NMR in Biomedicine

Tumor pseudo-progression (PsP) refers to an initial increase in tumor size or the appearance of new lesions. These pseudo-progressive lesions are predominantly composed of infiltrative inflammatory cells, such as macrophages. This phenomenon commonly occurs in patients undergoing radiation therapy or immunotherapy and typically indicates a positive treatment response. However, it often leads to premature treatment cessation due to misinterpretation as disease progression. Non-invasive imaging biomarkers capable of distinguishing pseudo-progression from true progression would greatly aid in treatment decision-making. In our preliminary study, we explored the potential of gadoterate meglumine (Gd-DOTA, a macrocyclic Gd-contrast) in combination with amine chemical-exchange saturation transfer (amine-CEST) imaging to differentiate tumor from radiation necrosis by assessing Gd-DOTA uptake by infiltrating immune cells, such as macrophages. To evaluate whether amine-CEST, in combination with Gd-DOTA, can differentiate macrophages from cancer cells, we incubated them with Gd-DOTA for 30 minutes. Subsequently, the cells were processed, and amine-CEST imaging was performed on a 9.4 Tesla preclinical scanner. Upon treatment with Gd-DOTA, we did not observe a significant change in amine-CEST contrast in F98 cells compared with untreated cells, whereas treated macrophages exhibited a marked decrease (~40%) in amine-CEST signal compared with untreated macrophages. This reduction in signal was attributed to the uptake of Gd-DOTA by macrophages, which notably shortened water T1 relaxation, thereby quenching the amine-CEST signal. Conversely, cancer cells showed no appreciable change in the amine-CEST signal, indicating no Gd-DOTA uptake. Furthermore, to validate that T1 shortening influences amine-CEST signal, cancer cells were also treated with manganese chloride (MnCl2) for 30 minutes. The uptake of MnCl2 by cancer cells similarly induced T1 shortening, as observed in macrophages, resulting in a decrease in the amine-CEST signal from these cells. Next, we performed the amin-CEST imaging on F98 tumor-bearing rats and radiation necrotic rats. Post-injection with Gd-DOTA showed no appreciable change in the amine-CEST contrast in the tumor-bearing rat, whereas a significant decrease in contrast was observed in the radiation necrotic rat. This further demonstrates that no change in the amine-CEST contrast in tumor-bearing rats is due to cancer cells failing to take up Gd-DOTA. The decrease in amine-CEST contrast in radiation-treated rats reflects the uptake of Gd-DOTA by macrophages infiltrating the radiation-necrotic regions. This straightforward imaging approach holds promise for clinical translation. It offers a novel method for characterizing pseudo-progressive lesions and monitoring diverse treatment responses in cancer patients using standard clinical scanners.

BackgroundMethylmalonic acidemia (MMAemia) is a genetic metabolic disorder characterized by an accumulation of methylmalonic acid (MMA) and impaired energy metabolism leading to increased lactate (Lac). The signals of MMA (1.23 ppm) and Lac (1.33 ppm) overlap, making their separation using conventional MRS challenging. An MRS method to differentiate the two metabolites could enhance pathophysiological understanding and improve treatment monitoring - Hadamard-edited MRS has the potential to achieve this. PurposeTo develop a Hadamard-encoded J-difference editing approach for independent detection of MMA and Lac at 3T. MethodsA novel Hadamard-encoded editing scheme was implemented and evaluated with density-matrix simulations, phantom and in vivo experiments. The new four-step scheme uses frequency-selective editing pulses, applied at 3.2 ppm and 4.1 ppm to modulate the J-coupled methyl resonances of MMA and Lac, respectively. Hadamard combinations of the four sub-experiments yield the separate difference-edited spectra for each target metabolite. ResultsSimulations and phantom experiments clearly illustrate the separated signals of MMA and Lac. In vivo validation experiments show a Lac signal (but no MMA) in a healthy infant, and both Lac and MMA (separated into their respective Hadamard-combination spectra) in a patient with MMAemia. ConclusionHadamard-encoded editing at 3T can separate MMA and Lac signals and shows promise for studying altered metabolism in patients with MMAemia.

Aceruloplasminemia (ACP) is a rare neurodegenerative disorder characterized by extreme cerebral iron overload and a shift towards larger iron aggregates, providing a unique possibility to study how iron aggregation shapes MRI contrast in vivo. We introduce a clinically feasible, multi-parametric quantitative MRI (qMRI) framework that combines quantitative susceptibility mapping (QSM), [Formula], and R2 to disentangle changes in total iron concentration from alterations in iron aggregation and its spatial organization at the cellular scale. Our biophysical model links the microstructure sensitive [Formula] ratio and the slope of the susceptibility-relaxation relationship (iron) to iron aggregation size and distribution. In a 3T qMRI study of three patients with ACP and three matched controls, we observe a marked increase in [Formula] and a pronounced increase of the [Formula]-QSM slope (iron: controls 154.09 {+/-} 52.89 s-1ppm-1; patients 296.68 {+/-} 57.18 s-1ppm-1; p = 0.016), consistent with enhanced iron aggregation and altered spatial organization. Model-based decomposition of transverse relaxation indicates that up to approximately 40% of the observed R2* elevation in ACP is attributable to changes in iron distribution beyond increased iron concentration alone. These findings establish a robust, translational qMRI approach for quantitative in vivo assessment of iron aggregation, revealing microstructural drivers of iron-related neurodegeneration that extend beyond bulk iron load.

Proton magnetic resonance spectroscopic imaging (1H MRSI) enables quantitative mapping of brain metabolites, but its clinical use remains limited by long acquisition time. The goal of this work to improve the applicability of high-resolution 1H FID-MRSI at 7T by enhancing GRAPPA-based acceleration through deep learning-driven k-space reconstruction. In particular, compared with conventional GRAPPA, MultiNet PyGRAPPA enables substantially higher in-plane acceleration while suppressing residual lipid aliasing and preserving metabolite map fidelity in non-lipid-suppressed MRSI. Building on the MultiNet PyGRAPPA framework, we introduce a comprehensive comparison of advanced machine-learning models for predicting missing k-space points. Multiple architectures--including multilayer perceptrons, convolutional neural networks, and several U-Net variants--were trained within a variable-density k-space undersampling scheme to support acceleration factors of R = 4, 6, and 7. The proposed U-Net model extends the MultiNet concept by leveraging nonlinear hierarchical feature extraction, thereby improving reconstruction fidelity while maintaining robustness to noise.The methods were evaluated in vivo using retrospectively undersampled 7T 1H FID-MRSI datasets from healthy volunteers and patients. Quantitative analyses demonstrate that the U-Net outperforms the original MultiNet approach, offering improved SNR retention rate, reduced lipid RMSE, and higher structural similarity of major metabolites. Metabolite maps reconstructed with the U-Net showed reduced lipid artifacts and improved anatomical consistency. In conclusion, integrating deep convolutional networks into GRAPPA-based k-space prediction provides a more reliable and higher-fidelity reconstruction pipeline. When combined with variable-density undersampling, this approach enables faster acquisition of high-resolution 1H MRSI without compromising spectral quality or metabolite quantification.

BackgroundCerebral vasculature is a key biomarker of brain health, and time-of-flight (TOF) magnetic resonance angiography (MRA) provides noninvasive assessment of vascular anatomy. However, conventional TOF-MRA requires long scan times, increasing patient burden and susceptibility to motion artifacts. Compressed sensing (CS) offers a feasible acceleration strategy. PurposeTo quantitatively evaluate CS acceleration in TOF-MRA at 3T and 7T using automated whole-FOV vascular segmentation and semi-automatic segmentation of representative vessels. Study typeProspective Population23 healthy human participants (3T) and 8 healthy human participants (7T). Field Strength/SequenceCS TOF-MRA (CS factors 4 and 8 at 3T; 8 at 7T) was compared against non-accelerated (CS0) TOF-MRA. AssessmentVisual comparison and vascular segmentation were performed using automated whole-FOV methods and semi-automatic segmentation of the posterior cerebral artery and anterior choroidal artery. Statistical TestsContrast-to-noise ratio (CNR), voxel count, and vessel diameter were assessed using two-tailed paired t-tests. ResultsWhole-FOV CNR differed significantly across CS factors at 3T (CS0 > CS4: p < 0.001, d = 0.77; CS0 < CS8: p = 0.008, d = 0.36; CS4 < CS8: p < 0.001, d = 1.11) and 7T (CS0 < CS8: p = 0.002, d = 0.54), with semi-automatic segmentation yielding consistent findings (p < 0.01 for all comparisons). The diameter measurements for segmented vessels are also higher with high CS-factors (PCA 7T: left: p = 0.006, d = 0.93, right: p = 0.045, d = 0.43; AChA 7T: left: p < 0.001, d = 0.66, right: p = 0.009, d = 1.06; PCA 3T: p < 0.001 for all comparison dLeft = 0.52 (CS0 vs. CS4), 0.56 (CS4 vs. CS8), 1.11 (CS0 vs. CS8) and dRight = 0.78 (CS0 vs. CS4), 0.57 (CS4 vs. CS8), 1.17 (CS0 vs. CS8)). Data ConclusionCS shows promise for enhancing clinical applicability of TOF-MRA, with advantages most pronounced at 7T.

Background and Aims: Steatotic liver disease (SLD) has high clinical and public health relevance. Robust population estimates of SLD and its subcategories are challenging due to the limitations of ultrasound measurements or non-invasive scores, particularly for low-grade steatosis. We aimed to quantify SLD prevalence using magnetic resonance imaging (MRI) in the population-based German National Cohort (NAKO). Methods: Hepatic multi-echo Dixon MRI was performed at 5 dedicated study sites with identical setup across Germany. Liver fat (proton density fat fraction, PDFF), R2* as proxy for liver iron, and liver volume were assessed. The resulting data of N = 29'842 individuals (age range 20-72 years) were weighted by survey weights for regional representativeness, resulting in a sample of 50% women and a mean age of 45.6 years. SLD was defined as PDFF [&ge;] 5.75%, and sex-specific prevalence according to age, BMI, socioeconomic status and geographic region was calculated. Results: Overall, SLD prevalence was 21.3% in women and 35.7% in men, and the majority were metabolic dysfunction-associated (MASLD, 89.3% of all SLD cases). Prevalence increased with age in a sex-specific pattern, suggesting potential menopausal effects in women. There was a relevant prevalence of SLD in individuals with normal weight (5.3% in women, 13.2% in men) and the age group <25 years (7.5% in women, 11.9% in women). Differences in prevalence between low and high socioeconomic status were more pronounced in women (37% vs 15.8%) compared to men (45.5% vs 30.3%). Conclusions: Data underscore the high public health relevance of SLD and its subcategory MASLD. The considerable prevalence in groups historically considered low-risk, such as younger or lean individuals, emphasizes the need for raising awareness early.

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.

PurposeTo present a first-of-its-kind artificial intelligence (AI-)integrated MR pulse sequence that detects out-of-voxel (OOV) artifacts in real-time (within-TR) and responds prospectively by updating the crusher gradient scheme. MethodsPer Excitation Real-time Execution & Guided Responses with Integrated Neural-network Evaluation (PEREGRINE), developed for deployment of deep learning models and sequence updates, operated time-domain (TD) and frequency-domain (FD) convolutional autoencoders that detect OOV artifacts. Scans without (AI-off) and with (AI-on) updates were collected from the prefrontal cortex of healthy volunteers using edited MRS. The degree of OOV contamination (OOV score) was quantified per transient based upon the prevalence of OOV signals in the TD and FD data. OOV scores above a user-defined threshold triggered an update of the gradient scheme, iterating through 48 permutations (6 axis transpositions x 8 polarity flips). ResultsWithin each 2-second TR, PEREGRINE successfully provided single-transient OOV scores and updated gradients accordingly. No difference was observed between the OOV scores from the full ("Full" condition) AI-on and AI-off sessions due to the AI-on scan cycling over better and worse gradient permutations relative to the AI-off scan. However, the AI-on scan had significantly lower OOV scores than the AI-off scan when selecting the transients where PEREGRINE persisted ("Dwell" condition) on a given gradient permutation. Ultimately, Fit Quality Number (FQN), from linear combination modeling, improved significantly for the AI-on compared to the AI-off scan. ConclusionPEREGRINE enabled an AI-integrated sequence allowing for real-time evaluation and reduction of OOV artifacts, identifying gradient modifications that produced less OOV contamination.

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.

IntroductionPostmortem human brain magnetic resonance imaging (MRI) offers a unique opportunity to study finer neuroanatomical details and enables direct correlations with gold standard histological and immunohistochemical assessments. However, to prevent tissue decay, postmortem brains are preserved in fixative solutions which can alter tissue properties and exert substantial impacts on the MRI signals. The present study investigates the impact of formalin fixation, the most commonly used solution for postmortem human brain preservation, on different quantitative MRI contrasts. Methods142 intact human brain hemispheres immersed in 10% formalin for a range of fixation durations (between 0 days and 20 years) were imaged in a 3T MRI scanner. A subset of 10 brains were further scanned repeatedly at days 0, 3, 10, 20, 30, 60, 90, and 120 to allow for better characterization of the initial transient effects of fixation. Voxel-wise T1 and T2* relaxation, T1/T2 ratio, and myelin water fraction (MWF) maps were generated for each specimen and timepoint, and linear and nonlinear models were used to examine the spatiotemporal changes associated with progressive fixation. ResultsAll investigated metrics were significantly impacted by formalin fixation, albeit at different rates and with differing regional patterns. T1 and T2* relaxation time decreased as a result of progressive fixation, whereas T1/T2 ratio and MWF measures increased. T1 relaxation and T1/T2 ratio showed nonlinear patterns with initially accelerated changes that decelerate in the first few months, whereas T2* relaxation and MWF changes followed a more linear trend. ConclusionFormaldehyde fixation exerts systematic changes on quantitative MRI signals that can be modeled and adjusted for to allow for harmonized comparisons of MRI metrics across brains fixed for differing durations. The distinct temporal trajectories observed across metrics highlight the need to account for fixation duration in study design and downstream analyses, particularly when integrating datasets acquired under heterogeneous conditions. Our findings provide a quantitative framework for correcting fixation-induced biases, thereby improving the interpretability and reproducibility of postmortem MRI studies.

Purpose: To quantify the contributions of signal-to-noise ratio (SNR) and velocity-to-encoding ratio (v/VENC) to velocity uncertainty in phase-contrast (PC) MRI and to develop a framework for in vivo voxel-wise uncertainty estimation. Methods: Through-plane 2D PC-MRI of the ascending aorta was acquired using multiple velocity encodings (150, 200, 300 cm/s) and flip angles (0, 5, 15, 20 degrees) to vary v/VENC and SNR. Voxel-wise SNR and velocity uncertainty maps were generated using empirically calibrated phase-noise modeling. Phase-resolved subject-level analyses were performed to quantify the relative contributions of SNR and |v|/VENC to percent velocity uncertainty (%unc). Uncertainty was propagated to flow, stroke volume (SV), and cardiac output (CO). Results: Velocity uncertainty varied substantially across the cardiac cycle and depended on both SNR and |v|/VENC. Across cardiac phases, |v|/VENC accounted for most explained variance in %unc (partial R2=0.666), while SNR provided a smaller but meaningful contribution (partial R2=0.287; full R2=0.909). Near peak systole, SNR contributed more strongly while overall uncertainty remained low. In contrast, diastolic %unc became unstable as velocity approached zero. These effects were most pronounced at low |v|/VENC, where higher VENC settings increased uncertainty despite similar SNR. SV uncertainty ranged from 0.27% to 1.07% across VENCxFA protocols. Conclusion: Velocity uncertainty in PC-MRI depends on both SNR and VENC adequacy in a physiologically phase-dependent manner. Relative uncertainty may become inadequate for precise quantification in low-flow applications, such as diastolic regurgitant jets, despite adequate SNR. Spatiotemporal uncertainty mapping provides a framework for uncertainty-aware PC-MRI acquisition and interpretation.

PurposeIn principle, combined T2-Diffusion (D) MRI has the microstructural and chemical sensitivity to detect axonal and myelin water changes in Alzheimers disease and related dementias (ADRD), but its practical implementation may be hindered by demanding hardware requirements. This work assesses the feasibility and accuracy of T2-D for ex vivo analysis of WM lesions in ADRD tissue. MethodsA thawed ex vivo brain sample from the Michigan Brain Bank and a T2-D phantom were scanned at 7T using a combined diffusion relaxometry (CDR) sequence. A non-negative least squares (NNLS) conventional data processing pipeline was used to disentangle water pools with unique T2-D signatures. Simulations examined the effects of minimum TE and SNR on recovery of myelin water (short T2, slow diffusion). ResultsAcross tissue types, T2-D data consistently resolved three spectral components. Phantom experiments showed detection of short T2 and slow diffusion features similar to those observed in ADRD ex vivo tissue, and confirmed CDRs ability to accurately resolve multiple components. Simulations indicated reliable T2-D recovery for myelin with SNR > 30 dB and minimum TE < 25 ms. ConclusionStrong T2 and D weighting could be combined to capture the expected axonal, myelin, and extracellular (EC) regions in T2-D space. The observed short-T2, restricted-D components are therefore unlikely to be artifacts and instead support interpretations as physically meaningful myelin and axonal water signatures.

Low-cost portable MRI has the potential to improve the accessibility of MRI, but new acquisition methods and protocols must be developed and evaluated to accommodate the reduction in SNR and greater impact of system imperfections. Diffusion tensor imaging (DTI) is a candidate tool for monitoring population health, but the bias and variance of quantitative diffusion tensor-derived metrics must be evaluated prior to designing such studies. DTI of the corpus callosum was performed on an in-house, portable 100 mT MRI system using a slab diffusion weighted Fast Spin Echo with radiofrequency (RF) encoding. Slice coverage was restricted to the corpus callosum to shorten scan time and reduce sensitivity to large rigid motion. In vivo DTI images were obtained in two healthy volunteers with nominal voxel size 50 mm3, scan time 25 minutes, and two different volunteers using nominal voxel size 25 mm3, scan time 35 minutes. Mean diffusivity (MD) and fractional anisotropy (FA) coefficients of variation were estimated in the 50 mm3 acquisition using a bootstrap approach and compared to resolution-matched data obtained on a conventional 1.5T system. MD / FA maps were compared quantitatively and qualitatively. Mean MD values in the corpus callosum obtained on the 100 mT system were within 10% of the reference 1.5T acquisition, but FAs were underestimated by 20-30%. The corpus callosum median MD coefficient of variation was 3.7%, and the median FA coefficient of variation was 7.5%. FA maps obtained at 100 mT had an elevated FA noise floor and color FA maps had lower apparent resolution but some white matter tracts were still distinguishable. HighlightsO_LIDiffusion Tensor Imaging (DTI) of the corpus callosum was performed on a portable 100 mT MRI scanner with 50 mm3 voxels in 25 minutes scan time. C_LIO_LIMean Diffusivity estimates in the corpus callosum obtained at 100 mT and 1.5T differed by less than 0.1 x 10-3 mm2/s. C_LIO_LISome white matter tracts were visible in color Fractional Anisotropy maps obtained at 100 mT but FA maps were underestimated by 20- 30% when compared to a resolution-matched 1.5T acquisition, and had lower apparent resolution. C_LI

IntroductionR2* is the transverse relaxation rate of tissue, influenced by local magnetic field inhomogeneities arising from susceptibility differences. It decomposes into R2 and R2', where R2' is the reversible component most sensitive to blood oxygenation and forms the basis of the BOLD fMRI signal. Although orientation dependence of R2 and R2* in white matter (WM) has been attributed primarily to myelin, the vascular contribution to R2' has not been systematically characterized. This study investigated the orientation dependence of WM R2', assessed existing models, and developed an improved model incorporating both myelin and vascular effects. MethodsMonte Carlo simulations were performed using BOLDswimsuite with 2D WM voxels generated from 5,000 fibres. Spin-echo (TE = 70 ms) and gradient-echo (TE = 35 ms) signals were simulated across 30 fibre orientations (0{degrees}-90{degrees}), and R2' was calculated as R2* - R2. Oxygenation, cerebral blood volume (CBV), vessel size, and vessel geometry were systematically varied. Four published models and a novel Myelin-Blood model were fitted to simulated R2' data and compared using R2 and RMSE. ResultsStrong orientation dependence was observed for R2 and R2*. Parallel and mixed vessel geometries produced greater R2' amplitude and orientation dependence than random geometries. Decreasing oxygenation and increasing CBV amplified orientation effects; vessel size altered peak locations. Existing vascular models performed poorly, and the empirical myelin model showed substantial errors near the magic angle. The Myelin-Blood model provided near-perfect fits (mean R2 = 0.999, RMSE = 0.007 Hz), reducing RMSE by approximately 74%. DiscussionWM R2' cannot be adequately described by vascular or myelin effects alone. Myelin is the primary determinant of orientation dependence, but systematic vascular contributions were observed, particularly near the magic angle and under low oxygenation. The Myelin-Blood model improves characterisation of WM R2' and may reduce orientation-dependent bias in qBOLD OEF estimation while improving interpretation of WM BOLD fMRI signals.

Multi-site MRI studies in preclinical neuroimaging are emerging, but unlike in human studies, characterization of inter-scanner variability remains limited. In this study, we assessed intra- and inter-scanner variability between two similarly equipped 7 Tesla MRI scanners using a phantom and ex vivo mouse brain specimens. Diffusion-weighted imaging revealed slight differences in gradient amplitudes between the scanners, while estimated apparent diffusion coefficient (ADC) values showed a coefficient of variation below 1.5% and inter-scanner differences below 2% near the magnet center. Volumetric analysis based on proton density-weighted images showed negligible intra-scanner differences across sessions, while inter-scanner volumetric differences were mostly less than 2% and spatially non-uniform across the brain. Quantitative maps of R1, R2*, and MTsat showed inter-scanner relative differences of less than 5%, 10%, and 20%, respectively, with white matter exhibiting greater variability than gray matter. These findings provide a foundation for future large-scale, multi-scanner preclinical neuroimaging studies.

Diffusion MRI simulations based on realistic tissue microstructure provide a means to validate biophysical models and optimize acquisition protocols, but their computational cost restricts most studies to domains far smaller than a clinical voxel. The objective of this study was to develop an automated and scalable framework that converts whole-slide histology into diffusion MRI simulations at clinically relevant spatial scales while remaining feasible on standard workstation hardware. We present an end-to-end pipeline integrating two-dimensional whole-slide cell segmentation, mesh generation, and finite element Bloch-Torrey simulation. To enable simulations at large spatial scales without prohibitive memory growth, we introduce a subdomain tiling strategy in which the tissue domain is partitioned into extended subdomains simulated independently under no-flux boundary conditions. Signals are aggregated only from the central regions of each subdomain to minimize boundary artifacts. For an 800 {micro}m x 800 {micro}m histology-based domain, the aggregated signal differed by 0.07% from the corresponding full-domain finite element simulation while reducing wall-clock time from several days to hours and maintaining bounded memory usage independent of global domain size. When applied to a 2016 {micro}m x 2016 {micro}m heterogeneous region approximating the in-plane dimensions of a clinical voxel, the apparent diffusion coefficient obtained from the full domain differed from values computed in smaller dense and sparse subregions, demonstrating the influence of structural heterogeneity at clinically relevant scales on derived diffusion metrics. The proposed framework establishes an automated and memory-stable approach for generating diffusion MRI simulations directly from routine histology.

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.

Most high-field MRI scanners conduct imaging using phased-array coils, in which the signals received by an array of coil elements are combined for downstream processing. Optimally combining these signals requires knowledge of each coil's spatial sensitivity profile, which can be acquired from a volume coil with homogeneous sensitivity across the field-of-view. However, this approach is not often used on high-field MRI scanners, especially on non-clinical systems; therefore, this work uses an algorithm based on the singular-value decomposition (SVD), called SVD-B1, to estimate coil sensitivities directly from the array data itself. Images produced by SVD-B1 are devoid of wormhole artifacts and open-ended fringe lines commonly seen in more conventional reconstructions. Quantitative Susceptibility Maps (QSMs) produced using the algorithm were compared to those produced using other combination algorithms across clinically relevant regions of in-vivo and postmortem human brains. As progressive levels of simulated noise were added to the data, SVD-B1's QSMs were up to 3% (in-vivo) and 13% (postmortem) more consistent (as measured by their Intraclass Correlation Coefficient) than those from other algorithms. Additionally, these QSMs were up to 8.5% (in-vivo) and 36% (postmortem) more accurate than other QSMs with respect to a "single-coil" reference. A parallel imaging extension of SVD-B1, called SVD-B1 GRAPPA, achieved similar results for QSMs generated from progressively more accelerated acquisition data. These results show that SVD-B1 can improve the sensitivity of high-resolution QSM to subtle changes in fine-grained tissue structures (e.g., in neurodegenerative disease) and help reduce scan times in clinical settings where shorter scans are imperative.

PurposeTo address the limitations of single-distance, 1D performance metrics in RF coil design. This work introduces a multi-objective, volume-of-interest (VOI) based analysis to systematically characterize the trade-offs between power efficiency, pSAR efficiency, and homogeneity as a function of dipole length (l) and distance-to-load (d) for multiple dipole geometries and target anatomies. MethodsElectromagnetic simulations of straight and end-meandered dipole antennas were performed with varying lengths (100-500 mm) and distance-to-load (1-81 mm) over three anatomical targets (prostate, kidney, heart). Homogeneity, power efficiency, pSAR efficiency, and load sensitivity performance metrics were calculated within each anatomical VOI. Inter-element coupling at variable d was assessed in a 3-element array, and a subset of single-element simulations was experimentally validated using B1+ mapping. ResultsA fundamental trade-off was found between power efficiency and pSAR efficiency. Optimal power efficiency was achieved with shorter dipoles (150 mm < l < 300 mm) closer to the sample (d < 30 mm), while optimal pSAR efficiency and homogeneity were achieved with longer dipoles at further from the sample (d > 60 mm). Inter-element coupling increased with distance-to-load but could be managed by increasing element spacing. Experimental measurements were in good agreement with simulation trends. ConclusionIncreasing distance-to-load to 40-60 mm, compared with commonly used distances of 20-30 mm, offers a practical strategy for improving pSAR efficiency and homogeneity with a minimal decrease in power efficiency. This work provides a quantitative analysis that enables RF coil designers to make informed, data-driven decisions when developing next-generation body arrays and suggests that unshielded end-meandered dipoles could be an optimal transmit element geometry.

PURPOSE: The UK7T travelling head dataset was used to characterise the reproducibility of 7T measurements of the susceptibility of the hippocampal subfields, focusing on the Cornu Ammonis (CA1, CA2 and CA3), dentate gyrus (DG), subiculum (SUB), tail of the hippocampus (TAIL) and entorhinal cortex (ERC). METHODS: Susceptibility maps were created from whole-brain 3D single-echo GRE data (TE=20 ms; 0.7 mm isotropic resolution) using Multi-Scale Dipole Inversion. Automatic Segmentation of Hippocampal Subfields (ASHS) was applied to high resolution T1- and T2-weighted images for segmentation. The mean magnetic susceptibility and volume of hippocampal subfields was evaluated in 50 data sets, comprising 5 repeat acquisitions on 10 healthy participants (age 32 + or -6 years; 3 female). RESULTS: Averaging over subjects, susceptibility values spanned an 18ppb range over the hippocampus (ranging from -13.3ppb in DG to 4.7ppb in ERC). Susceptibility values in the larger hippocampal subfields showed a consistent pattern of variation across subjects, being generally more positive in ERC and SUB than in CA1 and more positive in CA1 than in DG and TAIL. The standard deviation of subfield susceptibilities over subjects ranged from 8.2ppb in the TAIL to 1.7ppb in CA1, and the average standard deviation across repeated measurements, which ranges from 1.7 to 4 ppb, was less than half of the inter-participant standard deviation in all subfields. Susceptibility values in the smaller subfields (CA2 and CA3) were more variable, but ICC(2,k) values for all subfields were >0.82. CONCLUSION: The reported data characterises the variation and reproducibility of hippocampal subfield susceptibility measurements at 7T.