ABSTRACT Magnetic resonance spectroscopic imaging (MRSI) is a non‐invasive neuroimaging tool that maps brain metabolites, providing insights into brain physiology and pathology. However, clinical adoption remains limited due to low spatial resolution and partial volume effects, where acquired signals detect metabolism from different tissue types, preventing clear identification of tissue‐specific metabolic patterns. We developed a statistical framework based on non‐negative geographically weighted regression (NNGWR) designed to overcome these limitations. The method integrates tissue partial volumes with MRSI data using spatially adaptive regression to interpolate metabolite levels across the imaging volume while separating distinct tissue contributions. Importantly, our model accounts for spatial heterogeneity within tissues rather than assuming metabolic uniformity, providing more physiologically realistic estimates. Simulations using synthetic ground truth data show that our method consistently achieves a lower median absolute percentage error than cubic spline and zero‐filling interpolations, especially under high noise or low‐resolution conditions. A total of 120 MRSI datasets were acquired at 3 T from a cohort of 36 healthy volunteers to assess predictive performance. To handle real‐world data, we implemented a robust reweighting scheme that addresses variable spectral quality by progressively downweighting degraded data rather than discarding it, balancing data retention with model stability. Application to a glioblastoma clinical case demonstrated successful separation of metabolite ratios across healthy, tumour and necrotic tissues. The model identified intratumoural metabolic heterogeneity and provided local confidence for transparent clinical interpretation. By decomposing metabolic contributions by tissue type and accounting for natural spatial variations, the approach enables improved brain tumour characterisation. This model is applicable to other neurological conditions where tissue‐specific metabolic alterations serve as disease biomarkers, helping both clinical diagnostics and research into brain pathophysiology.
ABSTRACT Time‐resolved arterial spin labeling (ASL)‐based noncontrast 4D MRA enables visualization of cerebral arterial flow dynamic patterns and holds great potential for evaluating cerebrovascular disease. However, conventional ASL 4D MRA implementations using look‐locker acquisitions are susceptible to progressive RF saturation during extended readouts, leading to substantial ASL signal attenuation and loss of vessel delineation in distal arteries. These limitations potentially hinder a wider clinical translation of 4D MRA, especially when assessment of whole‐brain vascular anatomy and flow dynamics is needed. This study introduces a practical and reliable ASL 4D MRA technique for whole‐brain or large volumetric imaging by integrating pulsed ASL with undersampled multiple overlapping thin‐slab stack‐of‐stars (SOS) golden‐angle radial acquisition. The proposed method was evaluated against conventional single‐slab 4D MRA, 3D time‐of‐flight magnetic resonance angiography (TOF‐MRA), and pseudocontinuous ASL (pCASL)‐based 4D MRA in terms of vessel delineation, blood flow dynamics, and image quality. Compared to the other methods, the proposed method substantially reduced RF saturation effects and preserved signal intensities in distal small arteries, resulting in significantly improved vessel continuity and contrast throughout the cerebral vasculature, as well as reliably capturing complete inflow‐to‐outflow arterial flow dynamics with good scan efficiency and image quality. The proposed technique enables high‐quality, time‐resolved whole‐brain angiography, offering a promising noncontrast imaging tool for comprehensive assessment of cerebral arterial hemodynamics in clinical and research applications.
ABSTRACT Chemical exchange saturation transfer magnetic resonance fingerprinting (CEST‐MRF) is a promising quantitative molecular imaging technique. To reduce scan time, current CEST‐MRF implementations typically use echo‐planar imaging (EPI) readouts, which are prone to geometric distortion and signal dropout. In this study, we developed a motion‐robust, geometrically accurate CEST‐MRF method using radial k‐space sampling, locally low‐rank reconstruction, and neural network–based quantification. The acquisition schedule was optimized using deep learning, and its accuracy was validated in numerical simulations with digital phantoms. The number of spokes per measurement was determined through simulations and in vivo ablation studies in healthy volunteers. Five healthy subjects underwent repeated scans, and regions of interest were defined for analysis. Tissue maps generated with the proposed method were compared with values obtained from nonlinear least‐squares (NLS) fitting of multisaturation‐power z‐spectra. Motion sensitivity and test–retest reproducibility were evaluated using the coefficient of variation (CV) and intraclass correlation coefficient (ICC). Clinical feasibility was demonstrated in a subject with a history of remote middle cerebral artery infarction. The results show that 3D quantitative CEST maps can be acquired in 11 min using 34 spokes per measurement. Numerical simulations yielded mean errors below 14% for all tissue parameters, while in vivo parameter estimates agreed well with both NLS‐derived values and prior brain CEST‐MRF studies. The mean ICC across all tissue maps was 0.92 in white matter and 0.87 in gray matter, with mean intersubject CVs of 5.4% and 3.4%, respectively. The mean error between motion and no‐motion conditions was 8.6%. In the pathology case, changes in CEST‐MRF tissue parameters were consistent with clinically diagnosed cystic gliosis. This approach enables accurate and reproducible 3D quantitative CEST imaging of the brain within clinically feasible scan times.
ABSTRACT Robust cardiorespiratory motion correction is critical for high‐quality ultra‐high‐field (UHF) preclinical cardiac magnetic resonance imaging (MRI). Whereas radial tiny golden angle (tyGA) sampling, data‐driven self‐gating (sg), compressed sensing (CS), and ultrashort echo time (UTE) are individually well‐established techniques, their integration into a unified acquisition and reconstruction pipeline, together with systematic validation at 11.7 T remains insufficiently explored. This study presents and validates a UHF‐optimized tyGA‐based UTE workflow that enables high‐temporal‐resolution sliding‐window dynamic imaging (SW‐tyGA‐UTE) and high‐spatial‐resolution cardiorespiratory self‐gated cine imaging (Gated‐tyGA‐UTE) from a single continuous acquired tyGA acquisition. All experiments were performed on an 11.7‐T small‐animal MRI system in 16 healthy wild‐type mice. Cardiac functional parameters, image quality metrics (signal‐to‐noise ratio [SNR] and contrast‐to‐noise ratio [CNR]), and inter‐/intra‐observer reproducibility were systematically compared between the proposed tyGA‐UTE pipeline and two navigator‐gated reference techniques: retrospectively gated FLASH and UTE methodologies. SW‐tyGA‐UTE and Gated‐tyGA‐UTE reconstructions yielded global left ventricular functional parameters comparable to those obtained with the reference methods (all p > 0.05). All functional metrics showed excellent intra‐observer (ICC = 0.94–0.99) and inter‐observer (ICC = 0.90–0.98) reproducibility, with ejection fraction showing the highest measurement consistency. Image quality was evaluated using an image‐based within‐ROI noise quantification approach that captures both thermal noise and reconstruction‐related artifacts. In this analysis, the Gated‐tyGA‐UTE protocol showed no statistically significant differences in left ventricular blood‐pool SNR, myocardial SNR, or CNR relative to the reference sequences. In contrast, the high‐temporal‐resolution sliding‐window reconstruction showed significantly reduced image quality across all three metrics, consistent with the expected spatiotemporal trade‐off of undersampled dynamic imaging. This study validates a robust, UHF‐optimized tyGA‐UTE imaging workflow that generates both high‐fidelity quantitative cine data and ultra‐fast dynamic cardiac images from a single acquisition. The unified pipeline eliminates the need for sequential repeated scans, minimizes anesthesia‐related physiological variability, and improves experimental efficiency, providing a practical tool for preclinical cardiac phenotyping, longitudinal studies, and drug screening.
ABSTRACT Neurofeedback training is a procedure in which participants learn to modulate brain activity through their own efforts. Here, we designed a real‐time neurofeedback training using functional magnetic resonance spectroscopy (fMRS) to modulate the concentration of glutamate, a major excitatory neurotransmitter, and its precursor glutamine (collectively referred to as Glx) in a volume‐of‐interest (VOI) located in the occipital lobes. During an induction phase participants employed a self‐selected strategy to increase or decrease Glx concentration relative to a reference concentration measured prior to neurofeedback training. Participants were informed about their performance after the induction phase by displaying a disk in green for success or red for failure. The disk size indicated the magnitude of success or failure. The procedure was repeated over the course of 30 trials. Participants were randomly assigned to experimental and control groups (15 participants per group). The experimental group received veridical feedback about their success or failure in the induction phase. The control group received random feedback. Glx concentration in the VOI was measured using 2‐s‐long consecutive Point RESolved Spectroscopy (PRESS) scans in a 3‐Tesla MRI scanner. The results showed that depending on group assignment, participants in the experimental group learned to increase or decrease Glx concentration over the course of training. No significant changes in Glx concentration were observed in the control group. These findings suggest that, with the help of veridical performance feedback, participants learned to modulate Glx concentration in the VOI in the desired direction over a relatively short period of time. fMRS‐based neurofeedback training may complement existing neurofeedback approaches by targeting neurotransmitter‐related processes.
