NMR in Biomedicine

Background: Gadolinium-based contrast agents (GBCAs) are routinely used in magnetic resonance imaging (MRI). Although macrocyclic GBCAs were initially considered biologically inert, it is now known that a fraction of patients retains gadolinium (Gd) for prolonged periods in tissues such as blood, bone, and brain. Because the first cellular interactions of GBCAs occur in the bloodstream, this study aimed to elucidate the uptake mechanism but also the intracellular persistence and release dynamics of gadoterate meglumine, one of the most widely used macrocyclic agents, in white blood cells (WBCs). Methodology and principal findings: WBCs and K562 cells were incubated with gadoterate meglumine under different conditions to investigate its cellular entry mechanisms. Uptake of the contrast agent was quantified by measuring intracellular Gd using single-cell inductively coupled plasma mass spectrometry (SC-ICP-MS). Time and concentration-dependent incubation of K562 cells revealed saturable uptake kinetics consistent with a Michaelis-Menten model which is independent of the phase of the cell cycle. Gadoterate meglumine uptake in both WBCs and K562 cells was shown to be an active process, as uptake was strongly reduced or abolished at low temperature (16C and 4C) and in the presence of metabolic inhibitors (sodium azide and 2-deoxyglucose). Co-incubation with multiple endocytosis inhibitors (Dyngo 4a, Dynole 2-24 and chlorpromazine) did not significantly decrease intracellular Gd levels in K562 cells and caused only a slight reduction in WBCs, indicating that endocytosis is not the main entry pathway for gadoterate meglumine in these cells. Furthermore, we assessed the retention time of the Gd inside the cells, showing that only after 24 hours post incubation 80% percent of the intracellular Gd was released through an active process. Finally, we demonstrate that one of the mechanisms of Gd release from WBCs involves extracellular vesicles, which may substantially increase its potential for downstream accumulation in different tissues, including immunoprivileged tissues like brain. Significance: The observed time-dependent accumulation, temperature and energy dependence of gadoterate meglumine uptake demonstrate that active cellular mechanisms are primarily responsible for GBCA internalization. Furthermore, our results indicate that macropinocytosis, phagocytosis, and clathrin-mediated endocytosis are not the primary routes of gadoterate meglumine entry. Hereby, we also describe that Gd externalization is an active process involving extracellular vesicles which may influence the Gd distribution in different tissues and its consequent long-term retention. Further studies are required to explore strategies to block this process in order to mitigate potential long-term gadolinium retention.

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

Purpose: {beta}-hydroxybutyrate (BHB), a ketone body and alternative cerebral energy substrate, can be measured in vivo using J-difference edited proton magnetic resonance spectroscopy (1H-MRS). Oral ketone supplementation with substrates such as the ketone monoester (R)-3-hydroxybutyl-(R)-3-hydroxybutyrate (KME) and 1,3-butanediol (BD) have gained attention as a mechanism to elevate circulating BHB and induce ketosis without dietary restrictions. Elevated brain ketone availability is of growing therapeutic interest as a strategy to support neuronal energetics in conditions such as epilepsy, neurodegenerative disease, and alcohol use disorder (AUD). However, both pathways introduce BD into the bloodstream, which crosses the blood-brain barrier. Critically, BD exhibits a spectral signature that closely resembles the prominent BHB peak in JDE-MR spectroscopic imaging (MRSI), identified in a pilot AUD study. Methods: Two separate JDE-MRSI acquisitions tailored for BHB and BD editing were implemented, exploiting frequency separation between the BHB (4.14ppm) and BD (3.95ppm) coupling partners of the observed 1.2ppm resonance to independently quantify each metabolite. Results: Brain BD concentrations (0.25-0.58mM) were comparable to or exceeded corresponding BHB concentrations (0.20-0.27mM) in all volunteers after consumption of a single dose of the KME, indicating that BD constitutes a major fraction of the signal conventionally attributed to BHB. Combined BHB+BD concentrations (~0.45-0.85mM) were consistent with brain BHB values reported in prior studies employing similar doses of the KME, indicating that those measurements likely reflect a combined BHB+BD signal. Conclusions: Separate quantification of the two metabolites is important for interpreting brain ketone studies and for understanding the full pharmacology of KME supplementation.

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

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

Chemical exchange saturation transfer (CEST) has become an indispensable NMR method to characterize slow exchange affecting biomacromolecules, especially for cases involving exchange between a major state and a minor state, the latter of which is often invisible in the spectrum. The CEST method is based on successive irradiation of selective regions of the NMR spectrum using a weak radiofrequency field, B1, while observing the effect on the visible major state when the B1 field saturates the invisible minor state. The need for selective saturation of narrow spectral regions has to date required acquisition of many tens of two-dimensional CEST spectra to sample the entire spectrum with sufficient resolution. Here we present the ACCEST method which measures an entire CEST profile from a single two-dimensional accordion-CEST spectrum plus a reference spectrum. ACCEST is based on the concept of accordion spectroscopy, where in the present implementation the carrier frequency of the weak saturating B1 field is stepped in synchrony with the dwell-time incrementation in the indirect dimension of the two-dimensional spectrum. We benchmarked ACCEST against conventional CEST, resulting in excellent agreement for both backbone 15N and methyl 13C CEST profiles. ACCEST offers substantial time savings that scale linearly with the number of spectra required in the corresponding conventional CEST experiment. Thus, ACCEST can dramatically speed up lengthy serial experiments, such as ligand titrations or temperature-dependent studies, and enable studies of non-equilibrium systems or samples with limited lifetimes.

Cerebral blood flow (CBF) is a quantitative metric for mapping perfusion. While the prototypical MRI approach arterial spin labelling (ASL) is well-validated in humans, the reproducibility of rodent ASL mapping remains poor, limiting translational impact. To address this gap, we used both newly acquired and analysis of previously published data to illustrate biological and physical sources of variation in CBF measured with ASL. Via a meta-analysis, we quantified the variation in CBF reported from the cortex of healthy rodents. A total of 23 mouse studies (343 data points) and 5 rat studies (41 data points) met the inclusion criteria. We demonstrate that reported CBF values exhibit a broad variability (50-400 ml/100g/min) driven primarily by experimental confounds rather than physiological differences. Our meta-analysis explores which factors cause variance in perfusion rates measured. Our experimental data highlight biological factors, particularly the choice of anaesthesia (e.g., isoflurane vs. medetomidine) and strain variations, that alter baseline CBF. Our work, reflecting both state-of-the-art and conventional practice in preclinical imaging, highlights the need to account for multiple sources of variability. Establishing community guidelines for rigorous ASL calibration and physiological monitoring will support improved study design and accelerate translational alignment between rodent and human perfusion measurements.

Purpose: To develop an accelerated motion-compensated diffusion-weighted pseudo-continuous arterial spin labeling (MCDW-pCASL) method using a spatial subspace low-rank reconstruction method for efficient quantification of blood-brain barrier (BBB) water exchange (kw) and permeability (PSw). Methods: An accelerated multidelay MCDW-pCASL sequence was developed to simultaneously encode intravascular and extravascular diffusion-weighted ASL signals across multiple post-labeling delays (PLDs). A spatial subspace low-rank reconstruction framework was optimized to enable joint estimation of cerebral blood flow (CBF) and BBB water exchange rate and permeability. Fourteen young healthy adults underwent test-retest scans (separated by ~1 week) at 3T with both the accelerated MCDW-pCASL and a conventional diffusion-prepared (DP) pCASL sequence. Whole-brain, gray-matter, and white-matter CBF and kw values were quantified to assess test-retest repeatability and cross-method agreement. An additional cohort of 30 older adults underwent single-session MCDW and DP scans to evaluate age-related perfusion and BBB kw/PSw differences. Intraclass correlation coefficients (ICCs) were used to assess reliability and agreement. Results: Accelerated MCDW-pCASL demonstrated excellent agreement with DP-pCASL for CBF (ICC = 0.89) and fair agreement for kw (ICC = 0.56). Test-retest repeatability of MCDW-pCASL was good for CBF, BBB kw and PSw (ICC {approx} 0.6). Across both sequences, younger subjects exhibited significantly higher CBF and kw compared with older adults. Conclusion: Incorporating a spatial low-rank subspace reconstruction enables accelerated MCDW-pCASL acquisition with reliable simultaneous quantification of CBF, BBB kw and PSw. Clinical applications of this method for assessing perfusion and BBB function are warranted.

BackgroundQuantitative dynamic contrast-enhanced myocardial perfusion cardiovascular magnetic resonance (CMR) enables estimation of myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). These measurements require an arterial input function (AIF), which is typically derived from the left ventricular blood pool. However, the contrast agent bolus undergoes dispersion during transport through the coronary vasculature before reaching the myocardial microcirculation. This may introduce systematic and spatially heterogeneous errors in MBF and MPR estimates. PurposeThis work provides an extended segmental analysis of bolus-dispersion-induced errors in quantitative myocardial perfusion MRI using previously established computational fluid dynamics (CFD) simulations in realistic porcine coronary artery models. The focus of the present analysis is the assignment of coronary outlets to myocardial segments and the resulting segmental variability of MBF and MPR errors. MethodsRealistic three-dimensional models of the left and right coronary arteries were extracted from an ex-vivo porcine imaging cryomicrotome dataset. The models extended down to the pre-arteriolar level and included 364 outlets for the left coronary artery and 104 outlets for the right coronary artery, with an average outlet diameter of 383 {+/-} 85 {micro}m. Blood flow was simulated under rest and stress conditions using OpenFOAM. Contrast agent transport was then modeled by solving the advection-diffusion equation using a gamma-variate bolus as input. Outlet concentration-time curves were analyzed using an indicator-dilution model to estimate MBF and MPR errors. Outlets were assigned to standardized myocardial segments, and segmental averages were evaluated with respect to coronary supply territory and travel distance from the model inlet. ResultsThe simulations demonstrated marked segmental heterogeneity of volume blood flow and bolus-dispersion-induced MBF and MPR errors. Errors increased with travel distance from the coronary artery inlet and were more pronounced in regions supplied by the right coronary artery, consistent with lower flow velocities and stronger bolus dispersion. The resulting systematic errors led to underestimation of MBF and overestimation of MPR, with segmental deviations reaching up to approximately 60%. ConclusionBolus dispersion in the coronary vasculature may lead to substantial segmental and location-dependent errors in quantitative myocardial perfusion MRI. This extended analysis indicates that dispersion-related bias is not spatially uniform, but depends on coronary supply territory, travel distance, and flow conditions. These effects should be considered when interpreting regional MBF and MPR estimates, particularly as automated quantitative myocardial perfusion CMR becomes more widely used.

I.Rapid developments in magic-angle-spinning (MAS) hardware over the past two decades have made possible the acquisition of high-resolution spectra of protons in solids, fuelling studies of small and large molecules alike. Nevertheless, proton resolution, limited by the strong dipole-dipole coupling network, remains a bottleneck even at MAS frequencies exceeding 100 kHz. We present here techniques based on phase-modulated homonuclear decoupling that dramatically improve proton coherence times and resolution compared to 60-95 kHz MAS alone using low average radio-frequency amplitudes (< 100 kHz). A relatively high sensitivity (40- 70%) and a straightforward optimization procedure directly on the sample being studied allows these gains to be realised in large biomolecules, as demonstrated here on a 326-residue cytoskeletal protein in its filamentous state. These techniques enable experiments with improved resolution on biomolecules while simultaneously taking advantage of the higher sensitivity available on probes with relatively large rotor volumes that cannot reach higher MAS frequencies.

Purpose: To develop a Field-Correcting GRAPPA (FCG) technique to correct the spatiotemporal-varying phase errors in EPI caused by eddy currents. Methods: The fast-changing gradient in EPI causes strong eddy current effects and associated spatiotemporal-varying phase errors, producing significant image artifacts. The use of higher gradient amplitude, slew rate, and ramp sampling factor for faster imaging exacerbates this problem. In this work, FCG was developed to address this challenge by using a multi-layer perceptron (MLP) to provide a compact representation of a family of GRAPPA-like kernels that correct the spatiotemporal-varying phase errors in the data. A dedicated calibration pipeline was designed to acquire high-quality source and target data for MLP training in both slice-by-slice and simultaneous multi-slice (SMS) acquisitions. To validate FCG's assumptions and performance, a field camera was used to provide ground-truth measurement of phase patterns. The performance of FCG was further validated on phantom and in vivo experiments using demanding EPI trajectories across multiple 3T and 7T systems. Results: Field camera measurements revealed strong spatiotemporal phase variations along the kx direction that repeat along ky during EPI readouts. The experiments on high-performance systems across 3T and 7T demonstrate that FCG can provide superior correction for the artifacts induced by spatiotemporal-varying phase errors compared with existing approaches. Conclusion: FCG is an effective and robust method for correcting spatiotemporal phase errors in EPI, enabling improved image quality on high-performance systems.

White matter lesion (WML) identification, assessment, and characterization using magnetic resonance imaging (MRI) are fundamental for diagnosis and monitoring of multiple sclerosis (MS). Portable ultra-low field (pULF) MRI at 64 millitesla (mT) has been shown to visualize WML with at least one dimension greater than 4 mm. An automated WML segmentation tool catered to pULF-MRI can provide standardized and accurate quantitative measurements of WML volume. In this study, we sought to investigate and compare the accuracy of machine-learning (ML) and deep-learning (DL) pULF MRI segmentation tools. Same-day paired pULF (64mT) and high-field (HF, 3T) MRI scans from 84 adults with MS or suspected-MS (mean age {+/-} SD: 48 {+/-} 13, 62 females) included T2-FLAIR and T1w images. Reference WML segmentations were manually annotated on pULF T2-FLAIR for all scans, with WML confirmed with registered HF T2-FLAIR. HF reference WML segmentations were created. Four automated segmentation methods were applied to pULF scans: Method for Inter-Modal Segmentation Analysis (MIMoSA), an ML algorithm trained on HF WML masks; WMH-SynthSeg, a convolutional neural network model with flexible segmentation capabilities across field strengths and resolution; nnU-Net, a DL algorithm trained on pULF reference WML masks; and Pseudo-Label Assisted nnU-Net (PLAn), a DL algorithm pre-trained on HF reference WML masks and refined with 64mT reference WML masks. Two models were trained with nnU-Net, one using T2-FLAIR images only (nnU-Net-FL) and one using T1w and T2-FLAIR images (nnU-Net-FL/T1). The same was done with PLAn, creating PLAn-FL and PLAn-FL/T1. The six automated WML segmentation outputs were compared to the manual segmentations to determine Dice Similarity Coefficient (DSC) scores. Associations of WML volume estimates with clinical measures were investigated. DSC scores with pULF reference WML masks from PLAn-FL (DSC mean {+/-} SD: 0.50 {+/-} 0.24) outperformed MIMoSA (0.24 {+/-} 0.20, p < 0.0001), WMH-SynthSeg (0.30 {+/-} 0.18, p < 0.0001), nnU-Net-FL (0.41 {+/-} 0.24, p < 0.0001), and nnU-Net-FL/T1 (0.41 {+/-} 0.26, p = 0.0004). Worse Expanded Disability Status Scale (EDSS) and Scripps Neurologic Rating Scale (SNRS) scores were correlated with higher WML volumes in the pULF and HF reference masks. They were also correlated with WML volumes derived from WHM-SynthSeg, nnU-Net-FL, nnU-Net-FL/T1, PLAn-FL, and PLAn-FL/T1, but not MIMoSA. After adjusting for age, WHM-SynthSeg, nnU-Net FL, nnU-Net-FL/T1, PLAn-FL, and PLAn-FL/T1 had significant associations with EDSS and SNRS scores. nnU-Net and PLAn performed best in segmenting WML on pULF-MRI at 64 mT, providing accurate quantitative estimates of WML burden. Moreover, WML volumes estimated by these algorithms were associated with clinical measures of disability, underscoring their utility for reflecting clinical and radiological disease severity. Given pULF-MRI's mobility and lower cost, these findings highlight its relevance in clinical trials, particularly in involving more participants who face logistical constraints and barriers.

Background: Artificial intelligence (AI), including generative and foundation-based methods, has rapidly expanded within medical imaging research. However, the structure, citation impact, collaboration patterns, and thematic orientation of national research ecosystems remain incompletely characterised. Objectives: To evaluate global research trends in AI applied to medical imaging between 2017 and 2025, with detailed analysis of United Kingdom (UK)-affiliated output, citation performance, collaboration structure, funding landscape, and thematic evolution, with emphasis on generative and foundation-based methodologies. Materials and Methods: A bibliometric analysis of Scopus-indexed publications (2017-2025) was performed using a predefined search strategy targeting AI and medical imaging concepts, with emphasis on generative and foundation-based terms. Records were analysed globally and filtered for UK affiliation. Descriptive indicators including total publications (TP), total citations (TC), citations per paper (CPP), and year-on-year growth were calculated. Co-authorship and keyword co-occurrence networks were generated using VOSviewer (v1.6.19). Results: A total of 13,452 publications were identified globally (194,650 citations; global CPP 14.47), of which 889 (6.61%) were UK-affiliated. The UK ranked fourth by publication volume yet demonstrated higher citation efficiency (CPP 21.00) than several higher-volume countries. UK output increased approximately 18-fold between 2017 and 2025, with evidence of a citation-lag effect in recent years. Research activity was concentrated within a small number of institutions accounting for nearly half of national output, although citation impact varied independently of volume. Journal-dominant dissemination was associated with higher average citation impact compared with conference-centric models. Keyword analysis identified three principal thematic clusters: generative/deep learning methodologies, MRI- and diffusion-focused applications, and broader diagnostic imaging workflows. Highly cited publications were initially dominated by generative adversarial network-based reconstruction and synthesis, with recent rapid citation growth observed in diffusion and foundation-model architectures. Conclusion: UK-affiliated research represents a rapidly expanding and highly cited component of the global AI medical imaging literature, with increasing emphasis on generative, diffusion-based, and foundation-model approaches. These findings provide a reproducible bibliometric baseline for monitoring research activity, collaboration patterns, and potential translational priorities, while recognising that citation-based indicators do not directly measure clinical implementation, methodological quality, or real-world impact.

SummaryO_ST_ABSPurposeC_ST_ABSTo develop 3D radial spiral phyllotaxis trajectories that provide a uniform density distribution of readout directions and support retrospective sequential binning, thereby reducing ringing artifacts and improving image quality. MethodsUPhy trajectory redefines the polar angle to achieve uniform density distribution of readout directions. FlexiPhy further decouples the azimuthal and polar ordering of interleaves through a randomized permutation, improving robustness to sequential binning. The proposed trajectories were evaluated in vivo on 10 healthy volunteers using two gradient-echo sequences on a 3T MRI scanner. Sequential temporal reconstructions were compared with reference reconstructions using structural similarity and relative L2 error metrics. ResultsUPhy presents analytically demonstrated uniform density distribution of readout directions. Quantitative analysis shows significantly higher SSIM values and lower relative L2 errors for FlexiPhy compared with both the original phyllotaxis and UPhy trajectories after Bonferroni correction (pcorrected < 0.05). ConclusionFlexiPhy enables more reliable sequential binning reconstructions by reducing trajectory-induced ringing artifacts and temporal inconsistencies. Moreover, its randomized construction is not tied to a specific binning strategy, making it broadly compatible with retrospective binning approaches used in dynamic and motion-resolved MRI.

Background: Epicardial adipose tissue (EAT) is a metabolically active fat depot adjacent to the myocardium and the coronary arteries that can be non-invasively assessed by cardiovascular magnetic resonance (CMR). Increased EAT volume quantified by CMR has been linked to adverse cardiac remodeling, atrial fibrillation, coronary artery disease, and heart failure. Among CMR techniques, isotropic three-dimensional (3D) Dixon imaging at 1.3 x 1.3 x 1.3 mm3 resolution was developed to improve tissue characterization, providing fat-water signal separation for precise volumetric EAT assessment. However, manual segmentation of 3D datasets is highly time-consuming. For integration into clinical and research CMR workflows, reliable and fast automated segmentation is needed. Purpose: To develop and evaluate an automated deep-learning-based pipeline for ventricular EAT quantification based on isotropic 3D Dixon CMR acquisitions. Methods: An nnU-Net model was trained on 165 3D Dixon CMR cases encompassing healthy individuals and patients with underlying cardiovascular disease. The model was trained using all four Dixon phase images (opposed-phase, in-phase, fat-phase, water-phase). Manual 3D ventricular EAT segmentations served as the ground truth for training and evaluation. Performance was evaluated in 30 independent cases using Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), volumetric agreement, Pearson correlation, intraclass correlation (ICC), and Bland-Altman analysis. Model performance was benchmarked against interobserver and intraobserver variability. Results: Automated segmentation achieved a mean DSC of 0.896 {+/-} 0.039 and HD95 of 1.84 {+/-} 0.93 mm versus ground truth. Volumetric agreement with ground truth was high (r = 0.984, ICC = 0.988, p < 0.001; mean bias -0.70 mL, limits of agreement (LoA) [-10.31, 8.90] mL), exceeding interobserver agreement (bias -25.24 mL, LoA [-42.81, -7.66] mL) and comparable to intraobserver reproducibility (bias 2.72 mL, LoA [-8.73, 14.17] mL). Automated segmentation required less than one minute per case compared to 58.4 {+/-} 7.9 minutes for manual segmentation. Two of 30 cases (6.7%) required minor manual correction, both less than five minutes. Conclusion: Fully automated nnU-Net-based ventricular EAT segmentation from isotropic 3D Dixon CMR achieves accuracy comparable to intraobserver reproducibility while significantly reducing post-processing time. The approach may facilitate large-scale and longitudinal EAT quantification in CMR-based research workflows.

We present a non-invasive approach for continuous monitoring of lactate dynamics in-vivo using near-infrared (NIR) spectroscopy. Lactate-related spectral features were measured non-invasively within the overtone region (1600-1850 nm). Several anatomical measurement sites were evaluated, and the middle phalanx of the dorsal finger emerged as the most promising location due to its superior spectral quality and stable tissue perfusion, becoming the exclusive site for all further experiments. Across multiple exercise sessions, predictive models achieved high within-day accuracy (R2[&ge;] 0.8), while cross-day performance was affected by spectral drift and physiological variability. A dynamic offset-correction procedure effectively mitigated these baseline shifts, enabling stable prediction accuracy across days, weeks, and subjects. These findings demonstrate the feasibility of NIR-based lactate estimation and highlight the importance of adaptive correction strategies for reliable long-term, non-invasive monitoring.

CT perfusion (CTP) is central to acute-stroke and oncologic imaging, yet quantitative outputs vary substantially across vendor software, undermining reproducibility. We present an open, transparent core (ctp-core) that fits first-pass time-attenuation curves with a gamma-variate model, derives perfusion indices (peak enhancement, time-to-peak, bolus-arrival time, and area under the curve) analytically from the fitted parameters, and renders parametric maps with the ASIST-Japan standardized lookup table (a-LUT) so that visualization is comparable across sites. Every parameter, bound, and processing step is exposed. The method is validated on Monte-Carlo synthetic curves with known ground truth; no confidential or patient data are used. Across signal-to-noise ratio (SNR) levels 5 to 100 (200 independent runs per level) the pipeline recovers peak time to within 0.03-0.52 s and peak amplitude to within 0.4-8.1% (mean absolute error), degrading monotonically with noise; at a representative SNR of 20 it recovers peak time within 0.13 s, peak amplitude within 2.0%, and bolus-arrival time within 0.51 s, with fit quality R-squared = 0.98. The reproducibility demonstration is deterministic (fixed seed) and re-runs to bit-stable metrics. All code, the synthetic-data generator, the standardized-visualization module, evaluation scripts, and a 34-test suite are released openly for independent verification. The contribution is a fully open, parameter-transparent gamma-variate plus standardized-visualization pipeline with reproducible synthetic benchmarks: a reference others can audit, reuse, and build on.

We developed a standardized, reproducible preprocessing framework for computed tomography (CT) imaging data from multi-institutional repositories such The Cancer Imaging Archive (TCIA), enabling consistent radiomics and artificial intelligence (AI) analyses. Imaging data from TCGA-KIRC patients available on TCIA were used as a representative heterogeneous dataset characterized by variation in acquisition protocols, inconsistent metadata, and differing image quality. The proposed modular pipeline includes series filtering, DICOM-to-NIfTI conversion, orientation harmonization to a canonical coordinate system, voxel spacing normalization, intensity clipping and normalization, segmentation integration, and metadata validation, and is implemented in a reproducible, notebook-based framework compatible with common radiomics and deep learning workflows. This pipeline standardizes imaging data into analysis-ready volumes with consistent geometry, intensity distributions, and spatial alignment, reducing non-biological variability that can adversely affect radiomic feature stability and model performance. The modular design enables task-specific adaptation of individual preprocessing steps while maintaining overall consistency. Although demonstrated on TCIA, this framework is generalizable to other heterogeneous imaging datasets and provides a foundation for robust, large-scale computational imaging studies.

Task-based precision mapping has become a promising technique in functional MRI (fMRI) to robustly characterize and map an individuals unique activity patterns. These experiments consist of acquiring extensive imaging data in one participant, ultimately improving the sensitivity and specificity of individual-specific functional localization. Despite its advantages, studies have primarily focused on understanding individual-specific cortical activation, preventing a holistic view of a systems-level functional response, and to date, best approaches for the statistical analysis of controlled task-based, densely sampled, whole-brain data have not yet been fully established. Therefore, in this study, we collected whole-brain (i.e. covering cortex, cerebellum, and brainstem) multi-echo densely sampled data of the auditory system, a system with major subcortical components, and evaluated activation sensitivity as well as activation stability across data subsets of commonly-used whole-brain and region-specific inference testing approaches. The whole-brain approaches involved standard voxel-level and cluster-level inference schemes with varying statistical thresholds and a non-parametric permutation inference approach. The region-specific approaches involved an exploratory top % t-statistics methods and non-parametric permutation inference approaches. We found that a whole-brain voxel-level approach with a false discovery rate (FDR) correction (p<0.05) presented highest sensitivity across regions and subjects as well as most consistent detection of expected auditory regions, even with lower scan duration. In addition, we found that a region-specific top % t-statistic approach may be a useful exploratory functional localization tool and a complementary method to standard inference testing approaches.

Diffusion MRI (dMRI) enables noninvasive characterization of white-matter fiber orientations and tissue microstructure, but widely used approaches, such as constrained spherical deconvolution (CSD) and parametric multicompartment models, typically address these features separately. The diffusion tensor distribution (DTD) framework jointly represents fiber orientation and microstructure, but estimating DTD from finite, noisy measurements is severely ill-posed. Existing inversion methods either rely on nonnegativity constrained basis representations, which are challenging to sale to high-dimensional and high-resolution distributions, or use sampling-based approaches with limited reliability. We propose MaxEnt-DTD, a maximum-entropy algorithm for DTD estimation from finite and noisy dMRI data. By deriving the Lagrange dual formulation, we reformulate a constrained infinite-dimensional optimization problem into a finite-dimensional unconstrained convex optimization problem, substantially reducing the parameter space and enabling tractable whole-brain DTD estimation. We evaluate MaxEnt-DTD using both synthetic and in vivo data from the Human Connectome Project protocol and a second dataset using advanced B-tensor diffusion encoding. We compare MaxEnt-DTD-derived fiber orientation distributions with results from CSD and Monte-Carlo inversion methods, and assess fiber-specific microstructure measures and rotation-invariant metrics based on the cumulants of DTD. The results demonstrate that MaxEnt-DTD provides a reliable and efficient framework for joint fiber-orientation and microstructure analysis in dMRI.