The model was compared with two published models (U-Net and DenseNet U-Net) against the manual reference, evaluating segmentation metrics on test sets of different resolutions and hemodynamic quantification. The proposed nnU-Net achieved the highest Dice score (>0.85), the lowest HD95 ([~]3 mm), and the highest ICCs in cross-sectional area (0.62-0.87, except PCAs) and mean blood flow (0.78- 0.98). For wall shear stress (WSS) quantification, nnU-Net segmentations achieved the closest agreement with the manual reference (mean = 1.57 {+/-} 0.63 Pa, ICC = 0.96; max = 2.16 {+/-} 1.05 Pa, ICC = 0.97) and minimal bias ([&le;] 1.7%), whereas U-Net and DenseNet U-Net showed systematic under-(-5%) and overestimation (+7%), respectively. However, several vessel segments, including the ACA for DenseNet U-Net and the BA for U-Net, showed statistically significant differences (ANOVA post-hoc correction P < 0.05) in the flow-related metrics when compared with the manual reference. These results demonstrate that transfer learning with nnU-Net provides a robust, fully automated solution for intracranial artery analysis, and that segmentation accuracy directly affects 4D Flow MRI-derived hemodynamic quantification.

Brain age is a promising biomarker for detecting atypical and pathological brain aging, but its accuracy and reliability depend critically on MRI quality. The impact of common MR image degradations such as motion, ghosting, blurring, and noise on brain age predictions remains unclear. In this study, we systematically assessed the effects of four simulated MRI artifact types, across ten severity levels, on brain age prediction using three widely used deep learning-based algorithms (Pyment, MIDI, MCCQR), in high-quality T1-weighted images of healthy adults (age range 18-85, 54% female). Artifact severity levels (1-10) were generated using a power-function mapping of TorchIO simulation parameters calibrated to the full PondrAI QC visual rating scale (from perfect to severely degraded image quality). Linear mixed-effects models with predicted brain age as dependent variable revealed a significant interaction between algorithm, artifact type, and severity (p<0.001), indicating algorithm-specific sensitivity to artifacts. In artifact-free scans, mean absolute error (MAE) was 4.6 years for MCCQR, 7.1 years for Pyment, and 9.1 years for MIDI. At severity level 10, MAE increased with up to 110% for Pyment, 112% for MCCQR, and 16% for MIDI (motion); and with up to 75% for Pyment, 135% for MCCQR, and 34% for MIDI (ghosting). Blurring had minimal impact at low-moderate levels, but at maximum severity MAE increased by 26% for Pyment and 137% for MCCQR, while MIDI remained largely stable. Noise minimally affected Pyment and MCCQR (MAE increases [&le;]20%), but led to larger declines for MIDI (MAE increase 35%). The vulnerability of different algorithms highlights that training data, preprocessing strategies and underlying architectures influence robustness, emphasizing that artifact sensitivity is a key consideration when interpreting brain-age as a biomarker. Our results emphasize the need for artifact-aware evaluation and mitigation strategies when algorithms such as brain age are used in clinical research.

NEXI is a gray matter (GM) microstructural model designed to probe brain tissue microstructure in vivo using diffusion MRI. NEXI describes GM as two exchanging Gaussian compartments - neurites, modeled as randomly oriented, infinitely long sticks, and the extracellular space - allowing the estimation of biophysically interpretable parameters related to neurite microstructure and intercompartmental exchange. While modeling cell processes as sticks and each compartment as Gaussian are common assumptions for brain biophysical models of diffusion, neurite structural irregularities and the presence of somas, particularly in GM, may violate them and bias NEXI parameter estimates. Furthermore, the barrier-limited exchange assumed in the Karger model that underlies NEXI may also be violated in realistic conditions. Therefore, in this work, we evaluate NEXIs accuracy in numerical substrates that incorporate realistic GM features and membrane permeability. To this end, we generated several GM-like substrates with neurite beading, undulation, orientation dispersion, and somas across a range of membrane permeabilities. Diffusion signals were generated with Monte Carlo simulations of water diffusion and subsequently fitted with NEXI. Overall, NEXI accurately recovered exchange times across permeability levels and successfully disentangled exchange effects from other microstructural features, showing only minor bias in estimates from the realistic geometries. These results support its potential for in vivo GM microstructure mapping and studies of brain disorders.

Background and PurposeMagnetic resonance imaging (MRI) for radiation therapy treatment planning is currently being used in many anatomical sites to better visualize soft tissue landmarks, a technique known as an MRI simulation. A core component of modern MRI simulation configurations are the use of external laser positioning systems (ELPS) to help set up the patient. Though necessary for accurate and reproducible patient setup, the ELPS, if left on during imaging, may interfere negatively with image quality due to leaking electronic noise, of which MRI is sensitive to. It is currently unknown whether this leakage of electronic noise may further affect quantitative values derived from clinically employed relaxometric, diffusion, and fat fraction sequences. Therefore, in this study, we aim to characterize the impact of MRI simulation lasers on general image quality and quantitative imaging accuracy. Materials and MethodsFirst, a cine acquisition was used to visualize the real-time changes in image signal-to-noise ratio (SNR) from when the ELPS was deactivated to activated. To validate this effect quantitatively, the SNR was measured using the American College of Radiology (ACR) recommended protocol in a homogeneous phantom with the integrated body, 18-channel UltraFlex small, 18-channel UltraFlex large, 32-channel spine, and 16-channel shoulder coils. Next, a geometric distortion algorithm was tested in two vendor-provided phantoms while using the integrated body coil and the ACR Large Phantom protocol was tested. Finally, a series of quantitative MRI scans were performed using a CaliberMRI Model 137 Mini Hybrid phantom to validate quantitative T1, T2, and ADC while a Calimetrix PDFF-R2* phantom was used for quantitative PDFF and R2*. All scans were performed with both the ELPS both deactivated and activated. ResultsVisible electronic noise artifacts were seen when using the integrated body coil when the ELPS was activated on the cine acquisition which led to a four-fold decrease in SNR using the ACR protocol. This SNR drop was not seen when using the remaining tested coils. The automatic fiducial detection algorithm was affected negatively by ELPS activation leading to misidentification when identified perfectly with the ELPS deactivated. Degradation in image intensity uniformity, percent signal ghosting, and low contrast object detectability was seen during ACR Large Phantom testing using the 20-channel Head/Neck coil. Concordance across quantitative MRI values was similar when the ELPS was both deactivated and activated while a consistent increase in standard deviation inside the ADC vials was seen when the ELPS was activated. DiscussionThe extra noise induced from the activation of the ELPS during imaging should be avoided due to its potential to unnecessarily increase image noise. This is particularly true when conducting mandatory quality assurance testing for image quality and geometric distortion which utilize the integrated body coil which is most susceptible to ELPS-induced noise. Clear clinical guidelines should be implemented to make this issue known to the MRI technologists, physicists, and other relevant staff using an MRI with a supplementary ELPS for patient alignment. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/26347809v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@dd725borg.highwire.dtl.DTLVardef@7ed081org.highwire.dtl.DTLVardef@1aac775org.highwire.dtl.DTLVardef@10ce397_HPS_FORMAT_FIGEXP M_FIG C_FIG

Network neuroscience provides a powerful framework for studying the mechanisms underlying brain-related diseases. As analyses become increasingly computational, ensuring their numerical reliability has become a critical challenge. Small perturbations introduced during processing can propagate through complex pipelines, leading to variability in outcomes and raising concerns about the reproducibility of reported findings. Addressing this issue requires systematic evaluation of pipeline stability to ensure results remain within acceptable numerical limits. While the numerical variability of structural imaging workflows has been investigated, with findings ranging from negligible to substantial, functional MRI (fMRI) pipelines and their derived graph measures remain underexplored. Without rigorous stability assessment, conclusions drawn from these measures may remain uncertain. We systematically evaluated the numerical variability of graph measures of functional connectivity derived from the widely-used fMRIPrep pipeline and compared it to population variability. The resulting Numerical-Population Variability Ratio (NPVR) values typically ranged from 0.1 to 0.2 for most graph metrics, indicating a measurable influence of numerical variability on network-derived outcomes. NPVR values varied across brain regions, thresholding choices, and confound regression strategies. These findings highlight numerical variability as an important factor in functional network studies, particularly when examining subtle effects or working with small sample sizes.

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.

Accurate registration of regions of interest (ROIs) from standard atlases to participants native spaces is a critical step in fMRI studies, as it directly affects the reliability of sampled BOLD signals. While T1-weighted (T1w) image-based ROI registration is well validated and widely adopted in cortical fMRI, its performance degrades in brainstem studies due to the small size, dense packing, and poor visibility of brainstem nuclei on T1w contrast. We hypothesized that incorporating diffusion MR images, containing more information about internal brainstem architecture, should improve ROI registration accuracy. To test this, we developed four registration pipelines that either included or excluded diffusion-based alignment components and evaluated their performance using data from n=20 healthy participants. Registration accuracy was assessed using Dice coefficient for the red nucleus (RN) and the substantia nigra (SN), and mis-registration fraction--a metric developed for nuclei that cannot be manually delineated--for the dorsal raphe nucleus (DRN). The results showed that diffusion-based pipelines, using fractional anisotropy (FA) images, non-diffusion-weighted (b0) images, and multivariate combination, outperformed the T1w-only baseline. Probabilistic maps derived from inverse-transformed native ROIs further supported improved sensitivity to inter-individual anatomical variability in the diffusion-augmented pipelines. In addition, analysis of gradient magnitude maps from the Jacobian determinants revealed associations between localized deformation and image modality-specific landmarks. These findings demonstrate the potential of diffusion-augmented pipelines for improving brainstem ROI registration, which could enhance the robustness of fMRI studies on brainstem disorders characterized by functional dysregulation.

Functional MRS can measure the neurometabolic response to neuronal activation, therapeutic interventions and changes in physiology. Substantial technical challenges currently present a barrier to reproducible findings and broader adoption by the neuroscientific community. One such challenge is the conflation between genuine metabolic changes and bias caused by subtle spectral lineshape changes associated with the BOLD response. Previous studies have demonstrated an approximately 1% bias for glutamate estimates at 7T based on experimentally acquired data and a single conventional fitting algorithm. In this study, we use synthetic MRS data to estimate the bias for two conventional fitting methods (LCModel and ABfit-reg) at 3T and 7T and evaluate the efficacy of dynamic lineshape adjustment, during preprocessing and fitting analysis stages, to reduce bias. Using the same dataset, we also explore the potential bias in 2D fitting approaches, comparing several fitting models implemented in FSL-MRS. Bias between two conventional fitting methods without explicit linewidth correction was similar ([~]1% for glutamate) and in good agreement with previous experimental studies at 7T. Lineshape changes from the BOLD response cause similar bias in conventional and 2D fitting packages for 3T and 7T data, resulting in an overestimation of metabolic changes associated with neuronal activation. This bias may be significantly reduced (<0.2%) by incorporating a BOLD linewidth matching step for conventional analysis or by direct modelling for 2D analysis. We therefore recommend explicit BOLD lineshape correction or modelling for future task-based fMRS studies at 3T and above.

Cerebrovascular reactivity (CVR), the ability of cerebral blood vessels to dilate or constrict in response to a vasoactive stimulus, is a clinically meaningful measure of cerebrovascular health. Head motion and other noise sources substantially impact CVR quality, particularly in clinical populations. In this study, we evaluated multi-echo fMRI techniques, including optimal combination of echoes (ME-OC) and multi-echo independent component analysis (ME-ICA), for improving CVR quality relative to single-echo fMRI in participants with stroke. In a breath-hold fMRI dataset, ME-OC significantly improved CVR quality metrics and reduced the percentage of negative CVR values in normal-appearing gray and white matter (p<0.05). ME-ICA reduced the dependence of BOLD signals on head motion but did not improve CVR quality metrics. In a separate resting-state dataset, ME-OC effects were largely consistent with the breath-hold dataset, but ME-ICA also significantly improved CVR quality metrics and reduced negative CVR values in normal-appearing gray and white matter relative to ME-OC (p<0.05). These findings demonstrate that multi-echo fMRI can improve CVR estimation in clinical populations, particularly in low signal-to-noise datasets, enhancing the feasibility of CVR analyses in stroke studies and allowing for better visualization of stroke-related CVR deficits.

IntroductionStrain tensor imaging (STI) provides precise measurements of brain tissue deformation caused by cerebral arterial pulsations (CAP). This CAP-related brain tissue deformation is expressed in quantitative strain metrics, such as volumetric strain and octahedral shear strain, which hold promise as quantitative markers of the (mechanical) properties of both the intracerebral vasculature and the intervascular tissue components. However, the extent to which these strain metrics can be specifically linked to the underlying anatomical vascular and tissue properties remains largely unknown. This study aims to explore the relationship between STI metrics and independent markers of pulse pressure (arterial transit time, ATT), vascular function (cerebral blood volume, CBV; cerebral blood flow, CBF; mean transit time, MTT), and tissue properties (shear stiffness). MethodVolumetric and octahedral shear strain were computed from previously obtained 7T displacement data (approximately 2 mm isotropic resolution) of eight healthy subjects (27{+/-}7 years). Shear stiffness maps were generated from the same displacement data set using poroviscoelastic intrinsic MR elastography. Regional values of CBV, CBF, MTT, and ATT were obtained from standard-space atlases. Linear mixed-effects models were used to investigate potential regional relationships between specific strain metrics and the corresponding tissue, pulse pressure, or vascular markers. ResultsVolumetric strain showed significant positive correlations with CBV (globally, cortical gray and white matter) and significant negative correlations with ATT (globally, and in cortical gray and white matter), but not with shear stiffness. Octahedral shear strain showed a significant negative correlation with shear stiffness (globally, in subcortical gray and white matter) and also with ATT (globally, in cortical gray matter). ConclusionVolumetric strain reflects mainly vascular properties (pulse pressure, blood volume), while octahedral shear strain is more sensitive to tissue properties. These findings provide a foundation for future studies that investigate the physiological characteristics reflected by these strain metrics and their intricate interplay.

IntroductionConventional BOLD-fMRI relies on hemodynamic responses that are temporally and spatially indirect markers of neural activity. Developing alternative contrasts, sensitive to neuroelectrical phenomena, is a critical challenge in brain imaging. Spin-lock (SL) fMRI has shown promise in phantom studies for detecting magnetic field changes associated with neuronal activity, but its in-vivo sensitivity and practicality remain unclear. This study evaluated whether SL contrast can effectively detect and localize human neuronal activation, benchmarked against complementary functional modalities, magnetoencephalography (MEG) and 3T BOLD-fMRI, to assess the sensitivity of MR-based neuronal current imaging. MethodsThirteen healthy young volunteers underwent SL-based imaging during 8 Hz visual stimulation, along with BOLD and MEG acquisitions. Subjects viewed quadrant-checkerboard stimuli to elicit localized cortical responses. Two balanced SL contrast mechanisms, rotary excitation (REX) and stimulus-induced rotary saturation (SIRS), were employed. Postprocessing targeted stimulus-locked signal fluctuations using a regression-filtering-rectification strategy. Phantom experiments tested sensitivity and analysis pipeline performance. ResultsMEG revealed robust stimulus-locked responses in occipital cortex, with estimated local magnetic field amplitudes of [~]0.07 nT. Conventional BOLD-fMRI confirmed reliable hemodynamic activation. In contrast, neither balanced REX nor balanced SIRS produced consistent stimulus-related activation in vivo. Phantom experiments subsequently yielded detection thresholds of 0.2 nT for REX and 0.6 nT for SIRS, exceeding the MEG-estimated physiological field amplitudes. ConclusionsUnder the present experimental conditions, the tested spin-lock fMRI implementations did not achieve sufficient sensitivity for reliable in-vivo detection of neuronal magnetic fields at 3T. Phantom and MEG-based estimates indicate that physiological field amplitudes in the visual cortex lie below current detection limits. These findings establish quantitative constraints on direct neuronal current imaging with MRI and provide a benchmark for future methodological developments aimed at bridging electrophysiology and functional MRI. Key pointsO_LIWe assessed spin-lock fMRI sensitivity using combined SL-fMRI, BOLD-fMRI, MEG, and phantom measurements during visual stimulation. C_LIO_LIMEG and BOLD-fMRI confirmed robust neuronal and hemodynamic activation in the visual cortex. C_LIO_LISL-fMRI did not achieve reliable in-vivo detection of neuronal magnetic fields; phantom sensitivity limits exceeded MEG-estimated physiological field amplitudes. C_LI

Post mortem MRI studies of formalin-fixed brain tissue are essential for linking in vivo MRI contrast to underlying microstructure measured with ex vivo histology, yet formalin not only preserves tissue but also systematically alters MRI-relevant physical properties. To systematically quantify and model these effects, we longitudinally characterized multi-parametric mapping (MPM) measures -- longitudinal (R1) and effective transverse (R2*) relaxation rates, proton density proxy (NA), and magnetization transfer saturation ratio (MTsat) -- across the different post mortem processes, i.e. autolysis, fixation, and hydration. Five whole-human brains were scanned longitudinally during fixation (and in situ-after rehydration, when available), and compared with an independent in vivo cohort of 25 younger healthy participants. Each MPM parameter followed a distinct trajectory across different post mortem processes. The largest changes were found for R1 during fixation relative to in situ values (more than 250%), followed by R2* with an almost 60% increase, and MTsat with a 26% reduction from in vivo to in situ. NA showed no detectable change during fixation. We developed models describing fixation-induced changes and tissue shrinkage. The R1 changes and tissue shrinkage were closely aligned, reflecting a likely common mechanism. MTsat largely preserved tissue contrast during fixation and rehydration, supporting its use for spatial alignment between in vivo MRI, fixed-tissue MRI, and histology. With our quantitative assessment of post mortem process-dependent changes we provide a unique resource for future studies to better link in vivo to fixed post mortem MRI data and thereby bridge the gap to ex vivo histology.