Multimodal anomaly maps can reveal distributed structural and functional connectivity deviations in glioma, but resting-state functional connectivity also reflects scan-day motion, vigilance, symptoms, seizures, and medication exposure. We propose prespecifying whether the target is tumor-associated alteration, all-cause patient-state deviation, or prognosis; recording a compact clinical-state vector; auditing state leakage and repeatability; and externally validating incremental clinical value before patient-level use.
Hyperpolarized 129Xe chemical shift imaging (CSI) is emerging as an alternative approach to Dixon-based methods for assessing pulmonary gas exchange. CSI additionally permits mapping spectral parameters like linewidths, enabling more accurate, regional estimation of T 2 * . Moreover, regionally mapping the 129Xe chemical shift in red blood cells (RBCs) offers a novel means to assess arteriolar pathology through local measurements of blood oxygenation. However, such CSI-based mapping requires standardized acquisition and processing workflows along with well-defined healthy reference distributions to enable robust quantification of patterns reflecting normal physiology. To this end, we implemented a fast, elliptically sampled Cartesian 129Xe CSI acquisition at 3 T, exciting the dissolved resonances at 208 ppm. We used simulations to determine the optimal k-space filter and minimum SNR for reliable spectral fitting. This approach was applied in a cohort of healthy 18-30-year-olds (N = 11) to establish healthy reference distributions for quantitative mapping of RBC and membrane T 2 * , RBC chemical shift, and the RBC:Membrane signal ratio. CSI reference mean values ( ± 1 standard deviation after Box-Cox transform) for membrane and RBC T 2 * were 1.07 ms (0.97-1.17 ms) and 1.13 ms (1.05-1.22 ms), respectively, with an RBC shift of 217.9 ppm (217.2-218.5 ppm), and RBC:Membrane of 0.44 (0.37-0.54). CSI-derived mean values were all significantly different from those measured by whole-lung spectroscopy (p ≤ 0.02). Notably, RBC:Membrane and membrane T 2 * increased significantly from the gravitationally non-dependent to dependent lung regions, while isogravitational heterogeneity of RBC T 2 * and RBC shift decreased. Conversely, in a preliminary cohort of pulmonary hypertension (PH) patients (N = 4), RBC shift heterogeneity was elevated, even in gravitationally dependent lung, although significance was not reached in this small sample (p = 0.07). These results demonstrate how standardized quantitative 129Xe CSI facilitates rigorous characterization of cardiopulmonary disease signatures and, specifically, highlight RBC shift heterogeneity as a promising marker of PH.
Perfusion MRI, especially dynamic susceptibility contrast (DSC), is vital for glioma diagnosis and monitoring. While gradient-echo DSC is the current recommended implementation, combined spin- and gradient-echo (SAGE) DSC additionally allows for vessel size imaging (VSI) and has shown feasibility for microvascular characterization at 3 T. Higher field strength MRI offers increased contrast, resolution and signal-to-noise ratio, providing potential to improve microvascular characterization. This feasibility study aimed to implement a protocol for SAGE-DSC at 7 T and study its potential for microvascular characterization. SAGE-DSC was acquired in eight patients undergoing treatment for glioma (four females, median age 57 years) at 3 and 7 T. Data were analyzed using the temporal signal-to-noise ratio (tSNR) and contrast-to-noise ratio (CNR) and by comparing perfusion maps, VSI and vascular architectural imaging (VAI). Simulations were performed to study the relationship between vessel size and ∆ R 2 * for SAGE-DSC at 3 and 7 T. Contrast agent dosage could be reduced while maintaining higher CNR at 7 T (but lower tSNR). Assessment by an experienced neuroradiologist showed similar rCBV map quality at 3 and 7 T. VSI maps showed the same normal-appearing gray-to-white matter ratios while in-plane resolution improved at least 52% at 7 T. VAI hysteresis loops at both field strengths traversed the same counter-clockwise direction, with increased loop areas reflecting increased susceptibility effects at 7 T. Simulations confirmed the higher sensitivity of gradient-echo and the higher specificity of spin-echo to the microvasculature, with peak spin-echo specificity moving from 3 μm at 3 T towards 1.5 μm at 7 T. This study confirmed the feasibility of using SAGE-DSC at 7 T for microvascular characterization. rCBV and VSI maps show similar behavior while allowing for reduced contrast agent dosage and improved resolution. The spin-echo specificity shifted towards smaller vessel radii for increasing field strengths. These findings form the basis for exploration and validation in larger patient populations.
Advances in molecular characterization have significantly redefined our understanding of breast tumor heterogeneity, enabling improved diagnostics and the development of tailored therapeutic strategies. In particular, integrative omics approaches offer a broader scope for exploring the molecular and biochemical complexity of breast cancer. Notably, metabolomics through high-resolution magic angle spinning (HR-MAS) NMR spectroscopy enables nondestructive metabolic profiling of intact tissue samples, preserving the native biochemical context and offering unique potential for refining breast cancer subtyping and gaining deeper insight into disease biology. However, the reproducibility and comparability of HR-MAS-based metabolomics across studies are still limited by methodological variations, underscoring the need for standardized and reliable protocols to manage the complexity of breast cancer tissue spectra. In this study, LCModel analysis in combination with the Electronic REference To access In vivo Concentrations (ERETIC2) method as a quantitative reference was implemented for absolute metabolite quantification of breast cancer samples, providing a robust metabolic profiling approach. Generalized linear models were applied to evaluate the associations between quantified metabolites and tumor immunohistochemical classification, and transcriptomic correlation analysis was used to explore gene expression profiles underlying the observed metabolic alterations. A total of 27 metabolites were quantified from breast tumor samples spanning the Luminal A, Luminal B (human epidermal growth factor receptor 2-negative, HER2-), Luminal B (HER2+), and triple-negative breast cancer (TNBC) subtypes. Strong positive associations were observed between phosphocholine (PCho), betaine (Bet), total choline (tCho), and total creatine (tCr) and immunohistochemical subtype, whereas alanine, glucose, and myo-inositol showed negative associations. Notably, Luminal B (HER2-) tumors exhibited the highest PCho levels, linked to upregulated expression of choline kinase alpha (CHKA) and other enzymes of the Kennedy pathway. In contrast, Luminal B (HER2+) tumors exhibited elevated levels of alanine (Ala), glucose (Glc), and myo-inositol (Ins), indicating distinct metabolic reprogramming within the luminal subgroups. Additionally, this study highlights the potential role of betaine (Bet) in interconnecting metabolic and epigenetic pathways in breast cancer, as its concentrations were found to vary across subtypes and correlate with genes involved in fatty acid metabolism, DNA methylation, and estrogen signaling. Robust HR-MAS NMR profiling captured subtype-dependent metabolic signatures, and integrative analysis with transcriptomics identified gene expression differences associated with these signatures, thereby advancing the understanding of breast cancer heterogeneity.
High-resolution quantitative susceptibility mapping (QSM) combined with susceptibility source decomposition provides a powerful approach for investigating the magnetic susceptibility alterations in Alzheimer's disease (AD). In this study, ex vivo three-dimensional multi-echo gradient-echo (mGRE) images of 5xFAD and A p p SAA mouse brains were acquired at 30 μ m isotropic resolution using a 9.4 T MRI scanner. QSM was reconstructed and decomposed into diamagnetic component susceptibility (DCS) and paramagnetic component susceptibility (PCS). Individual amyloid-beta ( A β ) plaques were automatically detected and their susceptibility properties were quantitatively characterized. DCS and PCS revealed subvoxel mixtures of diamagnetic and paramagnetic components. Compared with conventional QSM, DCS exhibited clearer plaque visualization and higher plaque detection sensitivity. Histological analysis revealed the coexistence of A β aggregates and ferritin in both mouse models, with most plaques measuring less than 70 μ m in diameter. To assess the impact of spatial resolution on plaque visualization and susceptibility quantification, the acquired k-space data were downsampled to isotropic resolutions of 45, 60, and 90 μ m. As spatial resolution became coarser, plaque visibility degraded progressively, resulting in reductions in both the number of detectable plaques and the estimated plaque burden. Whole-cortex plaque loading decreased from 8.9% at 30 μ m to 3.1% at 90 μ m in 5xFAD and from 6.5% to 2.6% in A p p SAA mice. In both models, detected plaques exhibited predominantly diamagnetic susceptibility, with the diamagnetic component accounting for approximately 80% of the total absolute susceptibility. These findings demonstrate that high-resolution susceptibility mapping combined with source decomposition enables plaque-level characterization of diamagnetic and paramagnetic contributions and provides complementary biomarkers for assessing A β pathology and associated iron dysregulation in preclinical mouse models of AD.
Glutamate and glutathione share a complex and tightly regulated cycle where imbalances can be a symptom of neurocognitive disorders. Magnetic resonance spectroscopy (1H-MRS) is a noninvasive in vivo imaging technique used to quantify the concentration of human brain metabolites. Metabolite-targeted techniques produce simple brain spectra that use simple fitting models. This reduces algorithmic variability and increases the precision of measurements of the metabolite(s) of interest. Common metabolite-targeted techniques using spectral editing have drawbacks such as increased scan time and low signal-to-noise ratio efficiency and are prone to motion-related subtraction artifacts. We introduce a custom single-shot multifrequency-selective sequence known as double-DANTE-PRESS that does not involve spectral editing. This sequence uses narrowband refocusing pulses allowing the users to refocus the signal from within two frequency passbands of interest while suppressing unwanted signals from outside the passbands. This produces simpler spectra mostly containing signals from metabolites of interest. In this study, we use double-DANTE-PRESS to acquire measurements of glutamate and glutathione simultaneously and report on its precision at 7 Tesla in phantoms and in vivo human brain. Double DANTE-PRESS (TE/TR = 96 ms/3000 ms) was programmed within the software environment of the Siemens Magnetom 7 Tesla MR scanner at the Centre for Functional Metabolic Mapping of Western University (VE12U). Two scans were obtained on phantoms and in five healthy volunteers (20 × 20 × 20 mm3 voxel at the dorsal anterior cingulate cortex) as an in vivo proof-of-concept to refocus the glutamate multiplet at 2.35 ppm and glutathione singlet at 3.77 ppm simultaneously. In vivo, the mean inter-individual %CV of glutathione and glutamate were less than 10% and 7%, respectively, and the mean %CRLBs of glutathione and glutamate were 10% and 5%. Double-DANTE-PRESS can allow for the simultaneous detection of glutamate and glutathione. This technique simplifies spectral quantification allowing for precise and simultaneous measurements. Future work includes a formal in vivo test-retest study and application to clinically driven research in patients with neurocognitive disorders.
Low-field MRI (LF-MRI) improves imaging accessibility, but its practical utility is often limited by reduced signal-to-noise ratio, weaker tissue contrast, and variable image quality. These challenges are further compounded by the limited availability of low-field training data across scanners and the fact that raw k-space data are not always accessible in research and clinical workflows. In this study, we adapted a high-field-trained diffusion model for LF-MRI quality enhancement without low-field retraining or fine-tuning. During inference, the pretrained diffusion prior was combined with noise-level adaptive measurement guidance to suppress low-field noise while maintaining consistency with measurement-supported anatomical structures. With phase augmentation, this framework was further extended to magnitude-only DICOM-exported inputs when measured phase information was unavailable. The method was evaluated on 10 locally recruited healthy volunteers scanned at 0.05, 0.3, and 3 T, a public 0.064-T healthy-subject dataset with 10 paired cases, and patient cases acquired at 0.05 and 0.35 T. Compared with raw LF input and representative baselines including BM3D, MiDiffusion, and NAFNet, the proposed method (Nila) improved visual image quality, noise suppression, and tissue contrast, while preserving lesion-region appearance in patient cases. On the 0.3 and 0.064 T datasets with coregistered 3-T references, Nila achieved the best LPIPS and the highest or tied-highest NMI across contrasts, indicating improved similarity to the high-field reference. Multisample posterior inference further produced uncertainty maps that provide an explainable-AI view of spatial output variability, with elevated variance mainly localized to tissue boundaries and ambiguous regions. These results suggest that high-field diffusion priors can serve as practical and reusable tools for LF-MRI enhancement across heterogeneous systems.
Acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide, and early accurate diagnosis is critical for improving patient prognosis. Current clinical diagnostic methods have inherent limitations and delays, creating an urgent need for reliable novel biomarkers. This study aims to identify differential biomarkers between AMI patients and healthy controls using nuclear magnetic resonance (1H NMR) serum metabolic profiling and to explore their associated metabolic pathways. Serum samples from 32 AMI patients and 42 healthy controls were analyzed based on 1H NMR spectroscopy. Potential biomarkers of AMI were identified and screened using multivariate data analysis. MetaboAnalyst 5.0, an online software, was used for serum metabolic pathway analysis. Moreover, Spearman correlation analysis revealed the correlation between the potential biomarkers and clinical biochemical variables. Finally, the diagnostic model was further constructed using the receiver operating characteristic (ROC) curves to validate the diagnostic performance of the potential biomarkers for AMI. By integrating multivariate analysis, pathway enrichment, and correlation analysis with clinical parameters, this study identified coordinated metabolic reprogramming across energy, amino acid, and lipid metabolism in patients with AMI. We identified 13 representative metabolites as potential biomarkers for AMI patients. Compared with the healthy control group, the AMI group showed upregulation of 10 metabolites and downregulation of 3 metabolites. Furthermore, the diagnostic model constructed based on key metabolites can effectively distinguish between AMI patients and healthy controls. This study revealed metabolomic signatures of AMI, identified potential targets for novel early diagnostic biomarkers, and provided a reference for metabolomics research in cardiovascular diseases.
The aim of this study was to develop and apply an exploratory country-level Helium-MRI Vulnerability Index (HMVI) to assess structural vulnerability of MRI services to helium supply-chain disruption. This cross-sectional ecological study used publicly available country-level data from the World Health Organization, World Bank, Organisation for Economic Co-operation and Development and US Geological Survey. The global HMVI combined three components: MRI infrastructure dependence, helium supply exposure and health-system resilience. MRI dependence was scored by tertiles of MRI units per million population, helium exposure by national helium production status and resilience by tertiles of current health expenditure per capita. Scores ranged from 0 to 6 and were categorised as low (0-2), moderate (3 and 4) or high (5 and 6) vulnerability. A secondary full HMVI incorporating MRI examinations per 1000 population was calculated for countries with available examination volume data. The global analysis included 140 countries; China and India could not be included because MRI-density data were unavailable. The mean global HMVI was 3.94 ± 0.70, with a median of 4 and an observed range of 2-5. Six countries were classified as low vulnerability, 110 as moderate vulnerability and 24 as high vulnerability. HMVI scores differed significantly across WHO regions (Kruskal-Wallis H = 15.150, p = 0.0097) and World Bank income groups (H = 13.396, p = 0.0039). High-vulnerability countries had lower median health expenditure per capita than non-high-vulnerability countries (240.70 vs. 556.28 USD, p = 0.0051), whereas MRI density did not differ significantly (1.17 vs. 1.51 MRI units per million population, p = 0.3751). The full HMVI subgroup included 24 countries, of which eight were classified as high vulnerability. Sensitivity analysis, excluding countries with 2013 MRI-density data, retained 52 countries and identified five as high vulnerability. The HMVI provides an exploratory framework for characterising structural vulnerability of MRI services to helium supply-chain disruption. Vulnerability was shaped not by MRI density alone, but by the interaction between imaging capacity, helium supply exposure and health-system financial resilience. Updated scanner-level data, including helium consumption and low-helium system adoption, are needed to refine future assessments.
Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion are key molecular markers for glioma classification. Amide proton transfer weighted (APTw) and nuclear Overhauser effect-weighted (NOEw) markers showed promise for glioma characterization, by probing protein-related tissue properties. However, their interpretation is confounded by direct water saturation, macromolecules (semi-solid magnetization transfer-ssMT), and T1 relaxation. Here, we aimed to assess the performance of three APTw and NOEw metrics-uncorrected, spillover/ssMT-corrected (FMC), and fully spillover/ssMT- and T1-corrected (FMTC)-for glioma stratification. Fifty patients with suspected gliomas were prospectively enrolled (12 IDH-wild-type, 38 IDH-mutant, of which 21 with 1p/19q codeletion). Acquisitions were performed at 3 T using a 3D gradient echo readout with chemical exchange saturation transfer (B1 = 2 μT for APTw, 0.6 μT for NOEw; T1sat = 2 s), WASABI (WAter Shift And B1) for B0/B1 mapping, and saturation recovery for T1 mapping. Glioma subtypes were compared using metrics extracted from manually segmented masks, using two-tailed Mann-Whitney U tests, and the Benjamini-Hochberg false discovery rate (FDR) correction. Effect sizes were quantified using Cliff's δ with 95% bootstrap confidence intervals and classification performance was assessed by receiver operating characteristic analyses (area under the curve, AUC). IDH-mutant and wild-type gliomas differed significantly for the uncorrected APTw metric (p = 0.005, AUC = 0.79), with stronger discrimination following correction-APTw-FMC (p < 0.001, AUC = 0.94) and APTw-FMTC (p < 0.001, AUC = 0.96), both with large effect sizes. Only APTw-FMTC distinguished 1p/19q codeleted from non-codeleted gliomas before FDR correction (uncorrected p = 0.01, AUC = 0.74). The NOEw metrics did not differ between any molecular subgroups, likely due to limited sensitivity of this contrast at 3 T. These results suggest that correcting for fluid, ssMT, and T1 effects enhances the accuracy of APTw metrics, offering a more robust and biophysically grounded approach to noninvasive glioma diagnosis.
Hepatocellular carcinoma (HCC) exhibits metabolic heterogeneity that is not fully characterized by glycolysis-focused spectroscopic profiling. This study investigated whether in vitro hyperpolarized (HP) [2-13C]pyruvate NMR spectroscopy can identify a mitochondria-active HCC phenotype and assess its association with sensitivity to mitochondrial metabolic inhibition. HP [2-13C]pyruvate NMR spectroscopy was used to evaluate mitochondrial metabolism in McA-RH7777 HCC cells, with N1S1 cells serving as a glycolysis-dominant reference. Cell viability following treatment with the glutaminase inhibitor BPTES and the mitochondrial metabolic inhibitor CPI-613 was assessed by MTT assay, and metabolic changes following CPI-613 treatment were further evaluated using HP [2-13C]pyruvate. HP [2-13C]pyruvate demonstrated enhanced pyruvate-to-glutamate conversion in McA-RH7777 cells, whereas N1S1 showed minimal glutamate labeling. CPI-613 treatment resulted in a dose-dependent reduction in cell viability, while BPTES produced limited effects. Although pyruvate-to-glutamate conversion did not significantly decrease following CPI-613 treatment, pyruvate-to-lactate conversion increased, indicating metabolic adaptation. These findings demonstrate that HP [2-13C]pyruvate enables functional identification of a mitochondria-active HCC phenotype characterized by enhanced pyruvate-to-glutamate conversion. This approach may facilitate metabolic subtype classification, help identify tumors susceptible to mitochondrial metabolic inhibition, and enable non-invasive monitoring of treatment-induced metabolic adaptation.
Despite numerous magnetic resonance imaging (MRI) head motion mitigation strategies, the lack of rigorous evaluation limits their optimization and clinical adoption. We propose an in vivo framework combining a visual instruction system for reproducible head motion with reference standard interpose displacement estimation to assess intra-MRI tracking accuracy and precision. Its utility is demonstrated by comparing a markerless optical system (MOS) and a fat-signal navigator (FatNav). Six participants underwent 3T T1-weighted brain MRI with a FatNav module, performing visually guided 2° and 4° head rotations around the X- and Z-axes using MOS feedback. T1-weighted images were acquired at seven distinct head poses. MOS and FatNav motion estimates were compared against rigid registration of the T1-weighted images, which served as the reference standard. MOS- and FatNav-corrected images for the three successive head rotations were also compared using the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and a focus measure. FatNav accuracy was inferior for translations (p < 0.001) and 2°-4° rotations but improved to match MOS for subtle pitch+ and yaw+, even surpassing it for subtle yaw-. Meanwhile, MOS precision was higher for yaw+ than yaw- (p < 0.001) but inferior to FatNav for pitch+ (p = 0.041). MOS better restored T1-weighted image fidelity, yielding higher SSIM, PSNR, and focus (p < 0.01). Notably, the framework detected a subtle improvement in FatNav performance with neck masking, an effect uncaptured by conventional image quality metrics. In conclusion, while image quality metrics suggested superior overall correction with MOS, our framework provided a more detailed characterization of in vivo performance differences.
ABSTRACT Low‐field MRI has recently gained interest due to its potential for increased accessibility, reduced cost, and improved safety. However, high‐quality anatomical imaging and robust tissue characterization remains an active area of research, particularly when aiming for a simple, one‐click scan that captures all relevant information in a single acquisition. Bright‐blood imaging is widely used for visualizing cardiac structures and coronary arteries, whereas black‐blood is optimal for delineating the myocardium, atrial and vessel walls. High‐resolution imaging is required for the accurate detection and segmentation of small anatomical structures, such as the coronary arteries, to enable assessment of narrowing or blockages. Co‐registered mapping enables quantitative myocardial tissue characterization, offering valuable clinical information for the detection of myocardial abnormalities. In this study, we sought to develop a novel free‐breathing, motion‐compensated 3D multi‐contrast high‐resolution cardiac MR sequence for simultaneous assessment of whole‐heart cardiovascular anatomy via bright‐ and black‐blood imaging and myocardial tissue quantification by joint and mapping at 0.55 T in a single scan. Data were acquired over six interleaved contrasts with various preparation modules using a variable flip angle bSSFP spiral‐like readout with 2D image‐based navigation for translational motion correction, resulting in a predictable acquisition time of min. Images were reconstructed using non‐rigid motion corrected iterative sensitivity encoding followed by high‐dimensional patch‐based low‐rank denoising, resulting in the acquisition, reconstruction and quantitative mapping time of min. In the phantom study, sequence performance was evaluated using correlation and Bland‐Altman analysis against reference gold‐standard and clinical mapping methods. In vivo, 3D bright‐ and black‐blood volumes were assessed in multiple views, and vessel sharpness was quantified from multiplanar images. For joint mapping, bull's‐eye plots were generated to evaluate the mean, standard deviation, and coefficient of variation for apical, mid‐cavity, and basal segments, and results were summarized using violin plots. Differences between the proposed 3D sequence and established 2D methods were analyzed with a two‐tailed ‐test. In the phantom study, a small positive bias in of was observed compared with inversion recovery spin‐echo and with MOLLI, while for biases of compared with spin‐echo and with prep bSSFP were found. In vivo, statistically similar values of and values of were obtained, with differences versus MOLLI of () and versus prep bSSFP of (). The proposed sequence demonstrated high image quality and accurate mapping despite the inherent limitations of low‐field strength, suggesting its feasibility for comprehensive cardiac assessment in resource‐limited environments.
ABSTRACT This study aimed to propose a mixed single‐echo and multiecho MyoFoldstar sequence enabling simultaneous myocardial multiparametric mapping and wall‐motion quantification. MyoFoldstar is designed as a 2D, single breath‐holding sequence that sequentially performs joint T 1 / T 2 mapping, T 2 * mapping, and cine imaging within 17 heartbeats, using a golden‐angle radial gradient‐echo (GRE) readout and ECG synchronization. The joint T 1 / T 2 mapping collects seven single‐shot, single‐echo images on the first seven heartbeats with inversion and T 2 preparation ( T 2 prep). The subsequent T 2 * mapping acquires multishot, multiecho data over six heartbeats, followed by segmented cine with a single‐echo readout over four heartbeats. In vivo feasibility was demonstrated in 12 healthy volunteers at 3 T and compared with conventional single‐task sequences (MOLLI and SASHA for T 1 , T 2 prep bSSFP for T 2 , and BB‐meGRE for T 2 *). Accuracy was validated in phantom studies. Both in vivo and phantom studies demonstrated the feasibility of MyoFoldstar for simultaneously acquiring cardiac T 1 , T 2 , T 2 *, and cine imaging. MyoFoldstar‐derived myocardial T 1 (1521 ± 102 ms), T 2 (43.2 ± 3.0 ms), and T 2 * (19.2 ± 2.4 ms) showed good agreement with values from conventional single‐task sequences ( T 1 : 1248 ± 35 ms by MOLLI and 1580 ± 38 ms by SASHA; T 2 : 43.5 ± 2.2 ms by T 2 prep bSSFP; T 2 *: 23.4 ± 2.6 ms by BB‐meGRE). Compared with conventional cine, MyoFoldstar images exhibited reduced myocardium–blood contrast, yet left ventricular function quantifications were mainly preserved ( r > 0.9). Phantom results indicated that MyoFoldstar achieves good accuracy relative to reference standards. MyoFoldstar enables rapid myocardial T 1 , T 2 , T 2 *, and cine imaging within a single breath‐hold scan, delivering a time‐saving and comprehensive assessment of cardiovascular magnetization resonance.
This study evaluates the repeatability of a 3D simultaneous H-1/Na-23 MR fingerprinting (MRF) sequence in knee cartilage of healthy volunteers. Eight healthy volunteers underwent four knee scans each with 3D simultaneous H-1/Na-23 MRF at 7 T. Proton density (PD), tissue sodium concentration (TSC), and H and Na relaxation time maps were acquired over two visits with two consecutive acquisitions at both visits. Mean values and standard deviations of all MRF metrics were measured in three knee cartilage regions: patellar, femorotibial medial, and femorotibial lateral. Image processing included H-1 and Na-23 MRF dictionary matching, B-1(-) correction, TSC and PD quantification, and image registration between the scans. Repeatability was assessed using the coefficient of variation (CV) and intraclass correlation coefficient (ICC) for all measurements and Bland-Altman plots to compare intraday and interday measurement differences. Mean TSC values over all subjects and cartilage regions were 162 +/- 29 mM, with a CV of 12% +/- 1%. Mean Na-23 T-1 (30 +/- 2 ms) and T-21 (13 +/- 3 ms) values were relatively consistent (CV = 6%-19%), while T showed greater variability (1.62 +/- 1.60 ms, CV = 54% +/- 11%). For H-1, mean PD was 0.87 +/- 0.24 (CV = 20% +/- 8%), mean T was 1114 +/- 168 ms (CV = 11% +/- 5%), and mean T was 36 +/- 15 (CV = 20% 7%). ICC values suggested low-to-moderate discrimination power, with highest values observed for H T and lowest values for Na relaxation times. The Bland-Altman plots suggest similar intraday and interday differences. This study shows that we can acquire quantitative H and Na MRF maps simultaneously in knee cartilage at 7 T. Repeatability was the highest for TSC and Na T. ICC was the highest for PD and H-1 T-1.
Arterial spin labeling (ASL) has been used for perfusion imaging and non-contrast enhanced dynamic MR angiography (MRA), and both have become favorable for clinical diagnosis and treatment planning of cerebrovascular diseases. Separate sequences are typically required to obtain brain vascular hemodynamics and downstream perfusion information. However, concurrent dynamic MRA and perfusion imaging within a single acquisition can provide spatially co-registered macrovascular and microvascular maps to enhance diagnostic confidence and efficiency. In this work, we developed a dual-module ASL technique to concurrently obtain four-dimensional (4D) MRA and cerebral perfusion images from a single scan. Specifically, pseudo-continuous ASL (pCASL) and pulsed ASL (PASL) modules were integrated and encoded with a 3D stack-of-stars golden-angle radial acquisition. 4D MRA and perfusion contrasts were generated through pair-wise subtractions. Sparsity-constrained image reconstruction was used in generating 4D MRA and perfusion images from different portions of radial k-space data. Both numerical simulationsL. and in vivo experiments were performed to demonstrate technical feasibility. Both time-resolved MRA with high spatiotemporal resolution and perfusion-weighted images with good contrast were successfully obtained from a single scan using the proposed dual-module ASL technique. The performance of the proposed technique was compared against the reference 4D MRA technique in terms of vascular delineation and blood flow dynamics, and the conventional pCASL perfusion imaging with 3D GRASE for cerebral blood flow (CBF) measurement. The dual-module ASL showed comparable performance in depicting arterial blood flow dynamics and gray matter CBF quantification (p = 0.73) to the reference 4D MRA and 3D GRASE pCASL, respectively. These results indicate the feasibility of the proposed technique for concurrent 4D MRA and perfusion imaging from a single scan, which could be a potentially powerful imaging tool for the detailed characterization of dynamic blood flow patterns through the cerebrovascular structure and downstream perfusion.
Phosphorus magnetic resonance spectroscopy (31P-MRS) enables noninvasive measurement of brain metabolism, yet its reproducibility in clinical settings remains unclear. We systematically assessed intrasession and intersession variability as well as interindividual differences of key phosphorus metabolites at 3 T in healthy individuals and persons with Parkinson's disease under various experimental conditions. Intersession variability, as measured by coefficients of variation (CoVs) increased notably for longer scan intervals (~1 year), and metabolite ratios from well-resolved spectral signals (i.e., adenosine triphosphate [ATP], phosphocreatine [PCr], and intracellular inorganic phosphate [Pi]) exhibited consistently higher stability compared with ratios calculated from metabolite signals overlapping on the spectrum (e.g., total nicotinamide adenine dinucleotide [tNAD], as well as phosphate monoesters [PMEs] and phosphate diesters [PDEs]). Test-retest variability ranged from ~5 to 25 CoV%, where PCr, ATP-α, and ATP-γ were the most stable while glycerophosphocholine (GPC), glycerophosphoethanolamine (GPE), phosphoethanolamine (PE), and tNAD varied considerably. Interindividual variability was found to be higher than intraindividual variability for all metabolite ratios, ranging from ~9 to 33 CoV%. By systematically quantifying intraindividual and interindividual variability, as well as providing explicit sample size recommendations, this study facilitates more reliable longitudinal and cross-sectional clinical trials and translational studies of brain metabolism featuring 31P-MRS.
Chronic kidney disease (CKD) is a major health burden. Intrarenal microcirculation impairment occurs early in CKD and precedes measurable declines in renal function. We investigated whether contrast-free multiparametric MRI (mp-MRI) can characterize intrarenal microcirculation alterations and improve early CKD detection. This retrospective study included 54 patients with pathology-confirmed CKD (25 mild and 29 moderate-severe by the Katafuchi system) and 20 age- and sex-matched healthy controls (HCs) enrolled between March 2020 and August 2022. All participants underwent an mp-MRI protocol comprising arterial spin labeling (ASL), intravoxel incoherent motion imaging (IVIM), blood oxygen level-dependent (BOLD) imaging, and quantitative susceptibility mapping (QSM). Renal biopsy served as the reference standard, with estimated glomerular filtration rate and serum creatinine as clinical comparators. Group differences in MRI metrics were assessed, correlations with histopathology were evaluated, and multivariable logistic regression models were constructed to distinguish CKD (and mild CKD) from healthy controls. Renal blood flow, diffusion and microperfusion metrics (D, D*, f), BOLD-derived R2*, and susceptibility values differed significantly across groups, and each MRI parameter and clinical biomarker correlated with histopathological severity (|r| = 0.33-0.97). A mp-MRI model integrating RBFCortex, D*Cortex, fCortex, and R2*Cortex achieved excellent discrimination for CKD versus HCs (AUC = 0.976; 95% confidence interval [CI]: 0.949-1.000; p < 0.001) and for mild CKD versus HCs (AUC = 0.948; 95% CI: 0.891-1.000; p < 0.001). These findings support a pathophysiology-driven, contrast-free mp-MRI framework for noninvasive assessment of renal microcirculation and early CKD detection.
