NMR in Biomedicine

IntroductionGlioblastoma is characterized by heterogeneous tumor characteristics and infiltrative tumor boundaries, making accurate delineation difficult with extensive manual annotations. Chemical exchange saturation transfer (CEST) is a non-invasive MRI technique used for in vivo assessment of metabolic and macromolecular information through a Z-spectrum. CEST may provide insight into metabolic changes present in early-stage disease that are not visible in routine clinical imaging, thereby improving tumor delineation. In this work, we use an unsupervised anomaly detection (UAD) strategy to learn the distribution of features present in Z-spectra of healthy tissue and capture their deviations in pathology, foregoing the need for extensive labels. The approach leverages the metabolic information provided by CEST to improve the detection and delineation of glioblastoma and inform further treatment planning. MethodsA 1D convolutional autoencoder (CAE) was implemented to reconstruct Z-spectra from individual tissue voxels. The network was trained on Z-spectra acquired at 9.4T from healthy Sprague-Dawley rats and tested on data acquired from F98 glioma-bearing rats post Gd-administration. For baseline comparisons, Isolation Forest and Local Outlier Factor, which have shown success in anomaly detection, were implemented. For the CAE, our anomaly score was determined to be the mean squared reconstruction error. To facilitate clinical translation and evaluate the robustness of our model for under sampled Z-spectra, acceleration factors of 2x and 7x were performed with two sampling schemes: uniformly skipping frequency offsets and selecting offsets based on feature importance identified by Shapley value analysis and Integrated Gradients (IG). Binarization was performed by determining an optimal anomaly threshold, followed by comparison to ground truth tumor masks. Metrics related to model performance were assessed for baseline anomaly detectors on the fully sampled dataset and for the CAE on fully and under sampled datasets. ResultsThe best baseline anomaly detector was Isolation Forest, with an ROC-AUC of 0.967 and an F1-score of 0.584. Our method, the CAE, accurately reconstructed Z-spectral features, achieving Dice scores of up to 0.72 and outperforming the baseline model with an ROC-AUC of 0.968 and F1-score of 0.642. This model performance remained robust across sampling schemes and acceleration factors, with ROC-AUCs of [~]0.96 and similar Dice scores (up to 0.7). Feature importance analysis indicated that offsets in the range of {+/-}3.0 to 5.0ppm contributed most to the anomaly score. DiscussionThis study successfully demonstrated a UAD pipeline utilizing the Z-spectrum from CEST MRI for metabolically informed tumor delineation. The framework captures biochemical deviations that may precede or extend beyond morphologic abnormalities, enabling sensitive detection of tumor regions and intra-tumoral heterogeneity that previous methods may fail to capture. The offsets from the feature analysis indicated a strong contribution from the magnetization transfer (MT) pool to the spectral deviations captured by the model, with additional contributions from relayed nuclear Overhauser effect (rNOE) and amide proton transfer (APT). Model robustness with under sampling further highlights the pipelines potential in accelerated acquisitions, thus improving clinical practicality. While there is a need for validation on larger cohorts and clinical datasets, the current results demonstrate that this label-free, Z-spectral anomaly mapping can serve as an interpretable and scalable tool for monitoring tumor heterogeneity and progression, with potential applicability to other diffuse or metabolically subtle pathologies.

Pigs are an important translational research model for biomedical imaging studies, and especially for modeling diseases of the liver. Dynamic contrast enhanced (DCE)-MRI is experimentally used to measure liver function in humans, but has never been characterized in pig liver. Here we performed DCE-MRI of pig liver following the delivery of two FDA approved hepato-specific MRI contrast agents, Gd-EOB-DTPA (Eovist) and Gd-BOPTA (Multihance), and the non-hepatospecific agent Magnevist, and optimized the anesthesia and animal handling protocol to acquire robust data. A single pig underwent 5 scanning sessions over six weeks, each time injected at clinical dosing either with Eovist (twice), Multihance (twice) or Magnevist (once). DCE-MRI was performed at 1.5T for 60 minutes. DCE-MRI showed rapid hepatic MRI signal enhancement following IV injection of Eovist or Multihance. Efflux of contrast agent from liver exhibited kinetics similar to that in humans, except for one hyperthermic animal where efflux was very fast. As expected, Magnevist was non-enhancing in the liver. The hepatic signal enhancement from Eovist matched that seen in humans and primates, while the hepatic signal enhancement from Multihance was different, similar to rodents and dogs, likely the result of differential hepatic organic anion transport polypeptides. This first experience with these agents in pigs provides valuable information on contrast agent dynamics in normal pig liver. Given the disparity in contrast agent uptake kinetics with humans for Multihance, Eovist should be used in porcine models for biomedical imaging. Proper animal health maintenance, especially temperature, seems essential for accurate and reproducible results.

The ability to accurately identify and characterize regions of hypoxia has been an active area of study due to the biological ramifications of low oxygen tension in different pathological conditions, including inflammation, infections, wound healing, cardiovascular conditions, kidney and pulmonary diseases, hepatic and neurological toxicities, and cancer. Although hypoxia contributes a significant role in these conditions, the ability to accurately and instantaneously monitor the presence of hypoxia in vivo and correlate comprehensive analysis of hypoxia-dependent molecular signatures in response to treatments regimens is lacking. With the advent of hypoxia-responsive contrast agents for magnetic resonance imaging (MRI), including the recent development and characterization by our team of a novel probe that involves both 19F and EuII, the capability to integrate direct hypoxia imaging in real-time with new technologies that enable spatial transcriptomic profiling has become a possibility. To assess the capability of this agent for studying hypoxia, we used osteosarcoma as a model. In this preliminary study we demonstrate two major results: First, we show that convection-enhanced delivery (CED) is a reliable and robust methodology to distribute MRI contrast agents throughout a tumor. Second, we show that integration of direct hypoxia-detecting imaging modalities and spatial biology enables real-time in vivo insights into biology and identification of biomarkers directly applicable to disease development and response to therapy. The ability to identify and define hypoxia-mediated biology using direct rather than indirect MRI methods has extremely significant implications in the care of a wide-range of pathological conditions. Consequently, the framework outlined in these preliminary studies is applicable to other pathological conditions and provides the basis for direct in vivo hypoxia detection, monitoring, and biological analyses with translational applications to patient care and management.

Short-TE proton MRS is used to study metabolism in the human brain. Common analysis methods model the data as linear combination of metabolite basis spectra. This large-scale multi-site study compares the levels of the four major metabolite complexes in short-TE spectra estimated by three linear-combination modelling (LCM) algorithms. 277 medial parietal lobe short-TE PRESS spectra (TE = 35 ms) from a recent 3T multi-site study were pre-processed with the Osprey software. The resulting spectra were modelled with Osprey, Tarquin and LCModel, using the same three vendor-specific basis sets (GE, Philips, and Siemens) for each algorithm. Levels of total N-acetylaspartate (tNAA), total choline (tCho), myoinositol (mI), and glutamate+glutamine (Glx) were quantified with respect to total creatine (tCr). Group means and CVs of metabolite estimates agreed well for tNAA and tCho across vendors and algorithms, but substantially less so for Glx and mI, with mI systematically estimated lower by Tarquin. The cohort mean coefficient of determination for all pairs of LCM algorithms across all datasets and metabolites was [Formula], indicating generally only moderate agreement of individual metabolite estimates between algorithms. There was a significant correlation between local baseline amplitude and metabolite estimates (cohort mean [Formula]). While mean estimates of major metabolite complexes broadly agree between linear-combination modelling algorithms at group level, correlations between algorithms are only weak-to-moderate, despite standardized pre-processing, a large sample of young, healthy and cooperative subjects, and high spectral quality. These findings raise concerns about the comparability of MRS studies, which typically use one LCM software and much smaller sample sizes. Graphical Abstract O_TBL View this table: org.highwire.dtl.DTLVardef@6775bdorg.highwire.dtl.DTLVardef@62f27borg.highwire.dtl.DTLVardef@1d77ccorg.highwire.dtl.DTLVardef@a4049aorg.highwire.dtl.DTLVardef@2a1feb_HPS_FORMAT_FIGEXP M_TBL C_TBL Three linear-combination algorithms (Osprey, Tarquin and LCMode) were used to quantify the levels of tNAA, tCho, mI, and Glx in 277 short-TE PRESS. Group means and CVs of metabolite estimates agreed well for tNAA and tCho, but substantially less so for Glx and mI with a cohort mean correlation coefficient of [Formula], indicating moderate agreement between algorithms. These findings raise concerns about the comparability of MRS studies, which typically use one LCM software and much smaller sample sizes.

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.

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.

Despite the increased water content in fibrotic livers, numerous studies reported a decrease in ADC (apparent diffusion coefficient) in liver fibrosis. We argue that the ADC decrease in fibrotic livers is due to the T2 shine-through of ADC, as the longer T2 in liver fibrosis leads to less signal decay between the low and high b-value images. The metric slow diffusion coefficient (SDC) was proposed to mitigate the difficulties associated with this T2 shine-through of ADC. This study calculated ADC and SDC of one rat study with liver fibrosis induced by biliary duct ligation (BDL), and three sets of human liver fibrosis data. To tease out the menopausal effect on SDC, only the results of mens livers were analysed for the human datasets. The rat study showed, liver ADC decreased stepwise (in weeks after BDL procedure) following fibrosis induction, SDC increased stepwise. In human studies, all three datasets consistently showed advanced fibrosis had an ADC lower than that of earlier stage fibrosis; advanced fibrosis had a SDC higher than that of earlier stage fibrosis. When each liver SDC datum was normalized by the mean value of the controls without fibrosis, and the three human datasets were summed together, stage-1 liver fibrosis had a normalized SDC value lower than that of the controls, and there was a stepwise increase of SDC value from stage-1 liver fibrosis to stage-4 liver fibrosis. It is known that liver fibrosis is associated with lower perfusion, higher iron/susceptibility, and higher water content, and these three factors all contribute to the lower ADC measure. Higher iron/susceptibility lowers SDC measure, whereas higher water content elevates SDC measure. It is likely that for early-stage fibrosis, the net effect of susceptibility and water leads to a lower SDC, while for advanced fibrosis, the net effect leads to a higher SDC.

Hyperpolarized carbon-13 MRI is a promising technique for in vivo metabolic interrogation of alterations between health and disease. This study introduces a model-free formalism for quantifying the metabolic information in hyperpolarized imaging. This study investigated a novel model-free perfusion and metabolic clearance rate (MCR) model in pre-clinical stroke and in the healthy human brain. Simulations showed that the proposed model was robust to perturbations in T1, transmit B1, and kPL. A significant difference in ipsilateral vs contralateral pyruvate derived cerebral blood flow (CBF) was detected in rats (140 {+/-} 2 vs 89 {+/-} 6 mL/100g/min, p < 0.01, respectively) and pigs (139 {+/-} 12 vs 95 {+/-} 5 mL/100g/min, p = 0.04, respectively), along with an increase in fractional metabolism (26 {+/-} 5 vs 4 {+/-} 2 %, p < 0.01, respectively) in the rodent brain. In addition, a significant increase in ipsilateral vs contralateral MCR (0.034 {+/-} 0.007 vs 0.017 {+/-} 0.02 s-1, p = 0.03, respectively) and a decrease in mean transit time (MTT) (31 {+/-} 8 vs 60 {+/-} 2, p = 0.04, respectively) was observed in the porcine brain. In conclusion, MCR mapping is a simple and robust approach to the post-processing of hyperpolarized magnetic resonance imaging.

PURPOSEGlycolytic production of HDO from the metabolism of perdeuterated glucose provides a means for metabolic imaging with 2H MRI. The present study compared HDO production from a cost-efficient [2,3,4,6,6-2H5]glucose with [2H7]glucose in vitro and in vivo. METHODS2H NMR spectroscopy was performed to measure glucose consumption, lactate, and HDO production in the SFxL glioblastoma cell line. In vivo studies in healthy mice using 2H magnetic resonance spectroscopy were performed at 11.1 T after administering a bolus of either metabolic contrast agent. In vivo metabolite levels were quantified using unlocalized and slice-selective localized spectra. RESULTSOur in vitro results demonstrated similar glucose consumption and HDO production kinetics, although significant differences in lactate labeling were observed. The in vivo study showed comparable glucose consumption and HDO production kinetics following tail-vein bolus administration of either metabolic contrast agent, while lactate was not detected in the brain. CONCLUSION[2,3,4,6,6-2H5]glucose shows comparable HDO production to [2H7]glucose, while offering lower cost and reduced spectral complexity. These findings place [2,3,4,6,6-2H5]glucose as an alternative to [2H7]glucose for HDO-based DMI studies.

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.

AimLiver vessel density can be evaluated by an imaging biomarker DDVD (diffusion derived vessel density): DDVD/area(b0b2) = Sb0/ROIarea0 - Sb2/ROIarea2, where Sb0 and Sb2 refer to the liver signal when b is 0 or 2 (s/mm2); ROIarea0 and ROIarea2 refer to the region-of-interest on b= 0 or 2 images; and Sb2 may be replaced by Sb15 (b=15). This concept was validated in this study.\n\nMaterials and MethodsLiver diffusion images were acquired at 1.5T. For a scan-rescan repeatability study of 6 subjects, b-values of 0 and 2 were used. The validation study composed of 26 healthy volunteers and 19 consecutive suspected chronic viral hepatitis-b patients, and diffusion images with 16 b-values of 0, 2, 4, 7, 10, 15, 20, 30, 46, 60, 72, 100, 150, 200, 400, 600 were acquired. Four patients did not have liver fibrosis, and the rest were four stage-1, three stage-2, four stage 3, and one stage-4 patients respectively.\n\nResultsIntraclass correlation coefficient for repeatability was 0.994 for DDVD/area(Sb0Sb2), and 0.978 for DDVD/area(Sb0Sb15). In the validation study, DDVD/area(Sb0Sb2) and area(Sb0Sb15) were 14.80{+/-}3.06 and 26.58{+/-}3.97 for healthy volunteers, 10.51{+/-}1.51 and 20.15{+/-}2.21 for stage 1-2 fibrosis patients, and 9.42{+/-}0.87 and 19.42{+/-}1.89 for stage 3-4 fibrosis patients. For 16 patients where IVIM analysis was performed, a combination of DDVD/area, PF, and Dfast achieved the best differentiation for non-fibrotic livers and fibrotic livers. DDVD/area were weakly correlated with PF or Dfast.\n\nConclusionBoth DDVD/area(Sb0Sb2) and area(Sb0Sb15) are useful imaging biomarker to separate fibrotic and non-fibrotic livers, with fibrotic livers had lower measurements.

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.

IntroductionBrain-age is an estimate of the brains biological age derived from neuroimaging data, and has been proposed as a biomarker of brain health and disease risk. While brain-age estimation commonly uses high-field (HF) magnetic resonance imaging (MRI) (> 1.5 T) this is costly and inaccessible, limiting its applicability. Emerging ultra-low-field (ULF) MRI (< 0.1 T) technology is a cheaper and more accessible alternative, but its lower resolution raises questions about whether biomarkers like brain-age can be estimated reliably. MethodsWe assessed different brain-age pipelines in 23 adults scanned on one HF system (GE Signa Premier at 3 T) and two identical ULF systems (Hyperfine Swoop at 64 mT). 14 distinct acquisitions were used, defined by T1-or T2-weighting, resolution, and preprocessing: raw anisotropic orientations (axial, coronal, sagittal), isotropic scans, and super-resolution derivatives from multi-resolution registration (MRR) and SynthSR. These inputs (a total of n = 573 scans) were analysed with five brain-age software packages (BrainageR, SynthBA, MIDI, DeepBrainNet, Py-BrainAge). Performance evaluation entailed validity (brain-age vs. actual age), correspondence (ULF brain-age vs. HF brain-age), and test-retest reliability (ULF1 brain-age vs. ULF2 brain-age). ResultsOverall, performance was mixed across pipelines, though several ULF pipelines achieved performance comparable to HF. The four best-performing combinations were SynthBA on T2 scans without SynthSR, MIDI on T2 scans without SynthSR, PyBrainAge on T1 scans with SynthSR and using FreeSurfer recon-all-clinical, and BrainageR on T1 scans with SynthSR. These showed moderate-to- strong validity (r = 0.76-0.92, R2 = 0.54-0.64, MAE = 6.49-8.21 years), moderate- to-strong correspondence to HF (r = 0.84-0.93, ICC = 0.72-0.92), and excellent test-retest reliability (r = 0.97-0.99, ICC = 0.97-0.99). Moreover, some anisotropic acquisitions achieved comparable validity and reliability to MRR images when tested with the best-performing model, SynthBA (R2 = 0.57-0.62, ICC [CI] = 0.99 [0.97- 1.00], for coronal T2). ConclusionThis first systematic evaluation of brain-age at ULF demonstrates that accurate and reliable estimates can be achieved across multiple pipelines, with- out necessarily requiring image enhancement. Performance depended on the combination of model, scan type, and preprocessing. ULF brain-age estimation could be a practical and scalable tool for clinical decision-making, population research, and long-term patient monitoring, thereby helping to make advanced neuroimaging biomarkers more accessible worldwide.

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.

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.

PurposeJ-difference-edited spectroscopy is a valuable approach for the in vivo detection of {gamma}-aminobutyric-acid (GABA) with MRS. A recent expert consensus article recommends linear combination modeling (LCM) of edited MRS but does not give specific details of implementation. This study explores different modeling strategies to adapt LCM for GABA-edited MRS. Methods61 medial parietal lobe GABA-edited MEGA-PRESS spectra from a recent 3T multi-site study were modeled using 102 different strategies combining six different approaches to account for co-edited macromolecules, three modeling ranges, three baseline knot spacings, and the use of basis sets with or without homocarnosine. The resulting GABA and GABA+ estimates (quantified relative to total creatine), the residuals at different ranges, SDs and CVs, and Akaike information criteria, were used to evaluate the models performance. ResultsSignificantly different GABA+ and GABA estimates were found when a well-parameterized MM3co basis function was included in the model. The mean GABA estimates were significantly lower when modeling MM, while the CVs were similar. A sparser spline knot spacing led to lower variation in the GABA and GABA+ estimates, and a narrower modeling range - only including the signals of interest - did not substantially improve or degrade modeling performance. Additionally, results suggest that LCM can separate GABA and the underlying co-edited MM3co. Incorporating homocarnosine into the modeling did not significantly improve variance in GABA+ estimates. ConclusionGABA-edited MRS is most appropriately quantified by LCM with a well-parameterized co-edited MM3co basis function with a constraint to the non-overlapped MM0.93, in combination with a sparse spline knot spacing (0.55 ppm) and a modeling range between 0.5 and 4 ppm. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/445817v3_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@129ce60org.highwire.dtl.DTLVardef@1ac3797org.highwire.dtl.DTLVardef@1759d69org.highwire.dtl.DTLVardef@b190a3_HPS_FORMAT_FIGEXP M_FIG C_FIG 102 strategies to model GABA-edited MRS with linear combination modeling were evaluated to quantify GABA and GABA+ in Osprey. Significantly different GABA and GABA+ estimates were found when a well-parameterized macro-molecule at 3 ppm was included. The findings suggest that linear combination modeling needs to be adapted for quantification of GABA-edited MRS.

PurposeAccurate analysis of metabolite levels from 1H MRS data is a significant challenge, typically requiring the estimation of approximately 100 parameters from a single spectrum. Signal overlap, spectral noise and common artefacts further complicate analysis, leading to instability and reports of poor agreement between different analysis approaches. One inconsistently used method to improve analysis stability is known as regularisation, where poorly determined parameters are partially constrained to take a predefined value. In this study we examine how regularisation of frequency and linewidth parameters influences analysis accuracy. MethodsThe accuracy of three MRS analysis methods was compaired: 1) ABfit, 2) ABfit-reg and 3) LCModel, where ABfit-reg is a modified version of ABfit incorporating regularisation. Accuracy was assessed on synthetic MRS data generated with random variability in the frequency shift and linewidth parameters applied to each basis signal. Spectra (N=1000) were generated across a range of SNR values (10, 30, 60, 100) to evaluate the impact of variable data quality. ResultsComparison between ABfit and ABfit-reg demonstrates a statistically significant (p < 0.0005) improvement in accuracy associated with regularisation for each SNR regime. An approximately 10% reduction in the mean squared metabolite errors were found for ABfit-reg compared to LCModel for SNR >10 (p < 0.0005). Furthermore, Bland-Altman analysis shows that incorporating regularisation into ABfit enhances its agreement with LCModel. ConclusionRegularisation is beneficial for MRS fitting and accurate characterisation of the frequency and linewidth variability in vivo may yield further improvements.

PurposeThis study aims 1) to implement an operator-independent acquisition, reconstruction, and processing pipeline using a novel rosette k-space pattern for UTE 31P 3D MRSI and 2) to evaluate the clinical applicability and replicability at different experimental setups. MethodsA multicenter repeatability study was conducted for the novel UTE 31P 3D Rosette MRSI at three institutions with different experimental setups. Non-localized 31P MRSI data of 5 healthy subjects at each site were acquired with an acquisition delay of 65 s and a final resolution of 10 x 10 x 10 mm3 in 9 min. Spectra were quantified using the LCModel package. The potential acceleration was achieved using compressed sensing on retrospectively undersampled data. Reproducibility at each site was evaluated using the inter-subject coefficient of variance. ResultsThis novel acquisition and advanced processing techniques yielded high-quality spectra and enabled the detection of the critical brain metabolites at three different sites with different hardware specifications. In vivo, feasibility with an acceleration factor of 4 in 6.75 min resulted in a mean Cramer-Rao lower bounds below 20% for PCr, ATPs, PME, and the mean CoV of ATP/PCr resulted in below %20. ConclusionWe demonstrated that UTE 31P 3D Rosette MRSI acquisition, combined with compressed sensing and LCModel analysis, allows patient-friendly, operator-independent, high-resolution 31P MRSI to be acquired at clinical setups.

BackgroundThe presence of structural and geometric variations within gliomas, even among those with similar histologic grades, reflects the phenotypic heterogeneity unique to a genetic and epigenetic landscape. Whole glioma mass comprises of various subcomponents identified on MR imaging: enhancing, nonenhancing, necrosis, and edema fractions in varied fractions across patients. The geometry of whole tumor mass and the glioma subcomponents is highly irregular. Thereby, traditional Euclidean geometry is not suitable for quantifying the geometric dimensions. Here, we employ non-Euclidean geometric measurements: Fractal Dimension and lacunarity of the glioma subcomponents as a discriminator of IDH and MGMT status of gliomas. MethodsFractality and Lacunarity measurements were obtained using the tumor masks generated for enhancing, nonenhancing, and edema subcomponents from the preoperative T1, T1c, and T2-Flair MRI. Fractality and lacunarity measures of each subcomponent were evaluated between IDH mutant and wildtype gliomas. The fractality and lacunarity measures in IDH mutant and wildtype gliomas were further stratified for MGMT methylated and unmethylated gliomas. The fractality and lacunarities were trained and tested using supervised ML modeling as discriminators of IDH and MGMT status. Further, Cox Hazard estimations and the Kaplan-Meir investigations were performed to evaluate the impact of fractality and lacunarity measures of glioma subcomponents on the overall survival of the patients. ResultsIDH wildtype gliomas had [~]2-fold higher fractality for the enhancing subcomponent compared to IDH mutant enhancing subcomponent, while IDH mutant gliomas showed higher fractality for the nonenhancing subcomponent. Furthermore, the edema subcomponent did not differ for fractality or lacunarity measures between IDH mutant and wildtype gliomas. Fractal or lacunarity measures for either of the three subcomponents do not vary across MGMT methylated and unmethylated status with a given IDH mutant or wildtype gliomas. A combination of fractal measures of the enhancing and nonenhancing subcomponents together provided highly accurate and sensitive discrimination of IDH status using the supervised ML models. Moreover, fractality measure [&ge;] 0.69 for the enhancing subcomponent was associated with shortened patient survival: a fractal dimension value corresponding to that of IDH wild type gliomas. However, fractality and lacunarity estimates were not sensitive for discrimination of MGMT status. ConclusionGlioma structural heterogeneity measured as fractality and lacunarity using routine structural MRI measurements provide a noninvasive quantitative platform definitive of the molecular subtype of gliomas: IDH mutant vs. wildtype. Establishing fractality and/or lacunarity quantities as signatures of prognostic molecular events provides an avenue to bypass the need of biopsy/surgical interventions for decision-making, determining the molecular subtypes and overall clinical management of gliomas. Importance of the StudyThe non-Euclidean geometric measurements such as fractal dimension and lacunarity of enhancing, nonenhancing, and edema subcomponents are potentially unique quantitative metrics, discriminative of IDH status and patient survival. Fractality and Lacunarity estimates using the conventional structural MRI (T1w, T1C, T2, and T2F) provide an easy-to-use quantitative radiogenomics platform for improved clinical decisions, bypassing the need for immediate surgical interventions to ascertain prognostic molecular markers in gliomas, which is likely to improve overall clinical management and outcomes. Key PointsO_LIIncreased fractal dimensions of the enhancing subcomponents in IDH wildtype tumors, suggestive of highly irregular geometry, may potentially serve as a quantitative noninvasive determinant of IDH wildtype tumors. C_LIO_LIA combined fractal estimation of enhancing and nonenhancing subcomponents is the optimal and accurate discriminator of IDH mutant vs. wildtype. C_LIO_LIHigh fractal dimension of enhancing subcomponent and reduced fractality of nonenhancing subcomponent is predictive of shortened patient survival. C_LI

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.