Magnetic Resonance Imaging

Purpose: to introduce a novel magnetic resonance imaging (MRI) technique, MUSLIS MRI (muscle and lipid suppression), that generates two co-registered 3D sets of images in a single acquisition: one set with lipid signal suppression (MUSLIS1) and a second set with muscle signal suppression (MUSLIS2). Materials and Methods: Numerical simulations based on Bloch equations were performed to optimize the sequence parameters; the MP2RAGE sequence was employed for the implementation of MUSLIS imaging. MRI experiments were performed in vivo at 3 T. Quantitative measurements of signal contrast were carried out on MUSLIS1 and MUSLIS2. Various combination images and T1 parametric maps of the thighs were generated from the two co-registered 3D sets. Results and Discussion: Optimal sequence parameters to achieve lipid suppression in one dataset (MUSLIS1) and muscle suppression in the other dataset (MUSLIS2) were determined by numerical simulations. MUSLIS imaging was validated in vivo at 3 T: MUSLIS1 images and MUSLIS2 images displayed excellent lipid and muscle suppression, respectively. The values of the signal contrast between subcutaneous lipids and muscle, as well as between bone marrow lipids and muscle reached values close to 0.8. Thus, MUSLIS would allow for tissue characterization of lesion/masses and direct visualization of the adipose tissue. Combination images, generated from these two intrinsically co-registered sets of images (MUSLIS1 and MUSLIS2), displayed different contrasts and improved signal characteristics, such as mitigation of signal inhomogeneities. The development of MUSLIS imaging for musculoskeletal imaging followed the concept introduced with the FLAWS, an MRI sequence designed for brain MR imaging to generate two 3D sets of images, one with white matter suppression and the other with cerebrospinal fluid suppression. Similarly to FLAWS, which provides T1 maps of the brain, MUSLIS imaging yields T1 parametric maps of the musculoskeletal system. Furthermore, the unique characteristics of the combination images make MUSLIS imaging a potentially attractive technique at field strengths higher than 3 T. Conclusion: The proof-of-concept of a novel approach to musculoskeletal imaging, MUSLIS MRI, is proposed here and demonstrated by numerical simulations and MRI experiments performed in vivo at 3 T. Keywords: MRI, muscle, lipids, musculoskeletal imaging, high field MRI, fat suppression, FLAWS MRI, MP2RAGE, MUSLIS, inversion recovery, adipose tissue, bone marrow, subcutaneous fat, T1 mapping, 3 T, 7 T, neuromuscular disorders, metabolic diseases, muscular dystrophy.

PurposeWith growing interest in pediatric MRS to understand the neurochemistry of brain development, it is essential to establish pediatric-specific metabolite T1 and T2 relaxation times for reliable quantification of metabolites. Furthermore, characterization of age-related changes in relaxation times may provide insight into developmental differences in tissue microstructure and cellular composition. Therefore, the goals of this study were two-fold: (1) to estimate metabolite T1 and T2 relaxation times in a pediatric cohort; and (2) to assess linear relationships between age and relaxation times. MethodsTI- and TE-series data were acquired from the bilateral thalamus in 20 and 25 participants aged between 0 and 7 years, respectively. Data were processed and modelled in Osprey 2.5.0. Metabolite T1 and T2 relaxation times of total N-acetylaspartate at 2.0 ppm (tNAA2.0), total creatine at 3.0 ppm (tCr3.0), total creatine at 3.9 ppm (tCr3.9), total choline (tCho), myo-inositol (mI) and the combined glutamate and glutamine (Glx) were estimated by fitting the modelled signal amplitudes to TI-series inversion recovery signal and TE-series exponential decay equations, respectively. Linear relationships with age were tested for metabolite T1 and T2 relaxation times using Spearman correlations. ResultstNAA2.0 and Glx showed significant changes in T1 with age. tNAA2.0, tCr3.0, tCho and mI showed significant decrease in T2 with age. ConclusionSeveral metabolites showed significant changes in relaxation times with age in the thalamus region. Therefore, use of age-specific relaxation correction for pediatric studies using the linear equations provided is encouraged for reliable quantification of metabolites.

Purpose: Proton density (PD) and macromolecular proton fraction (MPF) are fundamental MR parameters characterizing water and macromolecular content in tissues. Both parameters can be mapped using a time-efficient MRI protocol, though PD maps require additional corrections for the reception profile (RP) non-uniformity and global scaling. Existing PD mapping methods suffer from uncertainties related to PD normalization to reference tissue. This study aimed to develop a data-driven RP correction method for PD mapping based on MPF as an internal scale. Theory and Methods: One-parameter model linking PD and MPF was derived under assumption of proportionality between total macromolecular volume and proton concentration. This model with a uniform parameter value across brain tissues generates a surrogate PD map, which is used to estimate RP from an uncorrected PD map. The proposed algorithm was tested against a common PD mapping method utilizing bias-field correction and normalization to cerebrospinal fluid. Brain MRI data were acquired at 3T from seven healthy individuals. The protocol included 3D multi-echo T1-, PD-, and magnetization-transfer-weighted gradient-echo sequences. Parametric maps were reconstructed using the single-point MPF mapping algorithm with data-driven B1 correction. Results: MPF-based and bias-field corrections yielded similar PD measurements in white and gray matter with no significant bias and remarkably small variability (coefficients of variation <0.6%). Conclusions: MPF-based RP correction enables computationally simple and robust quantitative PD mapping at no additional cost if MPF maps are available. The main advantage of the proposed method is automatic elimination of global scaling that obviates the need for PD normalization.

There is increasing demand for quantitative measurements in neuroimaging research, including absolute quantification of regional myelin density. Fast whole-brain macromolecular proton fraction (MPF) imaging enables acquisition and reconstruction of quantitative maps of MPF, a biomarker of myelin, and has been used in many research applications. To date, no standard protocols have been published for parcellating MPF maps. FreeSurfer is a widely used software package for automated brain parcellation. It is designed for use with high-resolution T1-weighted anatomical images, typically acquired with an MPRAGE sequence. MPF maps inherently provide high contrast between white and gray matter and can potentially be used as input images for parcellation, enabling simultaneous quantitative tissue characterization and volumetric assessment. However, it is unclear how the use of MPF maps as input images with FreeSurfer software affect reliability and repeatability of regional MPF and volume estimates. We evaluated three FreeSurfer parcellation workflows for this purpose. The workflows differed in the amount of neuroimaging data required and computational intensity. MPF and MPRAGE data were acquired in two separate sessions for 11 adults. Reliability was evaluated using mean relative differences in estimates, Bland-Altman analysis, and intra-class correlation coefficients (ICCs). Repeatability was assessed using within-subject coefficients of variation (CVws). All three workflows produced similarly high within-subject repeatability across scans. However, reliability in regional MPF estimates was lower in gray matter than in white matter for workflows that used MPF maps as input. Workflows that use MPF maps as FreeSurfer input may be adequate for estimating mean MPF in GM and WM of cortical parcels and in subcortical regions. However, the MPRAGE-based workflow is recommended when reliable individual-level estimates or volume estimates are required.

Purpose: We aim to acquire and reconstruct high signal-to-noise ratio (SNR), whole-brain, mesoscale (isotropic submillimeter) diffusion MRI (dMRI) in vivo in a clinically feasible scan time. Methods: We present ROVER-dMRI, a unified rotating-view acquisition and super-resolution reconstruction framework. Thick slices (up to 10 mm)acquired across many rotated orientations improve the base SNR, and a shared multi-resolution hash-encoded implicit neural representation (INR) with per-direction MLP head and a physics-informed, Bloch-simulated slice-profile forward model reconstructs these views into a continuous isotropic volume (up to 13x super-resolution) in a self-supervised manner. ROVER-dMRI was evaluated in vivo on human brain (0.78 mm isotropic and 0.5 mm isotropic) and monkey brain (0.6 mm isotropic) across MRI platforms with varying gradient performance, and benchmarked against gSlider-SMS and least-squares super-resolution reconstruction (LS-SRR). Results: At 0.78 mm isotropic resolution, ROVER-dMRI diffusion- weighted images achieved higher SNR than the vendor product sequence, whose images were limited by the noise floor, while its directionally encoded color (DEC) maps showed lower noise than gSlider. When reducing the number of views from 20 to 13 (reducing the scan time by 65% ), ROVER-dMRI acquisition/reconstruction preserved all fine details in the fiber orientations. In the monkey brain, its DEC map matched a 60-direction gSlider acquisition at only 40% of the scan time. ROVER-dMRI preserved coherent fiber-orientation-distribution (FOD) structure, including crossing fibers, and recovered fine details in the cerebellum at 0.5 mm isotropic, that are not visible at 0.78 mm resolution. Compared to state-of- the-art 0.5 mm methods, ROVER-dMRI reduced scan time by 73% while acquiring more diffusion directions. Conclusion: ROVER-dMRI offers a practical route to high-SNR whole-brain mesoscale diffusion imaging in vivo, with the pulse sequence and reconstruction pipeline released as open source. KEYWORDS: diffusion MRI, super-resolution, implicit neural representation, hash encoding, physics informed learning

Background: Ultra-low field (ULF) magnetic resonance imaging (MRI) offers accessible point-of-care neuroimaging, yet its capability to support multivariate network analyses remains understudied. Methods: We systematically evaluated the feasibility and reliability of Morphometric Similarity Networks (MSN) and Morphometric Inverse Divergence (MIND) networks derived from paired high-field (HF, 3T/1.5T) and ULF (64mT) scans in a primary cohort(N=23) and an independent replication cohort (N=19). We tested the impact of super-resolution processing, cortical parcellation granularity (Desikan-Killiany vs. Destrieux), and mesh resolution (fsnative vs. fsaverage5) on edge-level cross-scanner agreement (Lin's concordance correlation coefficient, CCC) and subject-level identifiability. Results: MIND networks demonstrated higher edge-level HF-ULF agreement (mean CCC = 0.95) and ULF reproducibility (mean CCC = 0.98) compared to MSNs (mean CCC = 0.86-0.87 and 0.91-0.94, respectively). In contrast, MSNs yielded higher subject-level differential identifiability across field strengths due to a broader distinction between within- and between-subject similarity. Reconstructing MIND networks on native high-resolution meshes (fsnative) maximised cross-scanner agreement, whereas coarser meshes and finer cortical parcellations enhanced individual network identifiability. Conclusions: Structural similarity networks can be reliably constructed from ULF MRI data. Optimal analytic choices depend on study objectives: MIND on native meshes maximises cross-scanner stability, whereas MSNs excel at preserving individual uniqueness, paving the way for globally scalable connectomic research.

Quantitative magnetic resonance imaging (qMRI) supports both basic neuroscience and clinical research, providing the foundation for longitudinal studies of brain development, aging, and neurological disease. However, its validity rests on a little-studied assumption: that physical relocation of imaging hardware leaves underlying neuroimaging biomarkers intact. When research facilities expand or modernize, an entire scanner undergoes radical disruption--field ramp-down, physical transport, shielding reconstruction, and re-shimming. Whether qMRI metrics remain stable following such invasive procedures without introducing systematic biases is largely unquantified. Here, we systematically evaluated the impact of relocating a 3T clinical scanner over a distance of 30 km on a broad, multimodal suite of neuroimaging markers in healthy volunteers. To disentangle variance components, we benchmarked within- and cross-site stability using percent variation (PV; measurement precision), intraclass correlation coefficients (ICC; absolute reliability), and Spearman rank correlations (R2; spatial and order agreement). We show that scanner relocation introduces no greater variance than baseline within-site assessments. High-resolution brain structural morphometry from different sequences (multi-echo MPRAGE, compressed sensing MPR2RAGE) and quantitative relaxometry (compressed sensing MP2RAGE T1 mapping) demonstrated excellent stability (PV<1%, ICC=0.89-0.99, R2>0.93). Diffusion tensor (mean diffusivity: MD, fractional anisotropy: FA) and mean signal kurtosis (MSK) metrics from multi-shell diffusion MRI maintained low variability across white and deep gray matter (global cross-site ICC>0.90; global PV: MD<1.7%, MSK<1.2%, FA<3.8%; global R2: MSK>0.93; MD>0.90; FA>0.77), confirming relocation robustness for diffusion-derived microstructural markers. Resting-state functional networks displayed high cross-site reproducibility (ICC>0.90), while task-based fMRI activation peaks retained acceptable spatial and magnitude agreement (ICC=0.30-0.60) despite intrinsic physiological variability. In the absence of software or hardware upgrades, physical scanner relocation leaves qMRI biomarkers remarkably intact--providing the empirical foundation required to preserve data integrity across major facility transitions and multi-site longitudinal cohorts. Key points that summarize the paperO_LIHardware transitions do not compromise qMRI data integrity: Physical relocation of a 3T scanner (including field ramp-down, 30 km transport, and re-shimming) introduces no greater variance in quantitative MRI metrics than baseline within-site test-retest assessments. C_LIO_LIMultimodal biomarker robustness: Brain morphometry, T1 relaxometry, diffusion tensor and kurtosis, resting-state functional connectivity display comparable within- and cross-site stability reflected by different metrics (percent variation, intraclass correlation coefficient and Spearman correlation) C_LIO_LIEmpirical backing for longitudinal research: In the absence of hardware or software upgrades, multimodal qMRI biomarkers remain remarkably stable, reassuring researchers that long-term cohort studies and multi-site harmonization can survive major facility relocations. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/26363342v1_ufig1.gif" ALT="Figure 1"> View larger version (57K): org.highwire.dtl.DTLVardef@773d03org.highwire.dtl.DTLVardef@46a09borg.highwire.dtl.DTLVardef@da5bc0org.highwire.dtl.DTLVardef@1bdbf9d_HPS_FORMAT_FIGEXP M_FIG This study evaluates the impact of physically relocating a 3T MRI scanner on neuroimaging biomarkers. By comparing before and after relocation sessions on healthy volunteers, we conclude that the procedure does not introduce significant variability. Parameters for morphometry, T1 relaxometry, diffusion, and resting-state functional connectivity show high stability. Task-based fMRI showed greater within-site variability, but no increase in variability following relocation, consistent with the inherently higher variability of task-evoked responses.. The results confirm the integrity of qMRI data after infrastructure relocation, ensuring reliability for longitudinal studies. C_FIG

Background: Whole-brain segmentation of neonatal low-field MRI is needed for regional volumetric analysis, but established segmentation systems are designed around different field strengths, contrasts, and age ranges. Methods: We first applied complete end-to-end pipelines, including FreeSurfer, FSL-based processing, Infant FreeSurfer, FastSurfer, BrainSuite, SPM12/CAT12, iBEAT v2, the dHCP structural pipeline, and M-CRIB-S, directly to 0.35T neonatal T2-weighted images. Because those systems did not produce a complete 47-region output, we next tested released end-to-end AI segmentation models, including SynthSeg, FastSurfer, FastSurferVINN, and QuickNAT. These were highly sensitive to input format, contrast, voxel geometry, and the high-field training domain. We then decomposed the task into skull stripping, registration, super-resolution, atlas propagation, and tissue refinement and tested the available components individually. Results: No complete end-to-end pipeline or released AI model delivered a usable 47-region segmentation in direct application. Stage-wise testing showed that preprocessing, reconstruction, registration, atlas propagation, and expectation-maximization each solved only part of the problem. Their final assembly produced complete 47-region outputs across the neonatal cohort. Conclusions: Direct application of established pipelines was insufficient for 0.35T neonatal T2-weighted MRI. A stage-wise combination of compatible components provided the workable solution.

Myelin supports rapid and efficient neural communication and is implicated in brain development, learning, aging, and neurological disease. Although numerous magnetic resonance imaging (MRI) approaches have been proposed to characterize myelin in vivo, they have rarely been evaluated together within the same participants and across repeated acquisitions. To address these gaps, we introduce the Myelin Imaging Reliability and ReprOducibility Resource (MIRROR), an openly available multimodal test--retest MRI dataset of diffusion MRI (dMRI), inhomogeneous magnetization transfer imaging (ihMT), quantitative susceptibility mapping (QSM), MP2RAGE, T1w/T2w ratio mapping, and transverse relaxometry in 22 healthy adults. We evaluated gray--white matter differentiation, intermetric correspondence, test--retest reliability, and participant specificity across 27 myelin-sensitive metrics. Gray--white matter differentiation was strong for dMRI derivatives, MP2RAGE-derived R1, T1w/T2w, and ihMT, while being substantially weaker for QSM measures. Intermetric spatial correlations were generally strongest within acquisition or modeling families. Test--retest reliability was generally high, particularly for dMRI-derived metrics, but was more variable among QSM-, ihMT-, and R2*-derived measures. Regional spatial profiles were also highly participant-specific, with discriminability approaching its upper bound for most metrics. All imaging data, derived maps, and processing code are openly released. MIRROR provides a benchmark for characterizing the measurement properties and practical tradeoffs of scalable myelin-sensitive MRI methods, supporting principled acquisition selection and accelerating methodological development and validation.

The choroid plexus (ChP) is a highly vascularized structure located in the brain ventricles that plays important roles in multiple central nervous system functions. ChP enlargement has been implicated in many neuropsychiatric conditions such as multiple sclerosis, depression, schizophrenia, and Alzheimer's disease. However, limitation in the accuracy of the current segmentation tools highlight the need for more reliable methods to quantify ChP volume in healthy and clinical human populations. The shape and size of the ChP vary drastically from person to person, and currently there are no large ChP datasets which allow comprehensive training of segmentation models. Here we introduce DataPlex, a collection of 700 structural MRI brain images where the lateral ventricle ChP has been manually labeled and checked by expert human raters. DataPlex is balanced and representative across a wide range of scanning parameters, demographics, and clinical profiles, and subject-level information will be available for each labeled image. DataPlex will be open source to support the creation of reliable segmentation tools for the human lateral ventricle ChP.

Multi-echo functional magnetic resonance imaging (fMRI) acquires signals with distinct contrast profiles across echo times (TEs). Empirical evidence suggests that different TEs capture distinct signal contributions with varying sensitivity to BOLD and non-BOLD processes from long to short TEs (Stroman, 2002; Krishnamurthy, 2023; Dong, 2024). Previous literature has not yet examined how brain states extracted from temporal dynamic functional connectivity (dFNC) evolve across TEs. Here we present a methodological and empirical study of TE-dependent evolution of dynamic connectivity states, with a phenomenological two-component model for interpretation. Multiple echoes were acquired with an echo planar time-resolved imaging (EPTI) sequence. TE-specific components were extracted via atlas-based group ICA (Neuromark 1.0), and brain connectivity states were generated via windowed dFNC and spatial ICA clustering of windows. Within-subject and group-level states evolved across TEs in network organization and variance. States exhibited TE-dependent variance profiles, with distinct peak echo times; and group-level points of state transition were independently mapped to TE-dependent transitions in variance. We modified a bi-exponential model to simulate and model changes in the dFNC time-course across TEs. The bi-exponential model revealed high consistency with subject states (R2 fit=0.86, res. error=0.55, 86% model convergence rate), suggesting a two-component signal system contributing to dynamic TE-dependent connectivity changes in state. A significant correlation between relative model terms was estimated on a cell-wise basis across states (r(24) = -0.43, p=0.03, 95% CI [-0.70, -0.04]). This suggests that specific bi-exponential signal parameters (i.e., short FNC1,TE=0, long FNC2,TE=0, and long FNC2,decay) were associated with the observed TE-dependent variations in connectivity states across TEs. In sum, our analyses provide downstream implications for TE-aware state analysis dependent on connectivity magnitude (FNCTE=0) and rate of connectivity decay (FNCdecay), in a two-component signal model, with future implications for TE-dependent state biomarkers.

Abstract Purpose: To determine whether structural MRI information can improve reconstruction of highly accelerated 3D snapshot chemical exchange saturation transfer (CEST) MRI. Methods: Fully sampled brain CEST data were retrospectively undersampled at acceleration factors (AFs) of 4, 6, 8, 10, and 12. We compared reconstruction without structural information with population-level pretraining using T1-weighted (T1w), T2-weighted (T2w), or combined T1w-T2w images, as well as direct conditioning using a co-registered subject-specific T1w image. Performance was evaluated in two held-out healthy subjects using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean absolute error (MAE) across frequency offsets. Z-spectrum fidelity and derived magnetization transfer ratio asymmetry (MTRasym) maps were additionally evaluated within three-dimensional gray- and white-matter masks. Results: Subject-specific T1w conditioning consistently achieved the highest PSNR and SSIM and the lowest MAE across AFs, with greater improvements at higher acceleration. It also improved Z-spectrum agreement, particularly in white matter and combined gray- and white-matter regions across most AFs. Improvements in MTRasym were more modest and varied across tissue types and acceleration factors. Conclusion: Incorporating subject-specific structural information can improve reconstruction of highly accelerated snapshot CEST MRI, particularly at higher acceleration factors. However, improvements in reconstructed source images did not consistently translate into better preservation of derived CEST contrast, highlighting the need for reconstruction strategies that more directly incorporate CEST spectral information.

Introduction: This study aimed to characterize the neurochemical profile of Alzheimer s disease (AD) continuum stages using proton magnetic resonance spectroscopy (1H-MRS) data for brain metabolites and macromolecules. We used Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS-DA) to classify diagnostic groups and assess feature importance. Methods: Our dataset consisted of 7 T short-echo time 1H-MRS spectra of the posterior cingulate cortex/precuneus of individuals diagnosed with AD (n = 27) or Mild Cognitive Impairment (MCI, n = 27) and Healthy Controls (HC, n = 31). Classification used 100-round Monte Carlo cross-validation based on metabolite concentrations and macromolecular peak intensities. Variable importance in projection (VIP) was assessed across classification models to identify the most relevant neurochemical features. The resulting metabolite rankings were compared with previous literature to evaluate their biological plausibility. Results: Classification was strongest for AD versus HC (AUC=0.88) and lower for comparisons involving MCI. Metabolites contributing most strongly to classification included myo-inositol, ascorbate, and choline-containing compounds, consistent with previous studies on AD-related neurochemical alterations. In addition, specific macromolecular components were identified as relevant features, suggesting a potential contribution of MM signals to the characterization of AD-related neurochemistry. Discussion: These findings support the use of short-echo-time 1H-MRS as a complementary tool for characterizing neurochemical changes in the AD continuum, with potential applications in disease characterization and monitoring, warranting validation in larger cohorts.

Background and purpose: Accurate identification of cardiac MRI volume orientations is essential for reliable image interpretation and for enabling downstream automated analysis pipelines. However, orientation labels and commonly assigned manually, making the process time-consuming and prone to variability. Recent advances in vision-language models offers new opportunities for automated label generation by jointly modeling visual and semantic information. Methods: In this study, we systematically compare a conventional end-to-end transformer-based captioning model with a CLIPCap architecture for automated cardiac MRI view labelling. Both models were evaluated on a dataset of 1184 de-identified cardiac MR images spanning across six clinically relevant orientations: short axis, two-, three-, four-chamber views, left and right ventricular outflow tract views. The caption transformer was trained from scratch, whereas CLIPCap leveraged pretrained CLIP image embeddings combined with lightweight mapping network and a frozen language model. Results: Experimental results demonstrate a substantial performance advantage from CLIPCap, which achieved an overall accuracy of 98% compared to 68% for the caption transformer. CLIPCap consistently delivered high precision and recall across all orientation classes, including those of high clinical importance, while the end to end transformer exhibited unstable performance and complete failure in certain views. Conclusion: These findings highlight the benefits of leveraging large scale multimodal pretrained model for medical image labeling tasks, particularly in data limited settings. The study suggests that CLIPCap provides a more robust and clinically reliable solution for automated cardiac MRI orientation labeling, supporting its integration into efficient and scalable clinical workflows.

Purpose: To evaluate whether visually assessed high-quality prostate MRI, as defined by the Prostate Imaging Quality (PI-QUAL) scoring system, is sufficient to ensure consistent AI model performance, or whether hidden acquisition-related technical effects introduce variability that cannot be detected through visual inspection. Methods: This retrospective study included 568 bpMRI scans from seven institutions, classified using PI-QUAL v2 into high-quality (scores 2-3) and low-quality (score 1) groups. Quantitative image quality features were extracted using MRQy from T2w and ADC sequences. Three experiments were conducted on high-quality scans: (1) unsupervised clustering to identify hidden technical batch effects; (2) V-Net models (M) for prostate capsule segmentation on T2w using single-cluster and balanced multi-cluster training strategies; and (3) VoxelMorph-based deformable registration (M2) to align ADC maps to T2w images. Performance metrics included DSC, precision, recall, volume ratio, HD95, ASSD, and TRE. Kruskal-Wallis tests assessed cross-cluster variability, Two One-Sided Tests (TOST) evaluated performance equivalence, and bootstrap resampling quantified training efficiency. Results: Clustering identified four clusters among 277 high-quality bpMRI scans. Single-cluster models showed variable performance (DSC=0.41-0.85) with significant batch effects (p<0.001), even with 140 training samples. The balanced multi-cluster model (n=60) eliminated batch effects (p=0.075), achieved equivalent performance (p<0.001), and demonstrated 2.23-fold greater training efficiency despite using 2.3-fold fewer samples. Registration performance also varied significantly across clusters (TRE=0.86-5.47 mm, p<0.001). Conclusion: Acquisition-related batch effects significantly impact AI generalizability in prostate MRI, even among high-quality scans. Quantitative clustering enables detection of technical variations and supports diversity-aware training strategies that achieve equivalent performance with 2.3-fold fewer samples while eliminating batch effects.

Patients with implantable cardioverter-defibrillators (ICDs) often are excluded from receiving CMR scans at 3.0T due to ICD-induced image artifacts. This work aims to transfer sequence protocols established for CMR imaging in the presence of ICDs at 1.5T to a first human application at 3.0T. All measurements were performed on a clinical 3T MRI scanner. Phantom measurements were used to establish and optimize wideband PSIR LGE imaging at 3.0T. Volunteer measurements with externally placed ICDs were used for optimization of CINE imaging and wideband PSIR imaging. Thirdly, patients with implanted ICD were examined with the optimized protocols. Human measurements showed that SA stacks obtained with spoiled GRE after contrast agent showed a markedly lower artifact burden than the conventional approach. For scar imaging, a wideband PSIR with a bandwidth of 3kHz and a frequency offset of +750Hz allowed a sufficient suppression of relevant artifacts in the majority of scans. No strong correlation between the image quality and the distance between the cardiac silhouette and the device was detected. The presented work shows the feasibility of clinical cardiac MRI at 3.0T including GRE-CINE and wideband PSIR-LGE for patients with ICDs.

The shift towards open science in preclinical research requires high-quality, comparable data adhering to FAIR principles, yet rigorous quality assurance (QA) and quality control (QC) frameworks remain less established in preclinical magnetic resonance imaging (MRI) than in clinical imaging. To address this limitation, a multicenter study was conducted across 21 international laboratories using standardized commercial liquid phantoms for mouse and rat MRI setups and a harmonized acquisition protocol. Data processing was centralized using AIDAqc, an automated pipeline extracting quantitative metrics including signal-to-noise ratio (SNR), temporal SNR (tSNR), ghost-to-signal ratio (GSR), and motion-equivalent temporal instability, combined with five complementary outlier-detection algorithms. The evaluated datasets encompassed magnetic field strengths from 3.0 to 16.4 T and heterogeneous coil and acquisition configurations. In mouse phantom data, SNR and tSNR were significantly lower at 3 T than at 7 and 9.4 T, whereas artifact-related metrics did not differ significantly between field-strength groups. In rat phantom data, differences between field strengths were less pronounced. At 7 T, substantial dataset-specific differences were observed in anatomical and functional quality metrics, also among datasets using the similar coil configuration. Multicenter reference values for frequently represented 7 T configurations were established, including SNR/tSNR of 48 {+/-} 3/48 {+/-} 1 dB for mouse surface-coil datasets and 49 {+/-} 3/44 {+/-} 4 dB for rat array-coil datasets. Longitudinal anatomical SNR showed generally low variability, whereas ghosting was more variable across time. Automated outlier detection additionally identified transient acquisition instabilities that were not always apparent by visual inspection. Together, these findings show that field strength and nominal coil configuration alone do not adequately characterize MRI performance and support standardized, longitudinal phantom-based QC combined with automated analysis as a practical approach for improving the reliability, transparency, and interoperability of multicenter preclinical MRI data.

Efficient neuronal communication relying on rapid saltatory conduction depends not only on the amount of myelin wrapping axons but also on myelin compact organisation. Yet, current quantitative MRI myelin imaging methods, such as Myelin Water Imaging (MWI) and quantitative Magnetization Transfer (qMT), are designed to estimate myelin content and are often treated as interchangeable, overlooking myelin organisation. We introduce Structural Myelin Integrity (SMI), a quantitative MRI metric designed to distinguish myelin compact organisation from content. SMI combines the distinct biophysical sensitivities of MWI and qMT to separate the water confined between myelin layers from the total myelin membrane mass. We evaluated SMI in two rodent models of myelin decompaction and one remyelination model, validating our findings with synchrotron small-angle X-ray scattering and electron microscopy. SMI detected myelin layers decompaction despite largely preserved myelin membranes, revealing structural abnormalities that were not explained by myelin content alone. During remyelination, SMI returned closer to healthy values at early timepoints than myelin-content metrics, consistent with the formation of thin-but-compact sheaths. These findings indicate that myelin content and organisation can dissociate and establish SMI as a non-invasive marker of myelin organisation, compatible with existing clinical MRI approaches for studying white matter changes not detected today.

Background: Intramuscular fat and fibrous tissue deposition increase with ageing, disuse, and disease and contribute to impaired muscle function and reduced 'muscle quality'. Magnetic resonance imaging (MRI) and biopsy provide reference measures of muscle composition, but their cost, invasiveness, and limited accessibility restrict routine use. B-mode ultrasonography has emerged as a practical, low-cost alternative, with ultrasound-derived echo intensity widely interpreted as a surrogate measure (index) of intramuscular fat, fibrosis, and 'muscle quality'. However, such interpretation warrants further scrutiny. Aim: To quantify the relationship between ultrasound-derived echo intensity and reference measures of intramuscular fat and fibrous tissue and determine whether these relationships vary across muscles or according to anatomical and methodological factors. Methods: A systematic search of six databases was conducted (last search: August 2026). Human and animal studies reporting correlations between echo intensity and measures of intramuscular fat or fibrous tissue using MRI, histology, or chemical analysis were synthesised using multilevel random-effects meta-analysis with study-level cluster-robust inference. Correlations were Fisher z-transformed and back-transformed for interpretation. Exploratory analyses examined fat compartment, muscle, muscle architecture, health status, study model, and distance-correction procedures. Results: Twenty-eight studies met the eligibility criteria. For intramuscular fat, 204 effects from 23 independent studies yielded a weak-to-moderate pooled correlation with echo intensity (r=0.54, 95% CI: 0.45-0.61; 95% PI: - 0.02-0.84; P<0.01), with substantial heterogeneity (I^2=74.5%). The wide prediction interval suggests the true correlation could range from negligible to strong. No significant moderation effects were observed for study model (animal vs. human: r=0.46 vs. 0.55; P=0.26), health status (clinical vs. healthy: r=0.64 vs. 0.49; P=0.11), probe-to-muscle distance correction (corrected vs. raw echo intensity: r=0.57 vs. 0.53; P=0.25), or muscle architecture (non-pennate vs. pennate: r=0.63 vs. 0.53; P=0.56), although some categories contained few studies and effects. Model-based inference indicated significant moderation effect of lipid compartment (intramyocellular lipid vs. total intramuscular fat vs. extramyocellular lipid: r=0.10 vs. 0.57 vs. 0.49; P<0.001) but this effect was not significant under cluster-robust inference (P=0.50). For fibrous tissue, six effects from four studies yielded a weak, non-significant pooled correlation (r=0.34, 95% CI -0.39, 0.81], 95% PI [-0.85, 0.96]; P=0.24). Conclusions: Echo intensity is weakly-to-moderately associated with intramuscular fat, and this varied substantially across studies and muscles, whereas evidence for intramuscular fibrous tissue is limited and uncertain. These findings provide limited support for interpreting echo intensity as a surrogate measure or an index of intramuscular fat or fibrosis or, on this basis, as an index of 'muscle quality'.

Structural differences observed in collision-sport athletes may reflect repetitive head-impact exposure, broader effects of athletic training, or pre-existing individual variation, making inclusion of a non-collision athlete group important for interpretation of any differences seen. Here, structural T1-weighted MRI data from 17 collegiate American football players, 16 cross-country runners, and 9 non-athletes were analyzed using FSL voxel-based morphometry (VBM), FIRST subcortical volumetry and vertex-wise shape analysis, and FAST whole-brain tissue segmentation. Spatial inference used permutation testing with threshold-free cluster enhancement and family-wise error correction, while scalar analyses used multiplicity-corrected procedures. FAST-derived absolute tissue volumes were also examined in a sensitivity analysis adjusting for FreeSurfer estimated total intracranial volume (eTIV). VBM showed recurrent brainstem-centered differences, with non-athletes showing greater local gray matter measures than both athlete groups, whereas football players showed greater brainstem measures than runners. FIRST vertex-wise analysis identified corrected differences involving the brainstem and selected subcortical structures, while no total FIRST structure volume survived false discovery rate correction. Unadjusted FAST analysis identified a CSF group effect, F(2, 24.38) = 11.46, p < .001, q = .001. After eTIV adjustment, the CSF group effect remained, F(2, 38) = 14.30, q < .001, and a white matter group effect was also identified, F(2, 38) = 9.59, q < .001; gray matter and total segmented tissue did not show corrected adjusted effects. These findings indicate that structural variation depends on anatomical scale, comparison group, and head-size adjustment and cannot be attributed uniquely to collision-sport exposure.