NMR in Biomedicine

Free carnitine is essential to mitochondrial health by buffering the free acetyl-CoA pool and thereby maintaining energy production. It is also responsible for transporting long-chain fatty acids into the mitochondria for oxidation. Almost all the body's carnitine is in muscle, and plasma concentrations do not reflect tissue content, but there are as yet no non-invasive techniques to assess muscle total or free carnitine. Here we describe a novel non-invasive postprocessing method, using standard 1H magnetic resonance spectroscopy data, for quantifying muscle total and free carnitine concentrations, which includes an orientation- visibility and spectral fitting component, and consideration of interfering metabolites. We demonstrate the importance of the orientation correction even within one muscle group (accounting for up to 1.9-fold difference within one muscle group and 2.9-fold difference in signal between muscles), show its good reproducibility (CoV 8-12%), and validate the results with mass spectrometry measurements in muscle biopsy samples. We apply this method in a group of patients with genetic mitochondrial disease, to investigate the relationship between mitochondrial dysfunction and muscle lipid accumulation. As predicted muscle total and free carnitine were lower in patients with disease and correlated with the degree of mitochondrial dysfunction and lipid accumulation. Further, robust spatial correlations of total carnitine and muscle lipid imply heterogeneity in mitochondrial function. Our findings suggest that increasing muscle carnitine stores could ameliorate the metabolic effects of and disorders related to mitochondrial dysfunction. Furthermore, it has not usually been known in supplementation studies whether l-carnitine actually reached the target tissue. We suggest that this novel method has significant potential for informing on physiology and pathophysiology, and as a biomarker in monitoring treatment response, investigative drug discovery, and personalised medicine.

Magnetic resonance imaging (MRI) is the imaging modality of choice for the diagnosis, characterization, and monitoring of multiple sclerosis (MS). Nevertheless, the contrasts manifested by MS lesions often overlap with those of other pathological conditions, highlighting the need for additional disease biomarkers. In addition, while saturation transfer (ST) MRI provides molecular information associated with myelin, protein, and lipids, quantifying the underlying proton exchange parameters remains challenging. Here, we describe a strategy that extends and modifies AI-boosted ST magnetic resonance fingerprinting (MRF) imaging at 7T. This approach was used to quantify the dynamics of the semisolid magnetization transfer (MT) and the aliphatic relayed nuclear Overhauser effect (rNOE at -3.5 ppm and -1.6 ppm relative to water) in a longitudinal cuprizone MS mouse model (n=12). In lipid phantoms, the reconstructed proton volume fractions were strongly correlated with known lipid concentrations across all three proton pools (r>0.96, p<0.001). In vivo, semisolid MT and rNOE proton volume fractions in the corpus callosum demonstrated a significant decrease (p<0.01) as early as week 4 of cuprizone feeding, preceding changes detected by conventional water relaxometry. ST-MRF based biomarkers were in agreement with histological findings. Overall, our results demonstrate the feasibility of rapid, multi-pool ST-MRF quantification for MS characterization.

Background Mitochondrial dysfunction and abnormal cerebral energy metabolism are implicated in many neuropsychiatric and neurodegenerative disorders. 13C magnetic resonance spectroscopy (MRS), combined with 13C-labeled substrate infusion, offers a non-ionizing, minimally invasive method for assessing fluxes through the main cerebral energy metabolism pathways. However, its human application at 7 T has not been fully established, especially within the frontal lobe. Purpose To explore a clinically translatable interleaved 1H/13C MRS protocol for quantification of cerebral glucose uptake and downstream metabolism at 7 T, and to estimate the tricarboxylic acid (TCA) cycle flux (VTCA) for validation. Study Type Prospective. Population Three young healthy volunteers. Field Strength/Sequence 7T; ACE-STEAM (indirect 1H-[13C]) and ISIS-DEPT (direct 13C-[1H]). Assessment ACE-STEAM and ISIS-DEPT were applied to acquire the time-resolved spectra in the frontal lobe. 13C-labeled glucose, glutamate, and glutamine fractional enrichment time courses were quantified to estimate VTCA through the one-compartment model. Statistical Tests The relative estimated fitting uncertainties (EFUs) were reported for the processed spectra. Nonlinear least squares minimization was used for flux fitting of 13C traces. Uncertainty of the estimated metabolic fluxes was evaluated using Monte-Carlo simulations. Results [1-13C]-glucose (GlcC1) was detected immediately on 13C MR spectra, followed by 13C-labeled GluH4 and GlnH4 and then GlxH3 can be quantified on 1H MR spectra. End-of-infusion mean enrichments were 17% (GluH4), 13% (GlnH4), and 7% (GlxH3). Brain glucose concentration ranged 1.86-2.94 mM, with 61% of the mean enrichment in C1. Group-average VTCA was 0.66 {+/-} 0.07 mol/g/min. Data Conclusion This interleaved 1H/13C MRS protocol enables minimally invasive quantification of cerebral metabolic fluxes, may provide a useful framework for investigating neuropsychiatric and neurodegenerative diseases at 7 T. Evidence Level 1. Technical Efficacy Stage 1.

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.

In magnetic particle imaging (MPI), signal is generated from the non-linear magnetic response of superparamagnetic iron oxide (SPIO) nanoparticles. The optimization of SPIOs for MPI is an active area of investigation. Tracer performance can be evaluated by magnetic particle relaxometry (MPR) and image-based metrics. This study characterized and compared the performance of ten commercially available tracers, spanning a broad range of magnetic core sizes, hydrodynamic diameters and surface coatings, using the peak signal intensity measured from MPR, and the total signal and maximum signal intensity measured from 2D MPI images. Synomag-D tracers exhibited the highest MPR peak signal intensities and highest maximum signal per {micro}g iron. ProMag, a micron-sized iron oxide particle (MPIO), yielded a low MPR peak signal intensity but had the highest total MPI signal per {micro}g of iron. A strong correlation was observed between MPR peak signal intensity and MPI maximum signal intensity (R2=0.94). In contrast, there was a weak correlation between MPR peak signal intensity and MPI total signal intensity (R2=0.38), though this correlation improved when MPIOs were excluded from the analysis (R2=0.91). While MPR generally predicts the tracer performance, it does not completely replicate the complex imaging conditions. As such, comprehensive tracer evaluation requires combining both MPR and image-based metrics.

Subject motion and gradient heating-induced frequency and phase offsets result in spectral misalignment during single-voxel MR spectroscopy (SVS) acquisitions. Several methods have been presented to align the spectra, but their performance on a 7T system is unclear. This study aimed to evaluate the practical importance of four retrospective correction methods, namely creatine fitting, residual water, spectral registration, and cross-correlation. SVS data were collected from 127 participants (88/39 female/male, 39{+/-}15 years) using a semi-localization by adiabatic selective refocusing sequence at 7T. Changes in the spectral linewidth, signal-to-noise ratio (SNR), similarity (mean similarity matrix (SImean)), and metabolite quantification (concentration, relative Cramer-Rao lower bounds (rCRLB)) after correction were evaluated. A Wilcoxon signed rank test was used to evaluate these changes. The p-values were adjusted using the false discovery rate (q<0.05) for multiple comparisons. For significant changes, the effect size was further calculated using the Rosenthal formula. The results demonstrated that all correction methods resulted in a significant decrease in the spectral linewidth and significant increases in the spectral SNR and SImean. The mean changes by using the four methods were 0.76-0.99 Hz for the spectral linewidth change (effect sizes:0.73-0.86), 5.79-6.95 for the SNR change (effect size:0.60-0.72), and 0.03-0.03 for the SImean change (effect size:0.87-0.87). The correction also significantly increased the estimates of the metabolite concentrations of PCr, Gln, Glu, GPC, Ins, NAA, Scyllo, and Tau (effect size:0.25-0.57), and significantly decreased the rCRLBs (effect size:0.26-0.42) of Scyllo, Tau, and Gly. These results indicate that each method provides practical benefits in improving the spectral linewidth, SNR, and similarity by showing a strong to very strong effect of the improvements (effect size>0.60). The correction only showed a weak to moderate effect of the changes in the metabolite quantification (effect size<0.60), suggesting that whether the correction results in more reliable metabolite quantification requires further validation.

Extracranial lipid contamination remains a challenge in proton magnetic resonance spectroscopic imaging (MRSI), especially in short acquisition delay MRSI, where broad lipid resonances overlap with metabolite and macromolecular signals. Although retrospective lipid suppression techniques are widely used in human MRSI, their effects on metabolite quantification in preclinical MRSI, which is more prone to lipid contamination, have not yet been examined. In this study, we assessed how the retrospective lipid suppression and spectral fitting range influence spectral quality, spatial metabolite mapping, and quantification variability using proton MRSI of rat brains at 14.1 T. Lipid suppression was applied via an orthogonal projection method to both fully sampled and compressed sensing datasets, each comprising data with minimal and pronounced lipid contamination. Spectral fitting was performed with both broad (4.1 - 0.2 ppm) and narrow (4.1 - 1.8 ppm) ranges. When lipid contamination was minimal, suppression caused only slight spectral and spatial changes, with consistent metabolite quantification across conditions. In contrast, datasets with pronounced lipid contamination exhibited notable spectral changes following suppression, consequently affecting spatial metabolite mapping and concentration estimates. Group analysis revealed that metabolites with low concentration estimates were most affected. Similar effects were observed in compressed sensing datasets. Our results provide a better understanding of the impact of retrospective lipid suppression on metabolite quantification in preclinical MRSI, thereby supporting future optimizations for its effective application.

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.

Abstract Introduction: Myelin water imaging provides a non-invasive approach for indirectly investigating myelin content of the brain by magnetic resonance Imaging (MRI). Myelin water fraction (MWF) reflects the fraction of water signal associated with water trapped between myelin layers. Postmortem imaging provides a unique opportunity to validate MWF metrics against gold-standard postmortem neuropathology. However, there is a need for validated sequences that would be applicable in postmortem settings. In this study, we validated and compared the applicability of turbo spin echo (TSE) and gradient-and-spin-echo (GRASE) techniques for myelin water imaging in formaldehyde fixed postmortem human brains. Methods: 40 postmortem human brain hemispheres were scanned with TSE and GRASE sequences. Acquired data were reconstructed using a non-parametric multicomponent T2 relaxometry approach using a consistent framework to support comparison between GRASE- and TSE-derived measures. Agreement between derived measures was quantified at both voxel and regional level. Maps derived from the full 32-echo GRASE reconstruction were compared against a truncated 14-echo reconstruction to examine the contribution of the longer echoes to the derived maps. Finally, a random forest regression model was trained based on TSE data to predict the GRASE-derived MWF maps. Results: Both sequences provided robust multicomponent T2 characterization across the brain, and the derived measures showed anatomical patterns consistent with expected differences across tissue types. TSE and GRASE derived maps had moderate to strong agreement at voxel (0.41< {rho} < 0.82, all pFDR<0.001) and regional levels (0.87< {rho} < 0.98, all pFDR<0.001), with consistently stronger agreement observed for regional metrics. Echo reduction comparisons between the full 32-echo GRASE reconstruction and a truncated 14-echo reconstruction showed minimal impact of echo truncation (all {rho} > 0.98, pFDR < 0.001). The nonlinear random forest regression model trained on TSE maps was able to accurately reproduce the MWF maps derived from GRASE (r = 0.91, RMSE = 8.87). Discussion: TSE and GRASE32 capture strongly related multicomponent T2 information in fixed postmortem human brain tissue, but with systematic and tissue-dependent quantitative differences across derived measures. These differences suggest that direct interchangeability should not be assumed and that cross-sequence mapping or calibration is required when quantitative equivalence is desired.

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.

Purpose: To demonstrate the feasibility of whole-brain multidimensional T1-T2 relaxation imaging on a portable 0.064 T MRI scanner. Methods: A two-dimensional inversion-recovery, fast spin-echo acquisition was used to jointly encode T1 and T2 relaxation. Joint relaxation distributions were reconstructed voxel-wise using marginal-distribution constrained optimization (MADCO). The approach was evaluated in a quantitative relaxation MRI phantom and subsequently applied in vivo in a healthy volunteer. Joint distributions, marginal distributions, distribution-derived measures, and statistical dependencies between the two relaxation dimensions were examined. Results: Phantom joint relaxation estimates showed good overall agreement with mono-exponential relaxation measurements and previously reported values at 0.064 T. The in vivo acquisition provided whole-brain coverage over a broad range of relaxation weightings and tissue contrasts. Reconstructed joint T1-T2 distributions showed spatially organized features across the relaxation space, including broad and overlapping relaxation components that were not fully represented by single-value relaxation maps or by either 1D marginal distribution alone. Selected regions of the joint relaxation space produced component maps with distinct spatial patterns. Statistical analysis further showed a dependence between the two relaxation dimensions, supporting the presence of added information in the joint distribution that is lost when treating T1 and T2 separately. Conclusion: Whole-brain multidimensional T1-T2 relaxation imaging is clinically feasible on a portable 0.064 T MRI scanner. Joint relaxation distributions provide information beyond single-value relaxation mapping and may support further developments of quantitative multidimensional imaging at ultra-low field.

Purpose: Magnetic resonance spectroscopic imaging (MRSI) is a versatile technique to investigate the spatial distribution of in vivo metabolism. However, processing MRSI data is demanding, and only a few software packages support end-to-end analysis. The goal of this study was to implement fully automated, end-to-end MRSI analysis into the open-source 'Osprey-MRSI' software package. Methods: MRSI-specific analysis and visualization capabilities were implemented, building on the existing Osprey workflow. Modifications included spatial transformation and filtering operations, automated brain masking and tissue segmentation of the MRSI data, improved lipid filtering, rapid integral maps, linear-combination modeling with explicit B0 frequency-shift correction, and generation of quality-control maps and metabolic images. A fully interactive GUI and semi-interactive HTML reports provide a user-friendly way to inspect each step of the analysis. All analysis derivatives are also exported in NIfTI and NIfTI-MRS format for easy visualization and synergies with other toolboxes and modalities. Results: The automated MRSI workflow was successfully used to analyze short- and medium-TE 3T in vivo MRSI datasets from all major vendors (Philips, GE, Siemens) across multiple sites. Correct coregistration of MRSI data and MR images was validated using phantom data from each vendor and existing MRSI processing tools. Conclusion: Osprey-MRSI offers state-of-the-art methods with minimal user interaction available for non-expert users. The modularity of the workflow and the modeling algorithm will foster innovation and development of novel MRSI-specific analysis methods.

Quantitative Susceptibility Mapping (QSM) relies on a tissue's underlying macroscopic geometry to lead to measurable orientation-dependent field perturbations. To understand and assess QSM error in vivo, anthropomorphic phantoms provide a useful model that mimic the electromagnetic properties and morphology of underlying tissue. Herein, we designed and manufactured an MRI compatible anthropomorphic phantom with cylindrical and spheroid compartments containing realistic susceptibilities to mimic hemorrhages, calcifications, and blood vessels. We estimated accuracy ({epsilon}, bias, RMSE) and repeatability (RC) of MEDI-susceptibility measurements within ROIs. We evaluated voxel-based agreement to validate susceptibility mapping under different acquisition conditions (3T versus 7T) and reconstruction algorithms (COSMOS versus MEDI). Reliable MEDI-based susceptibility measurements were obtained from ellipsoids but not from straws. The ellipsoids (|{epsilon}| = 0.007 to 0.083 ppm at 3T; 0.050 to 0.118 ppm at 7T) were more accurate than the straws (|{epsilon}| = 0.084 to 0.190 ppm at 3T; 0.105 to 0.160 ppm at 7T). The repeatability coefficient across all 6 ROIs (RC = 0.652 ppm at 3T; 0.459 ppm at 7T) was substantially larger than across the 4 ellipsoid ROIs only (RC' = 0.168 ppm at 3T; 0.141 ppm at 7T). The accuracy at 3T (bias = -0.002 ppm, RMSE = 0.082 ppm) was better than the accuracy at 7T (bias = -0.056 ppm, RMSE = 0.092 ppm). Using voxels from the 4 ellipsoid ROIs, we observed excellent agreement between COSMOS and MEDI susceptibility maps at 3T, with linear regression of y=1.00x-0.01 (r=0.99). We observed some underestimation of MEDI susceptibility maps relative to COSMOS at 7T, with linear regression and y=0.93x-0.04 (r=0.99). The results imply that QSM reconstructions are reliable with 3T scanners but can be challenging with 7T scanners at high magnetic susceptibilities.

Abstract: Purpose: Superparamagnetic iron oxide nanoparticles (SPION) are widely used as MRI contrast agents for cell tracking and molecular imaging. However, their detection using conventional T2/T2* weighted and susceptibility-weighted MRI relies on negative contrast (signal voids), which lacks specificity because similar hypointense signals can arise from other sources of magnetic susceptibility. This study introduces Frequency Shift Imaging (FSI), a positive-contrast technique based on an adiabatic Shinnar-Le Roux (SLR) self-refocused pulse that addresses the limited specificity of conventional SPION imaging. Methods: An adiabatic self-refocused RF pulse was designed using the SLR algorithm for frequency-selective positive-contrast imaging. The optimized pulse had a spectral bandwidth of 370 Hz, a peak RF amplitude of 14 T, and a duration of 15 ms. FSI data were acquired on a 7T MRI scanner. Phantom experiments used agarose-embedded 30 nm cell-labeled SPION at 0%, 25%, 75%, and 100% concentrations (pure). In vivo validation was performed following intramuscular SPION injection into the hind limb of a mouse. FSI images were compared with conventional acquisitions. Results: Quantitative phantom analysis showed normalized FSI signal intensities of 25.02% and 75.16% for samples containing 25% and 75% labeled cells, respectively, demonstrating an approximately proportional relationship between FSI signal and labeled-cell concentration. In vivo, FSI generated localized hyperintense positive contrast at the SPIO injection site, providing improved conspicuity and localization compared with the corresponding negative-contrast signal void. Discussion and Conclusion: FSI enables robust, B1-insensitive, positive-contrast SPION imaging at 7T with reduced echo time and SAR compared to paired adiabatic approaches, supporting its potential for quantitative cell tracking and molecular imaging applications.

The Gradient Impulse Response Function (GIRF) is widely used to model and correct gradient system imperfections in MRI, but scanner-specific GIRF measurement remains inaccessible to many research groups because existing approaches rely on specialised field monitoring hardware or fragmented and non-reproducible software workflows. To address this limitation, an open-source, end-to-end framework for phantom-based GIRF measurement is presented, providing a reproducible workflow requiring only standard MRI hardware and a spherical water phantom. The framework integrates vendor-independent pulse sequence generation, phantom-based data acquisition, automated data processing, and GIRF estimation. The framework was validated by comparing GIRF-predicted non-Cartesian k-space trajectories with independent measurements acquired using NMR field probes, which served as the gold-standard for trajectory characterization. Accurate prediction of rosette and spiral trajectories was demonstrated across multiple imaging orientations, with substantially lower trajectory error than the corresponding nominal trajectories. By providing the first openly available end-to-end implementation for phantom-based GIRF measurement, the barrier to routine scanner-specific GIRF characterisation is reduced, facilitating broader adoption of GIRF-based methods across the MRI community.

Background: Acute phase response is an early immunometabolic response to brain injuries, primarily coordinated by the liver via the activation of acute phase proteins. These immune responses can be both beneficial, promoting tissue repair, and detrimental, exacerbating neurological deficits, if not properly controlled. Despite the central role of the liver in immunometabolism, how hepatic metabolism dynamically adapts to traumatic brain injury remains under explored, primarily due to limited liver-specific modalities that can assess metabolic pathways in vivo. 13C MRI utilizing hyperpolarized 13C-pyruvate can assess key regulatory enzyme activities in hepatic metabolism. Methods: Rats with controlled cortical impact were studied in vivo using hyperpolarized [1-13C]pyruvate and [2-13C]pyruvate under fed and fasted conditions 3-4 days after injury. Hyperpolarized 13C products, including [13C]bicarbonate from [1-13C]pyruvate and [5-13C]glutamate, [1-13C]acetyl-L-carnitine, and [2-13C]phosphoenolpyruvate from [2-13C]pyruvate, were evaluated to assess mitochondrial and gluconeogenic metabolism. In parallel, liver tissues were collected following [U-13C3]pyruvate injection for NMR isotopomer analysis of phosphoenolpyruvate, glucose, and glutamate. Results: While no metabolic differences were detected under fed condition, [13C]bicarbonate and [2-13C]phosphoenolpyruvate increased after brain injury under fasted condition, indicating an upregulation of the hepatic gluconeogenic pathway after injury. 13C NMR of liver tissue extracts from injured rats showed an elevated [2,3-13C2]glutamate-to-[4,5-13C2]glutamate ratio and increased 13C-labeling in phosphoenolpyruvate than controls, confirming enhanced hepatic gluconeogenic pathway. Conclusion: This study demonstrates that hepatic acute phase response to brain injuries can be monitored in vivo by hyperpolarized pyruvate, which may be further utilized for longitudinal immunometabolic evaluation of the liver during pathogenesis and therapeutic interventions.

1Abstract/SummarySingle-channel surface coils remain central to rodent MRI, but conventional circular loop designs face an inherent trade-off between surface and depth sensitivity, limiting whole-brain coverage for applications such as resting-state BOLD fMRI. This work introduces a single-channel strongly-coupled geometry surface coil. It consists of a stop-sign shaped loop inductively overcoupled to a nested, three-turn elongated racetrack spiral designed to improve depth sensitivity and thru-plane coverage while remaining robust to variable sample loading. Benchtop characterization across three phantoms of differing size showed the parallel resonant mode and loaded quality factor changed negligibly with loading. In phantom imaging at 9.4 T, the SCG coil achieved in-plane SNR and temporal SNR comparable to, and at shallow depths exceeding, a commercial Bruker 2x2 receive-only rat brain array, while showing substantially more consistent tSNR across loading conditions. The SCG coil also demonstrated superior thru-plane tSNR over a 20 mm slice range at 3.5 mm depth, approximating the anterior-posterior extent of the rat brain. In vivo resting-state BOLD fMRI in eight rats, acquired with a double asymmetric spin-echo EPI sequence, yielded a default mode network consistent with prior reports and revealed a previously undescribed subcortical network spanning superior/inferior colliculi and cerebellar regions. These results establish the single-channel SCG as a promising foundation for next-generation rodent receive coil arrays, combining loading-independent tuning with extended sensitive coverage suitable for whole-brain functional imaging.

BACKGROUND: Circulating lipoprotein measures such as low-density lipoprotein (LDL) cholesterol and apolipoprotein B are causal biomarkers of cardiovascular risk. These can be quantified quickly and accurately by nuclear magnetic resonance (NMR) spectroscopy, but clinical translation has been slow. We investigated easy-to-operate and affordable benchtop NMR technology as a new means to quantify lipoprotein biomarkers in point-of-care settings. METHODS: Serum samples from 336 individuals were analysed with a benchtop NMR spectrometer operating at 80 MHz and a high-field NMR spectrometer operating at 600 MHz. Glucose, apolipoprotein A-I, apolipoprotein B, total triglycerides, total cholesterol, LDL cholesterol and high-density cholesterol were determined by standard biochemistry. Corresponding NMR-based measures were quantified by linear regression. The 80 MHz dataset included experiments with different scan settings to optimize measurement time (1,987 spectra in total). RESULTS: We identified 32 scans (2 min 8 s) as the minimum runtime for lipoprotein quantification. Total triglycerides and glucose were quantified with the highest accuracy (CV [&le;]5.2%, R2 [&ge;]95%), while LDL cholesterol was more challenging (CV = 10.1%, R2 = 72%) and apolipoprotein B in between (CV = 7.4%, R2 = 74%). Epidemiological correlations between biochemistry assays and sex, obesity, glycemia and blood pressure were reproduced by the corresponding benchtop assays (P [&ge;]0.11 for difference). CONCLUSIONS: We developed a new lipoprotein quantification method and demonstrated its feasibility for standard lipoprotein analytics. The portability and cost-effectiveness of benchtop NMR make it an appealing choice for research and clinical settings where rapid and robust results on site are an advantage.

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.

Arterial spin labeling (ASL) provides a valuable non-invasive tool for investigating cerebrospinal fluid (CSF) dynamics. While existing generalized kinetic models provide a useful analytical framework, incorporating explicit fluid mass-conservation constraints may improve the reliability of ventricular CSF quantification. We developed and validated a physics-constrained kinetic model that assumes a constant ventricular volume, under which the volumetric influx and efflux rates are balanced. Under this assumption, the localized CSF renewal rate (f) is modeled as a distinct washout process that acts jointly with intrinsic CSF T1 relaxation R1,CSF, yielding an effective decay rate Reff = R1,CSF + f, providing a more mechanistically interpretable description of the post-arrival signal decay. The model was further tailored to the global inversion physics of the FAIR (Flow-sensitive Alternating Inversion Recovery) sequence and incorporates an explicit zero-clamped pre-arrival boundary condition. When applied to an in vivo preclinical dataset, the proposed model, improved fitting stability and removed the finite-bolus truncation observed in the raw conventional model. Compared with the conventional model, the proposed model showed significantly improved goodness-of-fit (R2 = 0.95 {+/-} 0.05 vs. 0.82 {+/-} 0.06, p < 0.001) and a lower Akaike Information Criterion (AIC = 94.96 {+/-} 5.83 vs. 106.93 {+/-} 2.50, p < 0.001), suggesting it provides a more adequate and efficient representation of CSF dynamics. The proposed model yielded CSF dynamics estimates 14.70% higher than those obtained with the conventional model, with a mean ventricular CSF renewal time of 4.01 {+/-} 0.97 min and a renewal-equivalent volumetric flow rate of 0.97 {+/-} 0.41 {micro}L/min. The resulting estimates might be interpreted as localized, ASL-derived renewal metrics rather than direct measurements of net CSF production.