Magnetic Resonance in Medicine

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

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

BackgroundMethylmalonic acidemia (MMAemia) is a genetic metabolic disorder characterized by an accumulation of methylmalonic acid (MMA) and impaired energy metabolism leading to increased lactate (Lac). The signals of MMA (1.23 ppm) and Lac (1.33 ppm) overlap, making their separation using conventional MRS challenging. An MRS method to differentiate the two metabolites could enhance pathophysiological understanding and improve treatment monitoring - Hadamard-edited MRS has the potential to achieve this. PurposeTo develop a Hadamard-encoded J-difference editing approach for independent detection of MMA and Lac at 3T. MethodsA novel Hadamard-encoded editing scheme was implemented and evaluated with density-matrix simulations, phantom and in vivo experiments. The new four-step scheme uses frequency-selective editing pulses, applied at 3.2 ppm and 4.1 ppm to modulate the J-coupled methyl resonances of MMA and Lac, respectively. Hadamard combinations of the four sub-experiments yield the separate difference-edited spectra for each target metabolite. ResultsSimulations and phantom experiments clearly illustrate the separated signals of MMA and Lac. In vivo validation experiments show a Lac signal (but no MMA) in a healthy infant, and both Lac and MMA (separated into their respective Hadamard-combination spectra) in a patient with MMAemia. ConclusionHadamard-encoded editing at 3T can separate MMA and Lac signals and shows promise for studying altered metabolism in patients with MMAemia.

Purpose: To develop a Field-Correcting GRAPPA (FCG) technique to correct the spatiotemporal-varying phase errors in EPI caused by eddy currents. Methods: The fast-changing gradient in EPI causes strong eddy current effects and associated spatiotemporal-varying phase errors, producing significant image artifacts. The use of higher gradient amplitude, slew rate, and ramp sampling factor for faster imaging exacerbates this problem. In this work, FCG was developed to address this challenge by using a multi-layer perceptron (MLP) to provide a compact representation of a family of GRAPPA-like kernels that correct the spatiotemporal-varying phase errors in the data. A dedicated calibration pipeline was designed to acquire high-quality source and target data for MLP training in both slice-by-slice and simultaneous multi-slice (SMS) acquisitions. To validate FCG's assumptions and performance, a field camera was used to provide ground-truth measurement of phase patterns. The performance of FCG was further validated on phantom and in vivo experiments using demanding EPI trajectories across multiple 3T and 7T systems. Results: Field camera measurements revealed strong spatiotemporal phase variations along the kx direction that repeat along ky during EPI readouts. The experiments on high-performance systems across 3T and 7T demonstrate that FCG can provide superior correction for the artifacts induced by spatiotemporal-varying phase errors compared with existing approaches. Conclusion: FCG is an effective and robust method for correcting spatiotemporal phase errors in EPI, enabling improved image quality on high-performance systems.

PurposeTo develop a data-driven technique, Scout-based Multi-Echo NAvigator (SMENA), for joint estimation of motion and B0 inhomogeneity ({delta}B0) at a temporal resolution of [~]200 ms with minimal additional scan time for gradient-echo acquisition. MethodsSMENA consists of two key acquisition components: SMENA-scout and SMENA-nav. SMENA-scout is a rapid 3D 4-mm multi-echo acquisition completed in less than 8 seconds, providing images with matched contrast and phase at multiple echo times. SMENA-nav captures signal variations induced by motion and{delta} B0 during the scan using compact multi-echo navigator trajectories (3.5 ms) embedded within each TR. Motion and{delta} B0 maps were jointly estimated every [~]200 ms through a model-based optimization framework relating SMENA-scout to SMENA-nav. The estimation accuracy and correction performance of SMENA were evaluated in simulations and in vivo using multi-echo GRE and GRE-EPTI acquisitions. Multiple prospective motion experiments, including large continuous movement and deep breathing, were investigated. ResultsIn both simulations and in vivo experiments, accurate motion and{delta} B0 estimation were achieved. Compared with motion-only estimation, joint estimation reduced rotation and translation errors. Joint motion and{delta} B0 correction resulted in substantial improvements in image quality, particularly at longer echo times, producing an NRMSE of 10.4% compared to 31.6% with motion-only correction. High-temporal-resolution tracking of motion and{delta} B0 enabled improved reconstruction quality in scenarios involving continuous motion and deep breathing. ConclusionSMENA enables high-temporal-resolution joint estimation of motion and{delta} B0 with minimal additional acquisition cost, providing a practical solution for motion- and{delta} B0-robust MRI.

SummaryO_ST_ABSPurposeC_ST_ABSTo develop 3D radial spiral phyllotaxis trajectories that provide a uniform density distribution of readout directions and support retrospective sequential binning, thereby reducing ringing artifacts and improving image quality. MethodsUPhy trajectory redefines the polar angle to achieve uniform density distribution of readout directions. FlexiPhy further decouples the azimuthal and polar ordering of interleaves through a randomized permutation, improving robustness to sequential binning. The proposed trajectories were evaluated in vivo on 10 healthy volunteers using two gradient-echo sequences on a 3T MRI scanner. Sequential temporal reconstructions were compared with reference reconstructions using structural similarity and relative L2 error metrics. ResultsUPhy presents analytically demonstrated uniform density distribution of readout directions. Quantitative analysis shows significantly higher SSIM values and lower relative L2 errors for FlexiPhy compared with both the original phyllotaxis and UPhy trajectories after Bonferroni correction (pcorrected < 0.05). ConclusionFlexiPhy enables more reliable sequential binning reconstructions by reducing trajectory-induced ringing artifacts and temporal inconsistencies. Moreover, its randomized construction is not tied to a specific binning strategy, making it broadly compatible with retrospective binning approaches used in dynamic and motion-resolved MRI.

Purpose: To develop an accelerated motion-compensated diffusion-weighted pseudo-continuous arterial spin labeling (MCDW-pCASL) method using a spatial subspace low-rank reconstruction method for efficient quantification of blood-brain barrier (BBB) water exchange (kw) and permeability (PSw). Methods: An accelerated multidelay MCDW-pCASL sequence was developed to simultaneously encode intravascular and extravascular diffusion-weighted ASL signals across multiple post-labeling delays (PLDs). A spatial subspace low-rank reconstruction framework was optimized to enable joint estimation of cerebral blood flow (CBF) and BBB water exchange rate and permeability. Fourteen young healthy adults underwent test-retest scans (separated by ~1 week) at 3T with both the accelerated MCDW-pCASL and a conventional diffusion-prepared (DP) pCASL sequence. Whole-brain, gray-matter, and white-matter CBF and kw values were quantified to assess test-retest repeatability and cross-method agreement. An additional cohort of 30 older adults underwent single-session MCDW and DP scans to evaluate age-related perfusion and BBB kw/PSw differences. Intraclass correlation coefficients (ICCs) were used to assess reliability and agreement. Results: Accelerated MCDW-pCASL demonstrated excellent agreement with DP-pCASL for CBF (ICC = 0.89) and fair agreement for kw (ICC = 0.56). Test-retest repeatability of MCDW-pCASL was good for CBF, BBB kw and PSw (ICC {approx} 0.6). Across both sequences, younger subjects exhibited significantly higher CBF and kw compared with older adults. Conclusion: Incorporating a spatial low-rank subspace reconstruction enables accelerated MCDW-pCASL acquisition with reliable simultaneous quantification of CBF, BBB kw and PSw. Clinical applications of this method for assessing perfusion and BBB function are warranted.

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

Cerebrospinal fluid (CSF) motion is coupled with global blood oxygenation level-dependent (BOLD) fluctuations, but the spatial relationship between regional brain activity and CSF dynamics remains poorly understood. Here, we developed a single-shot BOLD-VENC sequence that combines gradient-echo BOLD imaging with spin-echo velocity encoding following the same RF excitation, enabling simultaneous measurement of brain-wide BOLD activity and spatially resolved slow CSF velocity at 3T. The velocity measurement was validated in a slow-flow phantom and in five healthy participants using paced-breathing, breath-holding, and visual-stimulation experiments. Phantom measurements showed strong agreement with prescribed velocities over 0.1-1.0 mm/s (R2 = 0.93-0.98). In vivo measurements demonstrated respiratory- and cardiac-dependent changes in CSF velocity magnitude and direction across the ventricles and cortical subarachnoid spaces (SAS). The established coupling between the negative derivative of the global BOLD signal and fourth-ventricle CSF inflow was reproduced, with a peak lag of 0.9 s. Global BOLD fluctuations were also coupled with spatially distributed CSF velocity changes across ventricular and cortical CSF spaces, with a similar peak lag of 1.2 s. During visual checkerboard stimulation, BOLD-CSF velocity coupling was localized primarily to the SAS surrounding the activated visual cortex, demonstrating a regional relationship between local BOLD activity and nearby CSF motion. These findings establish the feasibility of simultaneous BOLD and slow CSF velocity imaging and extend BOLD-CSF coupling from a global measure toward spatially resolved assessment of hemodynamic-CSF interactions.

PurposeRecent improvements in MRI gradient design and amplifiers, advanced MRI scanners are now routinely utilizing slew rates of several hundred T/m/s. However, full gradient performance cannot be exploited in high-resolution EPI due to peripheral nerve stimulation (PNS) limits. We aim to characterize and mitigate these PNS constraints using a simple sequence modification: PNS-optimized EPI gradient pulse shapes. MethodsPNS-Optimized Pulses for EPI (POPE): we selectively reduce the slew rate of gradient pulses at periods of high predicted PNS spikes, while leaving the rest of the waveform unchanged. PNS sensation was evaluated. ResultsPOPE allows 7%-35% faster imaging of EPI protocols resolutions of 1mm-0.3mm resolutions without exceeding predicted PNS. With such improvements, POPE allows robust 0.3 mm isotropic fMRI protocols that would have exceeded safety limits without it. ConclusionPOPE facilitates locally precise fMRI activation mapping on clinical 7T scanners at spatial resolution that were previously unattainable due to PNS limitations.

PurposeBreast microcalcifications trigger 70-80% of unnecessary biopsies because current imaging cannot distinguish malignancy-associated hydroxyapatite (HA) from benign-associated calcium oxalate (CaOx). Quantitative susceptibility mapping (QSM) could exploit the susceptibility contrast between these minerals (HA: {Delta}{chi} {approx} -7 ppm; CaOx: {Delta}{chi} {approx} -1 ppm relative to water), but no study has demonstrated compositional differentiation at clinical field strength. This work assessed susceptibility and R2* relaxation rate maps for microcalcification differentiation at 3 T using tissue-mimicking phantoms. MethodsA phantom comprising 12 tubes, each containing co-embedded HA and CaOx particles in BaCl2-crosslinked alginate gels (pure alginate, adipose-mimicking, and fibroglandular tissue-mimicking relaxation properties; n = 4 per type), were scanned at 0.70 mm and 0.86 mm isotropic resolution using a multi-echo gradient echo sequence. A consensus-aligned QSM pipeline and mono-exponential R2* fitting was developed. A digital twin phantom simulation quantified the contributions of partial volume effects and Total Variation (TV) regularisation to susceptibility underestimation. ResultsQSM detected HA in 18/24 measurements ({Delta}{chi}peak = -0.37 {+/-} 0.07 ppm in alginate at 0.70 mm) and CaOx in 0/24. R2* mapping detected HA in 23/24 and CaOx in 22/24. The digital twin identified TV regularisation as the dominant signal loss mechanism (57.5% loss), exceeding partial volume effects (24.3% loss). Combined parameters yielded three classification categories: QSM-positive with elevated R2* (HA), QSM-negative with moderate R2* (CaOx), and neither elevated (no calcification). ConclusionQSM at 3 T enables categorical HA detection while R2* provides complementary CaOx sensitivity, together enabling two-parameter microcalcification classification from a single multi-echo acquisition.

Background and ObjectiveSimulation of diffusion MRI signals from tissue microstructure is a fundamental problem in quantitative imaging, as it enables controlled study of how cellular architecture influences measured signals. However, physics-based simulations at clinically relevant scales are challenging due to a scale mismatch between imaging and histology: clinical diffusion MRI spans centimeter-scale fields of view with millimeter-scale voxels, whereas histology resolves structure at micrometer scales. Capturing voxel-wise signal formation therefore requires repeated simulations over heterogeneous microstructure, which becomes computationally and memory intensive in classical solvers. We propose a neural operator framework that amortizes this cost by learning local microstruc-ture-signal mappings once and applying them across large tissue regions. MethodsWe train a Fourier Neural Operator on finite-element simulations of histology-derived cell segmentations to predict magnetization fields from diffusivity and permeability maps. The model is embedded in a subdomain tiling strategy that enables scalable inference over whole-slide histology images. Unlike most conventional simulation pipelines, inference operates directly on regular grids derived from cell segmentations and does not require meshing. ResultsThe proposed framework enables simulation of apparent diffusion coefficient maps over 2D liver histology spanning 28.224 mm x 18.144 mm. It achieves over 2,600-fold acceleration compared with CPU-based finite-element simulation, reducing runtime from an estimated 217 days to under 2 hours. The network yields mean relative signal errors of 0.34%-0.43% at high diffusion weighting and 0.03% at low diffusion weighting, with maximum errors below 5%. On manually segmented datasets with greater morphological variability, mean errors increased slightly to 1.37%-1.79%. ConclusionsNeural operators enable computationally practical, mesh-free diffusion MRI simulation by amortizing expensive physics-based computation into a reusable operator applied across local sub-domains. This makes large-scale histology-based diffusion MRI modeling feasible while preserving high accuracy.

"Remote palpation" appears to be an oxymoron, but here we demonstrate a non-contacting MRI method to obtain mechanical stiffness parameters of the human brain solely by measuring deformations caused by the pumping action of the heart. Mechanical stiffness is an important tissue property that is highly sensitive to subtle changes in the tissue milieu; MR elastography (MRE) is among a handful of methods used to measure it, typically via an external driver/tamper that introduces mechanical waves into the tissue. Applying MRE in the brain is challenging due to the use of an external actuator/tamper and the mechanical anisotropy of brain tissue, which requires a 4th-order tensor to describe it. In this study, we use the intrinsic deformation of brain tissue caused by periodic cardiac pulsations to measure the 4th-order elasticity tensor throughout the brain while simultaneously estimating the 2nd-order diffusion tensor in each voxel throughout the cardiac cycle which we use as a priori information in the reconstruction of the elasticity tensor. While the DTI-derived mean diffusivity (MD) appears uniform throughout brain parenchyma, stiffness maps obtained at about 1 Hz (i.e., at the fundamental cardiac frequency) show that brain tissue is very soft within gray matter, and within white matter pathways, such as along the corpus callosum, corona radiata, etc. Generally, stiffness differences at internal tissue boundaries are expected to produce local stress concentration there, which may predispose tissues to damage, e.g., in traumatic brain injury (TBI). Therefore, our novel tamperless MRE method has the potential to not only identify such interfaces, but assess and follow changes in tissue stiffness there that might occur following injury.

PurposeThe motion of the brain tissue within the skull is thought to be induced by cardiac pulsations and other hemodynamic processes and may influence CSF flow, but its precise drivers and downstream effects are unclear. Understanding these phenomena requires an accurate and precise method of tissue motion quantification that can be extended to investigate multiple potential drivers of tissue motion in vivo. MethodsHere, a version of the Displacement ENcoding with Stimulated Echoes (DENSE) pulse sequence was implemented to measure cardiac-locked velocity responses in the pons and midbrain in eight healthy volunteers. The method featured retrospective cardiac gating, a single mixing time, and measured multiple voxel sizes. ResultsA previously undescribed double-peak pattern of cardiac-locked longitudinally directed brainstem velocity was identified that appears to reflect both nonrigid and rigid motion components. This pattern was only visible when estimating the cardiac response using absolute time after systole instead of the percentage of the cardiac cycle. Measurements were performed in a custom-built slow-flow phantom, and repeat sessions were acquired in two volunteers to assess accuracy and precision. Despite potential increased influence of CSF motion with larger voxel sizes, no voxel-size-dependent bias was found in the velocity estimations. Lack of voxel size bias was attributed to the complexities of partial-volume effects between CSF and tissue in phase-valued data that were evaluated using numerical simulations. ConclusionIn sum, a method to measure brain tissue motion with high spatiotemporal precision is presented that can be extended to applications beyond measuring cardiac-locked motion.

PurposeTo quantify parasitic losses in ultra-high field (UHF) magnetic resonance imaging (MRI) receive coils and determine how they contribute to the mismatch between numerically predicted and experimentally realized signal-to-noise ratio (SNR), with the goal of guiding receive-array designs toward ultimate intrinsic SNR (uiSNR). MethodsSNR was measured across multiple field strengths (3T, 7T, 10.5T) using commercial and custom-built arrays. To quantify parasitic losses, unloaded-to-loaded quality factor ratio (QR) measurements were performed on representative loop resonators and practical RF coils. Measured losses were combined with single-loop electromagnetic simulations to separate conductor, radiation, and component losses. These bench-derived loss estimates were then incorporated into full-array electromagnetic simulations of a 128-channel receive array to evaluate their impact on predicted intrinsic SNR. ResultsMeasurements across field strengths supported the expected supralinear increase of SNR with B0. QR analysis showed that, at UHF, radiation loss must be excluded from unloaded-Q measurements to avoid overestimating electronic-noise penalties, and that multiple seemingly modest parasitic losses collectively impose substantial SNR degradation. In the 128-channel array, simulations including only conductor and radiation losses predicted 93% of central uiSNR, whereas inclusion of the full measured parasitic-loss budget reduced predicted performance to 78%, in close agreement with the experimentally measured 77%. ConclusionsThe gap between predicted and realized SNR performance of high-channel-count 10.5T loop arrays can be largely explained by parasitic losses that are not captured in conventional simulations. A bench-measurement-informed simulation framework enables more realistic prediction of coil performance and provides practical guidance for optimizing future UHF receive arrays.

PurposeTo enhance the SNR in MRI within a localized region of interest using a novel interventional wireless RF resonator probe combined with a dual-drive pTx system. MethodsA dual-drive body birdcage coil was operated in a linearly-polarized mode to decouple a passive RF resonator probe from the transmit field while maintaining the resonator in a receive-only coupled mode. The resonator was fabricated using standard microfabrication techniques and tuned to the Larmor frequency of a 3T MRI system. The 10-g specific absorption rate (SAR) distribution was simulated to identify potential hot spots around the resonator prior to heating experiments. To evaluate the interaction between the resonator probe and the linearly-polarized transmit field, SNR and flip-angle distributions were measured in a phantom. In vivo imaging studies were subsequently performed using the resonator probe in conjunction with the linearly-polarized dual-drive birdcage coil. ResultsTemperature measurements demonstrated a normalized temperature increase of less than 0.10{degrees}C, corresponding to a SAR value below 1.21 W/kg. Experimental flip-angle mapping confirmed effective magnetic decoupling of the resonator probe using linearly-polarized dual-drive transmission. An SNR enhancement factor of 1.6 was achieved within the region of interest in phantom experiments. In vivo imaging demonstrated a 2.0 {+/-} 0.2-fold SNR enhancement in the vicinity of the resonator probe. ConclusionA novel interventional approach for localized SNR enhancement in MRI was demonstrated using a wireless RF resonator probe and a dual-drive pTx system. The proposed technique enables local signal enhancement while minimizing transmit-field interactions, thereby facilitating safe interventional MRI and potentially improving image quality and diagnostic performance.

Proton magnetic resonance spectroscopic imaging (1H MRSI) enables quantitative mapping of brain metabolites, but its clinical use remains limited by long acquisition time. The goal of this work to improve the applicability of high-resolution 1H FID-MRSI at 7T by enhancing GRAPPA-based acceleration through deep learning-driven k-space reconstruction. In particular, compared with conventional GRAPPA, MultiNet PyGRAPPA enables substantially higher in-plane acceleration while suppressing residual lipid aliasing and preserving metabolite map fidelity in non-lipid-suppressed MRSI. Building on the MultiNet PyGRAPPA framework, we introduce a comprehensive comparison of advanced machine-learning models for predicting missing k-space points. Multiple architectures--including multilayer perceptrons, convolutional neural networks, and several U-Net variants--were trained within a variable-density k-space undersampling scheme to support acceleration factors of R = 4, 6, and 7. The proposed U-Net model extends the MultiNet concept by leveraging nonlinear hierarchical feature extraction, thereby improving reconstruction fidelity while maintaining robustness to noise.The methods were evaluated in vivo using retrospectively undersampled 7T 1H FID-MRSI datasets from healthy volunteers and patients. Quantitative analyses demonstrate that the U-Net outperforms the original MultiNet approach, offering improved SNR retention rate, reduced lipid RMSE, and higher structural similarity of major metabolites. Metabolite maps reconstructed with the U-Net showed reduced lipid artifacts and improved anatomical consistency. In conclusion, integrating deep convolutional networks into GRAPPA-based k-space prediction provides a more reliable and higher-fidelity reconstruction pipeline. When combined with variable-density undersampling, this approach enables faster acquisition of high-resolution 1H MRSI without compromising spectral quality or metabolite quantification.

Cerebral blood flow (CBF) is a quantitative metric for mapping perfusion. While the prototypical MRI approach arterial spin labelling (ASL) is well-validated in humans, the reproducibility of rodent ASL mapping remains poor, limiting translational impact. To address this gap, we used both newly acquired and analysis of previously published data to illustrate biological and physical sources of variation in CBF measured with ASL. Via a meta-analysis, we quantified the variation in CBF reported from the cortex of healthy rodents. A total of 23 mouse studies (343 data points) and 5 rat studies (41 data points) met the inclusion criteria. We demonstrate that reported CBF values exhibit a broad variability (50-400 ml/100g/min) driven primarily by experimental confounds rather than physiological differences. Our meta-analysis explores which factors cause variance in perfusion rates measured. Our experimental data highlight biological factors, particularly the choice of anaesthesia (e.g., isoflurane vs. medetomidine) and strain variations, that alter baseline CBF. Our work, reflecting both state-of-the-art and conventional practice in preclinical imaging, highlights the need to account for multiple sources of variability. Establishing community guidelines for rigorous ASL calibration and physiological monitoring will support improved study design and accelerate translational alignment between rodent and human perfusion measurements.

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

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