NMR in Biomedicine

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.

Sodium (23Na) magnetic resonance imaging (MRI) provides valuable metabolic information, but it is limited by a low signal-to-noise ratio (SNR) and long acquisition times. To overcome these challenges, we present a Deep Image Prior (DIP)-based framework that combines anatomically guided proton (1H) MRI and metabolically guided 23Na MRI denoising via a fused proton-sodium prior within a directional total variation (dTV) regularization scheme. The DIP-Fusion approach minimizes a variational loss function combining data fidelity, fused dTV regularization, gradient consistency, and bias-field correction to reconstruct sodium images. MRI data were acquired from healthy volunteers and breast cancer patients. Healthy datasets were retrospectively undersampled at multiple factors, and fully sampled scans served as the ground truth. Patient datasets acquired for clinical purposes were reconstructed using the baseline DIP and the proposed DIP-Fusion methods. Sodium images were reconstructed using sum-of-squares (SoS) and adaptive combined (ADC) coil combination methods. We evaluated reconstruction performance using quantitative image quality metrics, including peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), mean squared error (MSE), learned perceptual image patch similarity (LPIPS), feature similarity index (FSIM), and Laplacian focus. In healthy volunteers, DIP-Fusion outperformed state-of-the-art reconstruction techniques across all undersampling factors. In patient datasets, DIP-Fusion demonstrated superior performance compared with baseline DIP, achieving improved structural fidelity and sodium-specific signal preservation. These results demonstrate the potential for robust, highquality sodium MRI reconstruction under accelerated acquisition, which could lead to reduced scan times and enhanced clinical feasibility.

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.

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.

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).

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

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.

PurposeEarly noninvasive assessments of treatment response are desperately needed to improve outcomes in glioblastoma (GBM). Molecular imaging techniques that measure glycolytic metabolism are being increasingly studied, but limitations such as variable substrate delivery present significant barriers to clinical interpretation. To develop more robust translational imaging biomarkers, we propose utilizing the interrogation of oxidative stress, a critical component of tumor metabolism for which no method of clinical measurement currently exists. This study investigates the simultaneous measure of oxidative stress and glycolytic flux using co-hyperpolarized [1-13C] dehydroascorbate and [1-13C] pyruvate (HP DHA/PA) as a predictor of treatment response in GBM. Experimental DesignTo establish a model that exhibits known metabolic responses to oxidative stress, we characterize radiation induced metabolic reprogramming in four human GBM lines (U87, U251, A172, T98) in vitro. We extend this in vivo and establish radiosensitive and radioresistant orthotopic xenograft models to investigate HP DHA/PA magnetic resonance imaging as a predictor of treatment response. ResultsIn vitro analyses revealed that radiation upregulates the pentose phosphate pathway and response is augmented by glutathione depletion. In vivo metabolomic profiling identified preferential nucleotide metabolism pathways in each tumor type. HP DHA/PA imaging revealed that DHA perfusion was not impacted by blood-brain-barrier integrity and detected reductions in DHA-to-vitamin C and pyruvate-to-lactate conversion in treatment-sensitive tumors, reflecting diminished reductive capacity following radiation. ConclusionsThese findings demonstrate successful prediction of radiosensitivity in GBM utilizing measurement of oxidative stress and establish HP DHA/PA imaging as an innovative method to address existing clinical limitations in treatment response assessment.

PurposeNon-water-suppressed proton spectroscopy, 1H-MRS, is desirable, as retaining the strong water resonance can facilitate automated online data corrections, internal concentration referencing, and monitoring of line narrowing effects in functional MRS. Removal of the water suppression module can also mitigate magnetization transfer effects and slightly reduce the minimum achievable TR and total RF power deposition. However, water suppression is typically considered essential due to eddy current-induced antisymmetric sidebands on the water resonance that distort the spectral baseline and obscure metabolite signals. Theory and MethodsThe Gradient Impulse Response Function (GIRF) was used to predict time-dependent magnetic field perturbations during the FID that generate the artefactual sidebands. The GIRF was measured in a one-time calibration, independent of spectroscopy acquisitions, enabling post-processing correction of the sidebands without sequence modification or additional dedicated hardware. GIRF-corrected non-water-suppressed single-voxel-spectroscopy (SVS) was compared to otherwise identical water-suppressed acquisitions in eight participants at 3T using semi-LASER and MEGA-PRESS sequences. ResultsAcross participants, GIRF correction reduced sideband amplitudes to levels comparable with the spectral baseline, enabling recovery of the underlying metabolite signals. Systematic increases in quantified metabolite concentrations were observed relative to water-suppressed acquisitions, consistent with water-suppression-induced magnetization transfer effects. Total creatine exhibited the largest increase, with enhancement ratios of 1.069{+/-}0.039 for MEGA-PRESS and 1.535{+/-}0.160 for semi-LASER acquisitions. ConclusionGradient-induced artefactual sidebands in non-water-suppressed MR spectroscopy can be effectively corrected using the GIRF to predict time-dependent magnetic field perturbations during the FID. In principle, the approach extends to other SVS sequences and field strengths following appropriate GIRF calibration.

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.

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.

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.

Background & AimsThe increasing global prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) including metabolic dysfunction-associated steatohepatitis (MASH) creates an urgent need for objective methods of histopathological assessment. Conventional histological approaches are time-consuming and rely on interpreters experience. Therefore, the results obtained may suffer from high variability and only offer coarse categorisation. In this study, we propose a fully automated, deep-learning-based pipeline for the segmentation and characterisation of histological liver features for MASH/MASLD assessment. MethodsSegmentation was applied to H&E sections from 45 mice and 44 humans with MASH/MASLD. The method, which we named qHisto (quantitative histology), utilises the nnU-Net framework and quantifies key histological components of the MASH score, including macro- and microvesicular steatosis, fibrosis, inflammation, hepatocellular ballooning and glycogenated nuclei. Additionally, we characterized the tissue using novel features that are inaccessible through manual histology, such as the distribution of fat droplet sizes, aspect ratio of nuclei and heatmaps. ResultsqHisto parameters showed strong positive correlations with conventional histology scores (fat area R=0.91, inflammation density R=0.7, ballooning density R=0.49) and also with quantitative magnetic resonance imaging (fat area vs. hepatic fat fraction R=0.87). Our novel scores showed that deformation of nuclei is driven by large fat droplets rather than the overall amount of fat. ConclusionsA key advantage of our method is spatially resolved, precise histological quantification. These features provide a finely resolved assessment of disease severity than conventional categorical scoring. By automating time-consuming and repetitive readouts, qHisto improves standardisation and reproducibility of MASH/MASLD feature quantification and provides scalable, slide-wide readouts that can support histopathologists and enhance clinical assessment and therapeutic development. Impact and ImplicationsThe proposed method provides an objective, automatic tool for comprehensive, histological liver analysis of MASH/MASLD, which can be extended to other diseases and organs. By offering classic and novel quantitative parameters and scores, our method could support histologists in their daily routines and provide researchers with further insight into steatotic liver diseases.

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 ([&ge;]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.

Phosphorus magnetic resonance spectroscopy (31P-MRS) enables non-invasive measurement of brain metabolism, yet its reproducibility in clinical settings remains unclear. We systematically assessed intra- and intersession variability as well as inter-individual differences of key phosphorus metabolites at 3 Tesla in healthy individuals and persons with Parkinsons disease under various experimental condition. Intersession variability, as measured by coefficients of variation (CoV) increased notably for longer scan intervals ([~]1 year), and metabolite ratios from well-resolved spectral signals (i.e., adenosine triphosphate (ATP), phosphocreatine (PCr), intracellular inorganic phosphate Pi) exhibited consistently higher stability compared to ratios calculated from metabolite signals overlapping on the spectrum (e.g., total nicotinamide adenine dinucleotide (tNAD), as well as phosphate monoesters (PMEs) and phosphate diesters (PDEs). Test-retest variability ranged from [~]5-25 CoV%, where PCr, ATP- and ATP-{gamma} were the most stable while glycerophosphocholine (GPC), glycerophosphoethanolamine (GPE), phosphoethanolamine (PE) and tNAD varied considerably. Inter-individual variability was found to be higher than intra-individual variability for all metabolite ratios, ranging from [~]9-33 CoV%. By systematically quantifying intra-individual and inter-individual variability, as well as providing explicit sample-size recommendations, this study facilitates more reliable longitudinal and cross-sectional clinical trials and translational studies of brain metabolism featuring 31P-MRS.

Non-invasive measurement of neurotransmitter-specific glucose metabolism in the human brain remains a major challenge, limiting mechanistic insight into excitatory-inhibitory imbalance across neurological and psychiatric disorders. Current methods lack the ability to selectively and precisely resolve neurotransmitter-specific metabolic pathways, particularly GABA, while remaining compatible with clinically feasible acquisitions. Here, we introduce a clinically compatible approach which enables targeted and non-invasive detection of glutamate, GABA, and lactate metabolism in the human brain. Called proton-observed proton-edited {superscript 1}3C-magnetic resonance spectroscopy (POPE-{superscript 1}3C-MRS), the method uses an exogenous {superscript 1}3C-glucose probe combined with standard proton radiofrequency hardware and widely available MR pulse sequences. We use a cross-species validation framework to first calibrate POPE-{superscript 1}3C-MRS in mice and then demonstrate its feasibility in humans at ultra-high field. While in vivo GABA labelling has been previously reported, POPE-{superscript 1}3C-MRS provides, for the first time, robust access to GABAergic metabolism using standard MRI hardware, feasible within clinical constraints, including applicability to deep brain regions. By refining existing indirect {superscript 1}H-{superscript 1}3C-MRS strategies and enabling targeted probing of excitatory and inhibitory metabolic pathways, POPE-{superscript 1}3C-MRS opens new opportunities for studying neurometabolic coupling and excitatory-inhibitory balance in vivo, with broad implications for translational and clinical neuroscience.

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.

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.

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.

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.