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.

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.

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.

The fibrous microstructure of tendons and ligaments is an important determinant of their mechanical behavior and integrity. Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that enables the inference of microstructural features within fibrous tissues and has recently been used to characterize the microstructure of dense connective tissues such as tendon and ligament. However, the effect of microstructural variations in tendon and ligament on DTI metrics remains unclear. To address this gap, we simulated diffusion MRI of second harmonic generation (SHG) image-informed square lattice fiber networks to determine which microstructural features have the strongest influence on DTI metrics. Then, we performed a second set of diffusion MRI simulations for randomly dispersed fibers within synthetic tendon volumes to relate DTI metrics to the influential microstructural features, including fiber dispersion. All DTI metrics were insensitive to collagen fiber crimp. Fiber dispersion did not affect mean diffusivity, decreased axial diffusivity, increased radial diffusivity, and decreased fractional anisotropy. These results provide valuable insight into the relationships between DTI metrics and microstructural properties of tendon and ligament, which is particularly relevant for inferring microstructural changes in impaired tissue using DTI. Furthermore, our findings are an important step in the translation of DTI for clinical and computational studies of dense connective tissues such as tendon and ligament.

BACKGROUNDThe cardiac Purkinje network plays a critical role in maintaining synchronized activation of ventricles but remains challenging to image due to its fine and unique structure. Conventional MRI techniques lack sufficient contrast to distinguish the underlying structural composition of Purkinje Fibers (PF). PURPOSEThis study investigates the potential of inhomogeneous Magnetization Transfer (ihMT) as a novel contrast mechanism for visualizing and differentiating subregions of the PF. METHODSFive fixed ex-vivo sheep hearts (n = 5) containing free running PF were scanned with a 9.4T MRI using a 2D ihMT RARE sequence. ASSESSMENTihMTR maps were analyzed using manually defined regions-of-interest (ROIs) corresponding to free-running PF, insertion points, and myocardium. Histological analysis (light and polarized microscopy) was performed on matched sections to quantify collagen types I and III, adipocytes, Purkinje cells, and cardiomyocytes. RESULTSThree ihMT protocols, which produced high ihMTR values in free-running PF (9.25-10.83%) and strong absolute contrast relative to the myocardium (2.00-2.17%) and insertion points (2.99-3.40%) in one sample were selected and applied to all samples. Across all samples, mean ihMTR in free-running was consistently higher than in insertion points (11.5 {+/-} 1.5% vs. 9.0 {+/-} 2.9%). Histological analysis revealed a significantly greater collagen content in free-running regions compared with insertion points (72.4 {+/-} 15.9% vs. 31.1 {+/-} 13.1%; p = 0.001), along with higher adipocyte content at insertion points vs. free-running regions (12.3 {+/-} 6.1% vs. 3.8 {+/-} 2.7%, non-significant). Collagen type III was more prominent at insertion points but remained a minor component overall. CONCLUSIONihMT imaging can distinguish PF subregions based on microstructural differences, particularly collagen and adipocyte distribution. This study lays the groundwork for developing biophysical models to interpret ihMT signals in terms of tissue composition and microstructure, providing a foundation for future studies. SponsorThis study received financial support from the French Government by the National Research Agency (ANR; SYNATRA ANR-21-CE19-0014-01) and Region Nouvelle Aquitaine (convention N{degrees}AAPR2022-2021-16609210).

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.

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

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.

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.

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.

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.

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.

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.

Brain-wide, noninvasive methods that directly resolve neuronal activity on millisecond timescales are still lacking in neuroimaging. Hemodynamic fMRI (1H-based) provides whole-brain maps of activity, yet its vascular origin creates spatial and temporal displacements from neuronal events that complicate interpretation, especially in disease conditions where neurovascular coupling is altered. Here, we developed an ultrafast 23Na fMRI platform at 14 T that combines a reshuffled k-t 3D gradient echo readout (TR/TE, 10ms/1ms) with an implantable RF coil and respiration-gated acquisition. This configuration provides the sampling rate and SNR needed to probe quadrupolar T2*-weighted single quantum sodium dynamics on the neuronal timescale. Across rats and mice, somatosensory forepaw stimulation produced a localized 23Na signal decrease of ~2-3% in the FP-S1, peaking ~10-30 ms post-stimulus. The activity pattern is well matched with conventional BOLD-fMRI maps acquired in the same animals. Trial-by-trial measurements during simultaneous iGluSnFR glutamate fiber photometry demonstrated that larger evoked glutamate transients coincided with larger NARS decreases, supporting a neuronal origin of the NARS contrast. We interpret the negative NARS response as a transient activity-dependent redistribution of sodium ions toward restricted, protein-rich microdomains, where more restricted rotational dynamics may accelerate T2*short decay and produce a 2-3% signal decrease without requiring large changes in bulk sodium concentration. Together, these results establish neural activity-related sodium (NARS) fMRI as a viable approach for direct, mesoscale neuronal mapping with MRI at millisecond resolution. Graphical AbstractA reshuffled k-t 3D gradient echo (GRE) readout with an implantable figure-8 RF coil and respiration-gated trials captures a ~2-3% negative 23Na response peaking 10-30 ms after forepaw stimulation in the FP-S1. NARS fMRI responses co-localize with BOLD-fMRI and scale with iGluSnFR glutamate transients, consistent with activity-dependent shifts toward short-T2* sodium microenvironments. Table of Contents (TOC) BlurbYu et al. introduce NARS-fMRI, an ultrafast 23Na method that resolves millisecond neuronal dynamics in rodent FP-S1. The negative sodium response (10-30 ms) co-localizes with BOLD and correlates with glutamate photometry, supporting a direct neuronal origin.

IntroductionConcurrent TMS-fMRI allows to observe how stimulation affects the target region and connected brain-wide networks. However, hardware limitations represent a major constraint: standard MR head coils provide high imaging sensitivity but no room for the TMS coil, whereas available TMS-compatible MR head coils offer access but low or strongly inhomogeneous signal. MethodsWe developed and benchmarked a flexible "Sushi" MR-receive setup, assembled from two repurposed 18-channel body arrays, that allows TMS coil positioning while maintaining full-brain coverage. Resting-state (n = 12) and task-based working memory (n = 8) fMRI were acquired with the Sushi coil, a commercially available 2x7-channel Surface-coil setup, and a standard Siemens 64-channel (non-TMS-capable) head coil. The image-quality cost of TMS capability was addressed by acquiring multi-echo fMRI to allow post-hoc optimization of signal-to-noise ratio (SNR), but no TMS was delivered. ResultsFrom resting-state fMRI, the Sushi mapped known canonical resting-state networks (RSNs) comparably to the 64-channel reference and was superior to the Surface coils, in particular for the default-mode, auditory, and visual networks. Task-fMRI data showed that Sushi recovered the working memory network more similarly to the 64-channel reference than the Surface coil. Temporal SNR was optimized for all coil acquisitions yielding [~]30-50% gain and improving between-coil comparability for RSNs identification and task-related activity. ConclusionsTogether, post-hoc multi-echo optimization and the Sushi coil setup provide a low-cost, ready-to-use solution for whole-brain concurrent TMS-fMRI recordings. Combining stimulation access with reliable functional network readouts is essential to probe mechanisms of TMS and to inform effective TMS interventions targeting altered brain systems.

Chemical shift-encoded magnetic resonance imaging using high-resolved 3D Dixon techniques enables the non-invasive and radiation-free assessment of whole-body adipose tissue and ectopic fat distribution. Automatic deep learning-based segmentation of metabolically relevant adipose tissue compartments and ectopic fat deposits in parenchymal tissue is the most important image processing step for the quantification of adipose tissue volumes and ectopic fat percentages from whole-body imaging. This work presents a segmentation model dedicated to the segmentation of 19 metabolically relevant adipose tissue compartments and ectopic fat deposits from whole-body Dixon MRI. The trained segmentation model is available upon request. Related post-processing routines to compute volumes and fat percentages are publicly available: https://github.com/tobihaui/WholeBodyATQuantification.

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.

IntroductionGlioblastoma is characterized by heterogeneous tumor characteristics and infiltrative tumor boundaries, making accurate delineation difficult with extensive manual annotations. Chemical exchange saturation transfer (CEST) is a non-invasive MRI technique used for in vivo assessment of metabolic and macromolecular information through a Z-spectrum. CEST may provide insight into metabolic changes present in early-stage disease that are not visible in routine clinical imaging, thereby improving tumor delineation. In this work, we use an unsupervised anomaly detection (UAD) strategy to learn the distribution of features present in Z-spectra of healthy tissue and capture their deviations in pathology, foregoing the need for extensive labels. The approach leverages the metabolic information provided by CEST to improve the detection and delineation of glioblastoma and inform further treatment planning. MethodsA 1D convolutional autoencoder (CAE) was implemented to reconstruct Z-spectra from individual tissue voxels. The network was trained on Z-spectra acquired at 9.4T from healthy Sprague-Dawley rats and tested on data acquired from F98 glioma-bearing rats post Gd-administration. For baseline comparisons, Isolation Forest and Local Outlier Factor, which have shown success in anomaly detection, were implemented. For the CAE, our anomaly score was determined to be the mean squared reconstruction error. To facilitate clinical translation and evaluate the robustness of our model for under sampled Z-spectra, acceleration factors of 2x and 7x were performed with two sampling schemes: uniformly skipping frequency offsets and selecting offsets based on feature importance identified by Shapley value analysis and Integrated Gradients (IG). Binarization was performed by determining an optimal anomaly threshold, followed by comparison to ground truth tumor masks. Metrics related to model performance were assessed for baseline anomaly detectors on the fully sampled dataset and for the CAE on fully and under sampled datasets. ResultsThe best baseline anomaly detector was Isolation Forest, with an ROC-AUC of 0.967 and an F1-score of 0.584. Our method, the CAE, accurately reconstructed Z-spectral features, achieving Dice scores of up to 0.72 and outperforming the baseline model with an ROC-AUC of 0.968 and F1-score of 0.642. This model performance remained robust across sampling schemes and acceleration factors, with ROC-AUCs of [~]0.96 and similar Dice scores (up to 0.7). Feature importance analysis indicated that offsets in the range of {+/-}3.0 to 5.0ppm contributed most to the anomaly score. DiscussionThis study successfully demonstrated a UAD pipeline utilizing the Z-spectrum from CEST MRI for metabolically informed tumor delineation. The framework captures biochemical deviations that may precede or extend beyond morphologic abnormalities, enabling sensitive detection of tumor regions and intra-tumoral heterogeneity that previous methods may fail to capture. The offsets from the feature analysis indicated a strong contribution from the magnetization transfer (MT) pool to the spectral deviations captured by the model, with additional contributions from relayed nuclear Overhauser effect (rNOE) and amide proton transfer (APT). Model robustness with under sampling further highlights the pipelines potential in accelerated acquisitions, thus improving clinical practicality. While there is a need for validation on larger cohorts and clinical datasets, the current results demonstrate that this label-free, Z-spectral anomaly mapping can serve as an interpretable and scalable tool for monitoring tumor heterogeneity and progression, with potential applicability to other diffuse or metabolically subtle pathologies.

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.