NMR in Biomedicine

Understanding the role of de novo lipogenesis (DNL) in human liver fat accumulation and insulin resistance has been hampered by a lack of non-invasive techniques capable of quantifying DNL-derived liver lipid. Here we develop a precise method that utilises deuterium magnetic resonance imaging, capable of detecting human 2H liver lipid signal changes in vivo due to DNL. We formulate MR-specific DNL equations and use these to determine if DNL accounts for the increased liver fat and metabolic risk previously reported in South Asians, compared with age- and BMI- matched individuals from European ancestry. We find an increased fraction of liver fat originates from DNL in South Asians, and that this strongly relates to the amount of liver fat and composition, implying DNL or related factors could play a pivotal role in driving the increased liver fat in South Asians.

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

Diffusion MRI can reflect features of tissue microstructure and homeostasis, but direct validation in living neural tissue remains challenging. Here we combine optical microscopy and NMR for real-time recording on viable ex vivo neural tissue during environmental perturbations. Simultaneous high-temporal-resolution NMR and optical microscopy are used to monitor apparent diffusion coefficient (ADC), apparent exchange rate (AXR), intrinsic optical signal (IOS), and intracellular calcium in ex vivo neonatal mouse spinal cord during osmotic and ionic perturbations. We find that ADC correlates strongly with IOS, while AXR decreases with depolarization. ADC and AXR are sensitive to distinct features of cellular swelling, supporting their complementary roles in probing tissue viability and function.

Elevated production of lactate is a key characteristic of aberrant tumour cell metabolism and can be non-invasively measured as an early marker of tumour response using deuterium (2H) magnetic resonance spectroscopy (MRS). Following treatment, changes in the 2H-labeled lactate signal could identify tumour cell death or impaired metabolic function, which precede morphological changes conventionally used to assess tumour response. In this work, the association between apoptotic cell death, extracellular lactate concentration, and early treatment-induced changes in the 2H-labeled lactate signal was established in an in vitro tumour model. Experiments were conducted at 7 T on acute myeloid leukemia cells which had been treated with 10 {micro}g/mL of the chemotherapeutic agent cisplatin. At 24 and 48 hours after cisplatin treatment, the cells were injected with 20 mM of [6,6-2H2]glucose and scanned over two hours using a two-dimensional 2H MR spectroscopic imaging sequence. The resulting signals from 2H-labeled glucose, lactate, and water were quantified using a spectral fitting algorithm implemented on the OXford Spectroscopy Analysis (OXSA) MATLAB toolbox. After scanning, the cells were processed for histological stains (TUNEL [terminal deoxynucleotidyl transferase UTP nick end labeling] and H&E [hematoxylin and eosin]) to assess apoptotic area fraction and cell morphology respectively, while a colorimetric assay was used to measure extracellular lactate concentrations in the supernatant. Significantly lower levels of 2H-labeled lactate were observed in the 48-hour treated cells compared to the untreated and 24-hour treated cells, and these changes were significantly correlated with an increase in apoptotic fraction and a decrease in extracellular lactate. By establishing the biological processes associated with treatment-induced changes in the 2H-labeled lactate signal, these findings suggest that 2H MRS of lactate may be valuable in evaluating early tumour response.

PurposeTwo main approaches are used for spectral analysis of edited data: simple peak fitting and linear combination modeling (LCM) with a simulated basis set. Recent consensus recommended LCM as the method of choice for the spectral analysis of edited data. The aim of this study is to compare the performance of simple peak fitting and LCM in a test-retest dataset, hypothesizing that the more sophisticated LCM approach will improve quantification of HERMES data compared with simple peak fitting. MethodsA test-retest dataset was re-analyzed using Gannet (simple peak fitting) and Osprey (LCM). These data were obtained from the dorsal anterior cingulate cortex of twelve healthy volunteers, with TE 80 ms for HERMES and TE 120 ms for MEGA-PRESS of glutathione (GSH). Within-subject coefficients of variance (CVs) were calculated to quantify between-scan reproducibility of each metabolite estimate. ResultsThe reproducibility of HERMES GSH estimates was substantially improved using LCM compared to simple peak fitting, from a CV of 19.0% to 9.9%. For MEGA-PRESS data, the GSH reproducibility was similar using LCM and simple peak fitting, with CVs of 7.3% and 8.8% respectively. ConclusionLinear combination modeling with simulated basis functions substantially improves the reproducibility of GSH quantification for HERMES data.

PurposeThis study aimed to characterize Blood oxygen level-dependent (BOLD) effects in 1H-MR spectra obtained during optogenetic activation of the rat forelimb cortex for the correction and estimation of accurate metabolite concentration changes. MethodsT2*-induced effects were characterized by linewidth changes and amplitude changes of water, NAA and tCr spectral peaks during the stimulation paradigm. Spectral linewidth-matching procedures were used to correct for the line-narrowing effect induced by BOLD. For an increased understanding of spectroscopic BOLD effects and the optimized way to correct them, a 1 Hz line-narrowing effect was also simulated on mouseproton MR spectrum 1H-fMRS data acquired using STEAM acquisitions at 9.4T in rats (n=8) upon optogenetic stimulation of the primary somatosensory cortex were used. Data were analyzed with MATLAB routines and LCModel. Uncorrected and corrected 1H-MR spectra of simulated and in-vivo data were quantified and compared. BOLD-corrected difference spectra were also calculated and analyzed. ResultsSignificant mean increases in water and NAA peak heights (+ 1.1% and +4.5%, respectively) were found accompanied by decreased linewidths (-0.5 Hz and -2.8%) upon optogenetic stimulation. These estimates were used for further definition of an accurate line-broadening factor (lb). Usage of an erroneous lb introduced false-positive errors in metabolite concentration change estimates thereby altering the specificity of findings. Using different water scalings within LCModel, the water and metabolite BOLD contributions were separated. ConclusionThe linewidth-matching procedure using a precise lb factor remains the most performant approach for the accurate quantification of small ({+/-}0.3 mol/g) metabolic changes in 1H-fMRS studies. A simple and preliminary compartmentation of BOLD effects was proposed, which will require validation.

PurposeD-Glucose and 3-O-Methyl-D-glucose (3OMG) have been shown to provide contrast in MRI-CEST images. However, a systematic comparison between these two molecules has not yet been performed. This study dealt with the assessment of the effect of pH, saturation power level (B1) and magnetic field strength (B0) on the MRI-CEST contrast with the aim of comparing the in vivo CEST contrast detectability of these two agents in the glucoCEST procedure. MethodsPhosphate buffered solutions of D-Glucose or 3OMG (20 mM) were prepared at different pH values and Z-spectra acquired at several B1 levels and at 37{degrees}C. In vivo glucoCEST images were obtained at 3 T and 7 T over a period of 30 min after injection of D-Glucose or 3OMG (at the doses of 1.5 and 3 g/kg) in a murine melanoma tumour model. ResultsA markedly different pH dependence of CEST response was observed in vitro for D-Glucose and 3OMG. The glucoCEST contrast enhancement in the tumour region following the intravenous administration (at the dose 3 g/kg) resulted to be comparable for both the molecules: 1-2% at 3 T and 2-3% at 7 T. The ST% resulted almost constant for 3OMG over the 30 min period, whereas a significant increase in the case of D-Glucose was detected. ConclusionOur results show similar CEST contrast efficiency but different temporal kinetics for the metabolizable and the non-metabolizable glucose derivatives in tumour murine models when administered at the same doses.

Out-of-voxel (OOV) signals are common spurious echo artifacts in magnetic resonance spectroscopy (MRS). These signals often manifest in the spectrum as very strong ripples which interfere with spectral quantification by overlapping with targeted metabolite resonances. Dephasing Optimization Through Coherence Order Pathway Selection (DOTCOPS) gradient schemes are algorithmically optimized to suppress all potential alternative coherence transfer pathways (CTPs), and should suppress unwanted OOV echoes. In addition, second-order shimming uses non-linear gradient fields to maximize field homogeneity inside the voxel, which unfortunately increases the diversity of local gradient fields outside of the voxel. Given that strong local spatial B0 gradients can refocus unintended CTPs, it is possible that OOVs are less prevalent when only linear first-order shimming is applied. Here we compare the size of unwanted OOV signals in Hadamard-edited (HERMES) data acquired with either a local gradient scheme (which we refer to here as "Shared") or DOTCOPS, and with first or second-order shimming. We collected data from 15 healthy volunteers in two brain regions (voxel size 30x26x26mm3) from which it is challenging to acquire MRS data: medial prefrontal cortex (MPFC) and left temporal cortex (LTC). Characteristic OOV echoes were seen in both GABA- and GSH-edited spectra for both brain regions, gradient schemes, and shimming approaches. A linear mixed-effect (LME) model revealed a statistically significant difference in the average residual based on gradient scheme in both GABA-(p < 0.001) and GSH-edited (p < 0.001) spectra; that is, the DOTCOPS gradient scheme resulted in smaller OOV artifacts compared with the Shared scheme. There were no significant differences in OOV artifacts associated with shimming method. Thus, these results suggest that the DOTCOPS gradient scheme for J-difference-edited PRESS acquisitions yields spectra with smaller OOV echo artifacts than the Shared gradient scheme implemented in a widely disseminated editing sequence.

IntroductionPotassium is essential in cellular functions, with specific importance in muscle activity and cardiovascular health. It is the main intracellular cation in the human body with 70% located in muscle. Traditional methods to measure potassium levels are invasive and lack specificity for intracellular concentrations. Recently, non-invasive in vivo investigation of K+ ion homeostasis has become feasible by using K Magnetic Resonance Imaging (MRI) and MR spectroscopy (MRS) at ultrahigh magnetic fields. However, studies demonstrating the sensitivity of K MRI or MRS to detect potassium alterations in disease or upon intervention are sparse. This study utilizes K MRS to non-invasively track real-time intramuscular potassium changes during exercise, providing an assessment of potassium dynamics and explores the potential for technical artifacts in the measurements. MethodsFive healthy subjects (three males, two females) were recruited to perform standardized dynamic knee extensions inside a 7T MR scanner. Potassium levels were measured using a K MRS protocol that included periods of rest, moderate, and heavy exercise followed by recovery. Additionally, possible measurement artifacts due to muscle movement or changes in coil position relative to the thigh were evaluated using K MRS and H MRI monitoring in separate sessions. ResultsThe study revealed a consistent decrease in potassium levels during both moderate and heavy exercise, with an average decrease of 5-6%. These changes were rapidly detectable and were reversed upon cessation of exercise, indicating effective in vivo monitoring capability. Possible experimental artifacts were investigated, and the results suggested not to be responsible for the detected potassium changes during exercise. The results of the non-localized K MRS measurements during exercise correlated well with expected physiological changes based on previous literature. DiscussionThe application of K MRS provides a valuable non-invasive tool for studying potassium dynamics in human skeletal muscle. This technique could enhance our understanding of muscle physiology and metabolic disorders. The ability to measure these changes in real time and non-invasively highlights the potential for clinical applications, including monitoring of diseases affecting muscle and cellular metabolism.

Obesity is a major risk factor for type 2 diabetes, yet not all individuals with obesity develop metabolic disease, underscoring the need for mechanistic biomarkers. Adipocyte hypertrophy is a hypothesized driver of insulin resistance, but current methods for quantifying adipocyte size are invasive. Here, we propose and validate a non-invasive MRI approach based on "short diffusion time" diffusion-weighted MR spectroscopy to estimate adipocyte size in vivo. Monte Carlo simulations confirmed the methods accuracy across a physiologic range of adipocyte sizes (20 - 150 m) and signal-to-noise ratios (SNR > 40). We applied this technique to the epididymal white adipose tissue (eWAT) of rats using in vivo 4.7T and ex vivo 11.7T MRI. Adipocyte sizes derived from diffusion MRI showed good agreement with histology, with minor systematic underestimation corrected by empirical factors. This approach does not require complex modeling or high diffusion weighting, increasing its translatability to the laboratory and clinical settings. Diffusion MRI may serve as a non-invasive "virtual biopsy" to monitor adipocyte morphology and improve understanding of obesity-related metabolic dysfunction.

PurposeFor decades, it has been common practice to quantify brain phosphomonoester (PME) 31P signals by a two-component model composed of phosphoethanoamine (PE) and phosphcholine (PC). This study presented the evidence of hidden PME (h-PME) signals and their selective detections toward accurate quantification of PE and PC. MethodsGaussian lineshape analysis was modeled to reveal h-PME. Inversion-recovery (IR) sequence was employed to null the PE and PC resonances for selective detection of h-PME. The fully-relaxed 31P spectra after h-PME correction were used to quantify PE, PC and other brain metabolites in a group of 16 healthy subjects. ResultsSpectral lineshape analysis and IR modulation revealed previously overlooked h-PME signals underlying sharp PE and PC resonances. The h-PME signals appeared as a broad "bump" (LW1/2: 105 {+/-} 25 Hz, N = 16), leading to poor spectral resolution between PE and PC. Fast relaxing h-PME signals, tentatively assigned to blood 2,3-DPG, were selectively detectable using IR sequence at an optimal inversion delay of 5.8 s. In fully relaxed 31P spectra, h-PME measured 44 {+/-} 9 % of the total PME signal, equivalent to 1.36 {+/-} 0.39 mM in single phosphoryl unit, compared to 1.41 {+/-} 0.23 mM for PE and 0.31{+/-} 0.10 mM for PC. ConclusionHidden PME signals are a significant constitute of PME signals in human brain 31P spectra, and need to be taken into account for quantifying PE and PC as biomarkers of altered phospholipid metabolism in brain pathologies.

Intravoxel incoherent motion (IVIM) theory in MRI was proposed to account for the effect of vessel/capillary perfusion on the aggregate diffusion weighted MR signal. The prevalent IVIM modeling is based on equation-1: SI(b)/SI(0) = (1 -PF) x exp(-b x Dslow) + PF x exp(-b x Dfast) [1] where SI(b) and SI(0) denote the signal intensity of images acquired with the b-factor value of b and b=0 s/mm2, respectively. We recently reported that, for the liver and likely for other organs as well, IVIM modeling of the perfusion component is constrained by the diffusion component, with a reduced Dslow measure leading to artificially higher PF and Dfast measures. With higher b-value associated lower image signal of the targeted tissue, Euqation-1 is focused on describing the signal decay pattern along increasingly higher b-values by three IVIM parameters. Signal intensity at each b-value (i.e., SI(b)) is normalised by the signal intensity of b=0 image (i.e., SI(0)). We noted an apparent problem for Euqation-1. For example, if we want to compare the IVIM parameters of the normal liver parenchyma and a liver tumor, following Euqation-1 we will take the assumption that the SI(0) of the normal parenchyma and the tumor are the same and considered equally as 1 (or 100) for the biexponential decay modelling. However, this assumption is invalid for many scenarios. From our liver IVIM database of 27 healthy female subjects, we chose six of the youngest subjects (20-27 yrs) and six of the oldest subjects (58-71 yrs) and measured the signals of the liver and left erector spinae muscle on b=0 and 2 s/mm2 images. The results show, while there was no apparent difference of left erector spinae muscle signal among the young and elderly groups, the elderly groups liver SI(0) is approximately 20 % lower than that of young group. This difference skewed the ratios of various SI(b)/SI(0) and the followed IVIM parameter determination. The general trend is that lower liver SI(0) is associated with lower Dslow and higher PF and Dfast. If IVIM bi-exponential decay fitting starts from a very low non-zero b images (such as b=2 s/mm2 images), this problem persists. We performed an additional analysis of our IVIM database of five cirrhotic livers and the results show SI(b=2) of cirrhotic right liver is positively associated Dslow (Pearson r=0.687), and negatively associated with PF (Pearson r=-0.733). Though the examples we used in this letter are on liver aging and liver fibrosis, the points discussed are expected to be generalisable to other pathologies and to other organs.

MRI of fixed tissue is an excellent way to study pathological changes caused by different diseases with great anatomical detail. It is, however, known that properties of tissue change with fixation. The aim of this study was to determine the variability of several quantitative MRI (qMRI) parameters in fixed brain tissue obtained from donors unaffected by neurological conditions and investigate the existence of quantitative parameters which vary little between specimens. We introduce a 3D method for high-resolution mapping of water content, T1 and T2* relaxation times and parameters characterising magnetisation transfer and apply it at 3T to 7 whole, fixed human brains (3 male, 4 female, aged between 47 and 79 years, mean age 67 years). The qMRI parameters determined include relaxation rates T1 and T2*, MT ratio and T1 and T2* after MT. From these we can further derive semiquantitative MT parameters such as the exchange rate (ktrans) and bound pool fraction (fbound). Correlations between these parameters are investigated. In addition, truly quantitative water content determined non-invasively with MRI is reported on whole human post mortem brains - to our knowledge, for the first time. Water content was found to have mean values of 73% for WM and 85% for GM with standard deviation below 2.5% over 7 brains, and thus a few percent units higher than in vivo (69% and 81%) and of comparable constancy.

BackgroundRadiomic studies on cardiac MR mainly focus on images from distinct time points rather than considering the systems dynamic nature. Recent studies have shown that radiomic features exhibit considerable variation across the cardiac cycle and that dynamic features can improve classification accuracy in downstream tasks. However, it is unclear whether the dynamic temporal evolution of radiomic features is sufficiently stable in the presence of noise. PurposeIn this work, we evaluate the stability of radiomic feature curves of cine CMR images under noise. MethodsWe extracted over 800 radiomic features from all time points of cine CMR images of 35 subjects from three cohorts with various levels of artificially added noise. The stability of feature curves is evaluated based on pairwise normalized mean squared errors, and features are ranked by their stability. ResultsFeatures exhibit a varying degree of stability, but stability is consistent across subjects. Besides generally stable and unstable features, some features are stable within the same noise level but unstable otherwise. ConclusionSome radiomic feature curves remain stable under noise while showing variability over the cardiac cycle. These features are promising candidates for improving models using dynamic rather than static feature values.

ObjectWe hypothesized that the inherent acquisition delay (AD) in {superscript 1}H-FID-MRSI can introduce systematic LCModel quantification biases due to strong spectral dephasing, and that Backward-Linear-Prediction (BLP) reconstruction toward AD = 0 ms can harmonize metabolite estimates across acquisitions with various delays. Materials and Methods2D {superscript 1}H-FID-MRSI were acquired in rats at 14.1T with three AD values (0.71, 0.94, 1.30 ms). Hippocampal metabolites were quantified using LCModel and AD-matched basis sets. Complementary Monte-Carlo simulations (n = 1000) replicated {superscript 1}H-FID-MRSI spectra at multiple ADs under realistic SNR conditions. BLP was applied to in vivo and simulated FIDs to back-predict missing points up to AD = 0 ms, enabling quantification within a unified basis set framework. ResultsIn vivo and simulated data showed clear AD-dependent variations for several metabolites (Gln, tCho, tNAA, Ins, Tau), with discrepancies frequently >10% despite AD-specific basis sets. Simulations confirmed metabolite-specific biases increasing with AD. BLP reconstruction preserved quantification consistency up to [~]0.98 ms of recovered FIDs, reducing inter-AD mismatches in vivo--particularly for Tau, tNAA and tCho--lowering the mean discrepancy from 10.5% to [~]5%. DiscussionThese findings show that AD affects {superscript 1}H-FID-MRSI quantification in LCModel, whereas BLP reconstruction can harmonize spectra across delays by enabling a virtual AD = 0 ms quantification scheme. This supports BLP as a practical strategy to improve consistency and comparability in MRSI studies.

J-difference-edited MRS is widely used to study GABA in the human brain. Editing for low-concentration target molecules (such as GABA) typically exhibits lower signal-to-noise ratio (SNR) than conventional non-edited MRS, varying with acquisition region, volume and duration. Moreover, spectral lineshape may be influenced by age-, pathology-, or brain-region-specific effects of metabolite T2, or by task-related blood-oxygen level dependent (BOLD) changes in functional MRS contexts. Differences in both SNR and lineshape may have systematic effects on concentration estimates derived from spectral modelling. The present study characterises the impact of lineshape and SNR on GABA+ estimates from different modelling algorithms: FSL-MRS, Gannet, LCModel, Osprey, spant and Tarquin. Publicly available multi-site GABA-edited data (222 healthy subjects from 20 sites; conventional MEGA-PRESS editing; TE = 68 ms) were pre-processed with a standardised pipeline, then filtered to apply controlled levels of Lorentzian and Gaussian linebroadening and SNR reduction. Increased Lorentzian linewidth was associated with a 2-5% decrease in GABA+ estimates per Hz, observed consistently (albeit to varying degrees) across datasets and most algorithms. Weaker, often opposing effects were observed for Gaussian linebroadening. Variations are likely caused by differing baseline parametrization and lineshape constraints between models. Effects of linewidth on other metabolites (e.g., Glx and tCr) varied, suggesting that a linewidth confound may persist after scaling to an internal reference. These findings indicate a potentially significant confound for studies where linewidth may differ systematically between groups or experimental conditions, e.g. due to T2 differences between brain regions, age, or pathology, or varying T2* due to BOLD-related changes. We conclude that linewidth effects need to be rigorously considered during experimental design and data processing, for example by incorporating linewidth into statistical analysis of modelling outcomes or development of appropriate lineshape matching algorithms. HighlightsO_LIIn-vivo GABA-edited 1H-MRS data from 222 subjects were filtered to simulate varying linewidth and SNR conditions C_LIO_LIFiltered datasets were quantified with six different modelling algorithms to assess the impact of linewidth and SNR on the metabolite level estimates. C_LIO_LISynthetic spectra with controlled GABA+ levels and in-vivo-like background signals (applied incrementally) were also assessed. C_LIO_LIFor both in-vivo and synthetic datasets, GABA+ estimates showed a significant association with Lorentzian linewidth across most algorithms, even for small changes in linewidth. C_LIO_LIWeaker, often opposing associations were observed for Gaussian linebroadening. C_LIO_LIThis indicates a potentially significant confound for studies where linewidth or lineshape may be expected to differ, even slightly, between groups. C_LIO_LIThe need for appropriate strategies to account for lineshape differences is highlighted. C_LI O_FIG O_LINKSMALLFIG WIDTH=173 HEIGHT=200 SRC="FIGDIR/small/582249v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@612fbaorg.highwire.dtl.DTLVardef@8462aforg.highwire.dtl.DTLVardef@16af040org.highwire.dtl.DTLVardef@1c3141f_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG To assess the degree to which aspects of linewidth, lineshape and SNR may confound GABA+ estimates, a collection of in-vivo datasets were quantified with six modelling algorithms, with linebroadening and SNR varied experimentally. Most algorithms showed a strong association between GABA+ estimate and Lorentzian linebroadening (2-5% decrease per Hz), with weaker effects for Gaussian broadening. This indicates a potentially significant confound in cases of differing relaxation parameters between groups or experimental conditions.

PurposeTo employ an Off-Resonance Saturation (ORS) method to measure the ferritin-bound iron pool, which is an endogenous contrast agent which can give information on cellular iron status. MethodsAn ORS acquisition protocol was implemented on a 7T preclinical scanner and the contrast maps were fitted to an established analytical model. The method was validated by correlation and Bland-Altman analysis on a ferritin-containing phantom. Ferritin-iron maps were obtained from post-mortem tissue of patients with neurological diseases characterized by brain iron accumulation, i. e. Alzheimers disease, Huntingtons disease and aceruloplasminemia, and validated with histology. Transverse relaxation rate and magnetic susceptibility values were also obtained for comparison. ResultsIn post-mortem tissue, the ferritin-iron contrast strongly co-localizes with histological iron staining, in all the cases. Quantitative iron values obtained via the ORS method are in agreement with literature. ConclusionsOff-resonance saturation is an effective way to detect iron in grey matter structures, while mitigating for the presence of myelin. If a reference region with little iron is available in the tissue, the method can produce quantitative iron maps. This method is applicable in the study of brain diseases characterized by brain iron accumulation and complement existing iron-sensitive parametric methods.

Background and PurposeIn-vivo magnetic resonance spectroscopy (MRS) of 2-hydroxyglutarate (2HG) may provide diagnostic and monitoring biomarkers in isocitrate dehydrogenase (IDH)-mutated glioma. A previous meta-analysis has shown good diagnostic accuracy of TE-optimized PRESS for IDH-mutated glioma, but most studies feature IDH-wildtype glioma as a comparison. However, when considering newly identified brain lesions that may mimic glioma, full characterization of its diagnostic utility should also consider the accuracy of 2HG measurement in non-tumor tissue. Therefore, we tested how well TE-optimized 2HG levels distinguish between IDH-mutated glioma and non-tumor tissue, in this case, normal-appearing brain. We further examined the impact of different spectral modeling strategies (baseline stiffness, macromolecule inclusion, and basis set composition). Materials and Methods48 patients with diagnosed/suspected IDH-mutated glioma were enrolled. 3T MRS data were acquired from tumor and contralateral non-tumor tissue with PRESS localization (TE = 97 ms, optimized for 2HG detection) and analyzed with LCModel software. Receiver operating characteristic analysis evaluated 2HG estimates ability to distinguish IDH-mutated glioma from non-tumor brain tissue. Modeling interactions between 2HG and other metabolites were evaluated to identify reasons for potential false-positive 2HG detection. ResultsTE-optimized PRESS distinguished IDH-mutated glioma from non-tumor tissue with lower sensitivity (range 0.76-0.62) and specificity (0.85-0.78) than literature suggests for IDH-mutated vs. IDH-wildtype glioma. Strong negative correlations between gamma-aminobutyric acid (GABA) and 2HG persisted across all modeling strategies and may lead to false-positive 2HG detection in non-tumor tissue. We further present a cautionary example from a patient on a ketogenic diet, showing that the ketone body acetone can interfere with 2HG detection. ConclusionsSpectral overlap with GABA and acetone can lead to false-positive 2HG detection in non-tumor tissue. Clinicians need to be mindful of these pitfalls when interpreting 2HG estimates.

31P-MRS is a method of choice for studying neuroenergetics in vivo, but its application in the mouse brain have been limited, often restricted to ultra-high field (>7 Tesla) MRI scanners. Establishing its feasibility on more readily available preclinical 7 Tesla (T) scanners would create new opportunities to study metabolism and physiology in murine models of brain disorders. Here, we demonstrate that the apparent forward rate constant (kf) of creatine kinase (CK) can be accurately quantified using a progressive saturation-transfer approach in the mouse brain at 7T. We also find that a reduction of approximately 20% in the breathing rate of anesthetized mice can lead to a 36% increase in kf attributable to a drop in intracellular pH and mitochondrial ATP production. To achieve this, we used a test-retest analysis to assess the reliability and repeatability of 31P-MRS acquisition, analysis and experimental design protocols. We report that most 31P-containing metabolites can be reliably measured using a localized 3D-ISIS sequence, which showed highest SNR amplitude, SNR consistency and minimal T2 relaxation signal loss. Using this protocol, our study identifies, for the first time, key physiological factors influencing mouse brain energy homeostasis in vivo and provides a methodological basis that will guide future studies interested in implementing 31P-MRS on preclinical 7T scanners.

Glioblastoma (GBM) brain tumors are among the most lethal of all human cancers, with a median survival time of [~]15 months. Treatment planning requires radiologic demarcation of tumor boundaries with contrast-enhanced magnetic resonance imaging (CE MRI); however, significant tumor burden extends beyond the contrast-enhancing margins of the tumor. GBM tumors have an increased expression of amino acid (AA) transporters, including the Alanine, Serine, Cysteine Transporter 2 (ASCT2) and the L-Type Amino Acid Transporter 1 (LAT1). This upregulation has been leveraged in positron emission tomography (PET) studies to detect tumor burden beyond the contrast-enhancing margins identified by standard-of-care CE MRI. Here we leverage recent approaches in deuterium metabolic magnetic resonance with this known upregulation of AA transporters in GBM to demonstrate that 2H MR can detect glioma based on enhanced branched- chain amino acid (BCAA) uptake. To the best of our knowledge, these data represent the first non- invasive quantification of AA concentrations in brain tumor and raises the potential to (i) detect tumor burden beyond contrast-enhancing margins and (ii) quantify AA metabolism using 2H MR spectroscopy.