Magnetic Resonance in Medicine

PurposeTo create a realistic in-silico brain phantom for positive and negative magnetic susceptibility that incorporates susceptibility anisotropy, enabling the evaluation of how susceptibility anisotropy influences susceptibility separation algorithm performance. MethodsWe expanded an existing QSM validation phantom by creating separate maps for positive and negative susceptibility, with the option of modeling susceptibility anisotropy. Multi-echo gradient echo data were simulated to evaluate four susceptibility separation techniques ({chi}-separation, DECOMPOSE-QSM, APART-QSM, and [Formula]). To assess the impact of noise, simulations were performed at different SNR levels (50, 100, 200, 300). ResultsOur findings showed that the error in negative susceptibility estimates increased by up to 53% when susceptibility anisotropy was present, compared to the case without susceptibility anisotropy, with {chi}-separation being the algorithm that was most sensitive to anisotropy. Robustness to noise varied across the assessed algorithms, with APART-QSM and {chi}-separation having the highest and lowest sensitivity to noise, respectively. ConclusionThe modified phantom is open-source and can serve as a numerical ground truth for evaluating susceptibility separation methods. Our findings emphasize the importance of incorporating susceptibility anisotropy into susceptibility separation models to improve their accuracy.

BackgroundCerebral vasculature is a key biomarker of brain health, and time-of-flight (TOF) magnetic resonance angiography (MRA) provides noninvasive assessment of vascular anatomy. However, conventional TOF-MRA requires long scan times, increasing patient burden and susceptibility to motion artifacts. Compressed sensing (CS) offers a feasible acceleration strategy. PurposeTo quantitatively evaluate CS acceleration in TOF-MRA at 3T and 7T using automated whole-FOV vascular segmentation and semi-automatic segmentation of representative vessels. Study typeProspective Population23 healthy human participants (3T) and 8 healthy human participants (7T). Field Strength/SequenceCS TOF-MRA (CS factors 4 and 8 at 3T; 8 at 7T) was compared against non-accelerated (CS0) TOF-MRA. AssessmentVisual comparison and vascular segmentation were performed using automated whole-FOV methods and semi-automatic segmentation of the posterior cerebral artery and anterior choroidal artery. Statistical TestsContrast-to-noise ratio (CNR), voxel count, and vessel diameter were assessed using two-tailed paired t-tests. ResultsWhole-FOV CNR differed significantly across CS factors at 3T (CS0 > CS4: p < 0.001, d = 0.77; CS0 < CS8: p = 0.008, d = 0.36; CS4 < CS8: p < 0.001, d = 1.11) and 7T (CS0 < CS8: p = 0.002, d = 0.54), with semi-automatic segmentation yielding consistent findings (p < 0.01 for all comparisons). The diameter measurements for segmented vessels are also higher with high CS-factors (PCA 7T: left: p = 0.006, d = 0.93, right: p = 0.045, d = 0.43; AChA 7T: left: p < 0.001, d = 0.66, right: p = 0.009, d = 1.06; PCA 3T: p < 0.001 for all comparison dLeft = 0.52 (CS0 vs. CS4), 0.56 (CS4 vs. CS8), 1.11 (CS0 vs. CS8) and dRight = 0.78 (CS0 vs. CS4), 0.57 (CS4 vs. CS8), 1.17 (CS0 vs. CS8)). Data ConclusionCS shows promise for enhancing clinical applicability of TOF-MRA, with advantages most pronounced at 7T.

PurposeTo present a first-of-its-kind artificial intelligence (AI-)integrated MR pulse sequence that detects out-of-voxel (OOV) artifacts in real-time (within-TR) and responds prospectively by updating the crusher gradient scheme. MethodsPer Excitation Real-time Execution & Guided Responses with Integrated Neural-network Evaluation (PEREGRINE), developed for deployment of deep learning models and sequence updates, operated time-domain (TD) and frequency-domain (FD) convolutional autoencoders that detect OOV artifacts. Scans without (AI-off) and with (AI-on) updates were collected from the prefrontal cortex of healthy volunteers using edited MRS. The degree of OOV contamination (OOV score) was quantified per transient based upon the prevalence of OOV signals in the TD and FD data. OOV scores above a user-defined threshold triggered an update of the gradient scheme, iterating through 48 permutations (6 axis transpositions x 8 polarity flips). ResultsWithin each 2-second TR, PEREGRINE successfully provided single-transient OOV scores and updated gradients accordingly. No difference was observed between the OOV scores from the full ("Full" condition) AI-on and AI-off sessions due to the AI-on scan cycling over better and worse gradient permutations relative to the AI-off scan. However, the AI-on scan had significantly lower OOV scores than the AI-off scan when selecting the transients where PEREGRINE persisted ("Dwell" condition) on a given gradient permutation. Ultimately, Fit Quality Number (FQN), from linear combination modeling, improved significantly for the AI-on compared to the AI-off scan. ConclusionPEREGRINE enabled an AI-integrated sequence allowing for real-time evaluation and reduction of OOV artifacts, identifying gradient modifications that produced less OOV contamination.

Low-cost portable MRI has the potential to improve the accessibility of MRI, but new acquisition methods and protocols must be developed and evaluated to accommodate the reduction in SNR and greater impact of system imperfections. Diffusion tensor imaging (DTI) is a candidate tool for monitoring population health, but the bias and variance of quantitative diffusion tensor-derived metrics must be evaluated prior to designing such studies. DTI of the corpus callosum was performed on an in-house, portable 100 mT MRI system using a slab diffusion weighted Fast Spin Echo with radiofrequency (RF) encoding. Slice coverage was restricted to the corpus callosum to shorten scan time and reduce sensitivity to large rigid motion. In vivo DTI images were obtained in two healthy volunteers with nominal voxel size 50 mm3, scan time 25 minutes, and two different volunteers using nominal voxel size 25 mm3, scan time 35 minutes. Mean diffusivity (MD) and fractional anisotropy (FA) coefficients of variation were estimated in the 50 mm3 acquisition using a bootstrap approach and compared to resolution-matched data obtained on a conventional 1.5T system. MD / FA maps were compared quantitatively and qualitatively. Mean MD values in the corpus callosum obtained on the 100 mT system were within 10% of the reference 1.5T acquisition, but FAs were underestimated by 20-30%. The corpus callosum median MD coefficient of variation was 3.7%, and the median FA coefficient of variation was 7.5%. FA maps obtained at 100 mT had an elevated FA noise floor and color FA maps had lower apparent resolution but some white matter tracts were still distinguishable. HighlightsO_LIDiffusion Tensor Imaging (DTI) of the corpus callosum was performed on a portable 100 mT MRI scanner with 50 mm3 voxels in 25 minutes scan time. C_LIO_LIMean Diffusivity estimates in the corpus callosum obtained at 100 mT and 1.5T differed by less than 0.1 x 10-3 mm2/s. C_LIO_LISome white matter tracts were visible in color Fractional Anisotropy maps obtained at 100 mT but FA maps were underestimated by 20- 30% when compared to a resolution-matched 1.5T acquisition, and had lower apparent resolution. C_LI

PurposeIn principle, combined T2-Diffusion (D) MRI has the microstructural and chemical sensitivity to detect axonal and myelin water changes in Alzheimers disease and related dementias (ADRD), but its practical implementation may be hindered by demanding hardware requirements. This work assesses the feasibility and accuracy of T2-D for ex vivo analysis of WM lesions in ADRD tissue. MethodsA thawed ex vivo brain sample from the Michigan Brain Bank and a T2-D phantom were scanned at 7T using a combined diffusion relaxometry (CDR) sequence. A non-negative least squares (NNLS) conventional data processing pipeline was used to disentangle water pools with unique T2-D signatures. Simulations examined the effects of minimum TE and SNR on recovery of myelin water (short T2, slow diffusion). ResultsAcross tissue types, T2-D data consistently resolved three spectral components. Phantom experiments showed detection of short T2 and slow diffusion features similar to those observed in ADRD ex vivo tissue, and confirmed CDRs ability to accurately resolve multiple components. Simulations indicated reliable T2-D recovery for myelin with SNR > 30 dB and minimum TE < 25 ms. ConclusionStrong T2 and D weighting could be combined to capture the expected axonal, myelin, and extracellular (EC) regions in T2-D space. The observed short-T2, restricted-D components are therefore unlikely to be artifacts and instead support interpretations as physically meaningful myelin and axonal water signatures.

Purpose: To quantify the contributions of signal-to-noise ratio (SNR) and velocity-to-encoding ratio (v/VENC) to velocity uncertainty in phase-contrast (PC) MRI and to develop a framework for in vivo voxel-wise uncertainty estimation. Methods: Through-plane 2D PC-MRI of the ascending aorta was acquired using multiple velocity encodings (150, 200, 300 cm/s) and flip angles (0, 5, 15, 20 degrees) to vary v/VENC and SNR. Voxel-wise SNR and velocity uncertainty maps were generated using empirically calibrated phase-noise modeling. Phase-resolved subject-level analyses were performed to quantify the relative contributions of SNR and |v|/VENC to percent velocity uncertainty (%unc). Uncertainty was propagated to flow, stroke volume (SV), and cardiac output (CO). Results: Velocity uncertainty varied substantially across the cardiac cycle and depended on both SNR and |v|/VENC. Across cardiac phases, |v|/VENC accounted for most explained variance in %unc (partial R2=0.666), while SNR provided a smaller but meaningful contribution (partial R2=0.287; full R2=0.909). Near peak systole, SNR contributed more strongly while overall uncertainty remained low. In contrast, diastolic %unc became unstable as velocity approached zero. These effects were most pronounced at low |v|/VENC, where higher VENC settings increased uncertainty despite similar SNR. SV uncertainty ranged from 0.27% to 1.07% across VENCxFA protocols. Conclusion: Velocity uncertainty in PC-MRI depends on both SNR and VENC adequacy in a physiologically phase-dependent manner. Relative uncertainty may become inadequate for precise quantification in low-flow applications, such as diastolic regurgitant jets, despite adequate SNR. Spatiotemporal uncertainty mapping provides a framework for uncertainty-aware PC-MRI acquisition and interpretation.

Purpose: To develop and evaluate a flexible, software-defined radar platform for contactless, vendor-independent motion detection and correction in MRI. Methods: A continuous-wave (CW) Doppler radar was implemented using a software-defined radio and the open-source GNU Radio framework. The system was deployed inside a 1.5T MRI scanner and synchronized with MRI acquisitions. We evaluated the performance in a custom-developed internal motion phantom and in healthy volunteers to track respiration and bulk motion. The radar-derived signal was validated against cine MRI and used to demonstrate both retrospective and prospective motion management techniques in phantom and in healthy volunteers. Results: The radar provided robust motion signals that correlated strongly with image-based ground truth signals in both phantom and volunteer experiments. Signal characteristics were found to be frequency-dependent, enabling optimization for different motion regimes. Retrospective correction of free-breathing abdominal data using the radar signal effectively suppressed respiratory artifacts, achieving image quality comparable to a self-gating approach. Prospective triggering successfully reduced motion artifacts in the phantom study. The system also reliably detected sporadic events such as swallowing during neck imaging. Conclusion: Software-defined radar was demonstrated to be an effective platform for both prospective and retrospective motion correction. Its independence from the MRI system, ultra-wide band capabilities, and body-region versatility enable the adaptation of the technique for a wide range of imaging applications and protocols.

Diffusion MRI simulations based on realistic tissue microstructure provide a means to validate biophysical models and optimize acquisition protocols, but their computational cost restricts most studies to domains far smaller than a clinical voxel. The objective of this study was to develop an automated and scalable framework that converts whole-slide histology into diffusion MRI simulations at clinically relevant spatial scales while remaining feasible on standard workstation hardware. We present an end-to-end pipeline integrating two-dimensional whole-slide cell segmentation, mesh generation, and finite element Bloch-Torrey simulation. To enable simulations at large spatial scales without prohibitive memory growth, we introduce a subdomain tiling strategy in which the tissue domain is partitioned into extended subdomains simulated independently under no-flux boundary conditions. Signals are aggregated only from the central regions of each subdomain to minimize boundary artifacts. For an 800 {micro}m x 800 {micro}m histology-based domain, the aggregated signal differed by 0.07% from the corresponding full-domain finite element simulation while reducing wall-clock time from several days to hours and maintaining bounded memory usage independent of global domain size. When applied to a 2016 {micro}m x 2016 {micro}m heterogeneous region approximating the in-plane dimensions of a clinical voxel, the apparent diffusion coefficient obtained from the full domain differed from values computed in smaller dense and sparse subregions, demonstrating the influence of structural heterogeneity at clinically relevant scales on derived diffusion metrics. The proposed framework establishes an automated and memory-stable approach for generating diffusion MRI simulations directly from routine histology.

Objective. Quantitative T2 mapping plays a critical role in brain imaging for assessing a range of neurological conditions, including neurodegenerative diseases, demyelinating disorders, and cerebrovascular pathologies. Despite its diagnostic potential, implementing quantitative T2 mapping at ultra-high magnetic field strengths ([&ge;]7T) poses significant challenges. These include elevated specific absorption rate (SAR) and radiofrequency (RF) field inhomogeneities, which can lead to prolonged scan durations and inaccuracies in quantification. Materials and Methods. Phase-based gradient-recalled echo (GRE) techniques have recently emerged as promising rapid acquisition with enhanced sensitivity to T2-related contrast. In this study, we introduce TWISTARE (TWo Interleaved Steady-states for T2 and RF Estimation), a novel dual steady-state 3D-GRE approach that employs interleaved flip angles and small RF phase increments to jointly estimate T2 and B1 maps. By combining two dual-steady-state scans, TWISTARE enables fast, whole-brain quantitative T2 mapping while reducing scan time and mitigating B1-related bias at ultra-high field. Results. Validation experiments included Bloch simulations, phantom studies and in-vivo imaging. The results demonstrated high precision in phantom experiments, achieving up to a two-fold reduction in acquisition time and achieved precision comparable to the gold-standard method in vivo within a similar scan duration. Discussion. TWISTARE establishes a fast steady-state framework for quantitative neuroimaging at ultrahigh field, offering potential benefits for both clinical and research applications, especially in longitudinal and dynamic studies of brain tissue.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSBrain magnetic resonance elastography (MRE) is an emerging quantitative neuroimaging technique that provides noninvasive maps of brain tissue viscoelasticity. For multi-center applications, robust cross-site reproducibility across scanner platforms is essential but remains insufficiently characterized. PurposeTo evaluate cross-site reproducibility of brain multifrequency MRE measurements between two MRI scanner platforms using harmonized protocols. Study TypeProspective cross-site test-retest reproducibility study. Study PopulationSixteen healthy adult volunteers (7 men, 9 women; mean age 32.2 {+/-} 8.0 years). Field Strength/Sequence3 T systems (Siemens MAGNETOM Cima.X and MAGNETOM Vida at two sites) with identical brain multifrequency MRE sequences, echo-planar imaging (EPI) readout, and standardized driver configuration. AssessmentEach participant underwent one MRE acquisition at each site. Shear wave speed (SWS) and penetration rate (PR) were quantified in whole brain, white matter, subcortical gray matter, and cortical gray matter regions using atlas-based region-of-interest (ROI) analysis in MNI152 space. Statistical TestsAbsolute relative difference (ARD), reproducibility coefficient (RDC), coefficient of variation (CV), intraclass correlation coefficient (ICC), and Bland-Altman plots were calculated to determine cross-site reproducibility. ResultsCross-site reproducibility was robust for major brain regions, with region-averaged ARD values for SWS ranging from 1.38 % to 3.43 % and for PR from 3.20 % to 7.25 % across tissues. RDCs for SWS ranged from 0.02 m.s-1 to 0.07 m.s-1, and for PR from 0.03 m.s-1 to 0.08 m.s-1. Coefficients of variation for SWS ranged from 0.82 % to 1.93 %, and for PR from 2.21 % to 4.09 %. ICC values for SWS ranged from 0.66 to 0.84 and for PR from 0.67 to 0.88. Bland-Altman analysis showed minimal systematic bias and tight limits of agreement. ConclusionBrain multifrequency MRE demonstrates robust reproducibility across distinct 3 T platforms when using harmonized acquisition and reconstruction. These results support the use of brain MRE as a quantitative biomarker and provide benchmark reproducibility metrics for future research.

Multi-site MRI studies in preclinical neuroimaging are emerging, but unlike in human studies, characterization of inter-scanner variability remains limited. In this study, we assessed intra- and inter-scanner variability between two similarly equipped 7 Tesla MRI scanners using a phantom and ex vivo mouse brain specimens. Diffusion-weighted imaging revealed slight differences in gradient amplitudes between the scanners, while estimated apparent diffusion coefficient (ADC) values showed a coefficient of variation below 1.5% and inter-scanner differences below 2% near the magnet center. Volumetric analysis based on proton density-weighted images showed negligible intra-scanner differences across sessions, while inter-scanner volumetric differences were mostly less than 2% and spatially non-uniform across the brain. Quantitative maps of R1, R2*, and MTsat showed inter-scanner relative differences of less than 5%, 10%, and 20%, respectively, with white matter exhibiting greater variability than gray matter. These findings provide a foundation for future large-scale, multi-scanner preclinical neuroimaging studies.

Most high-field MRI scanners conduct imaging using phased-array coils, in which the signals received by an array of coil elements are combined for downstream processing. Optimally combining these signals requires knowledge of each coil's spatial sensitivity profile, which can be acquired from a volume coil with homogeneous sensitivity across the field-of-view. However, this approach is not often used on high-field MRI scanners, especially on non-clinical systems; therefore, this work uses an algorithm based on the singular-value decomposition (SVD), called SVD-B1, to estimate coil sensitivities directly from the array data itself. Images produced by SVD-B1 are devoid of wormhole artifacts and open-ended fringe lines commonly seen in more conventional reconstructions. Quantitative Susceptibility Maps (QSMs) produced using the algorithm were compared to those produced using other combination algorithms across clinically relevant regions of in-vivo and postmortem human brains. As progressive levels of simulated noise were added to the data, SVD-B1's QSMs were up to 3% (in-vivo) and 13% (postmortem) more consistent (as measured by their Intraclass Correlation Coefficient) than those from other algorithms. Additionally, these QSMs were up to 8.5% (in-vivo) and 36% (postmortem) more accurate than other QSMs with respect to a "single-coil" reference. A parallel imaging extension of SVD-B1, called SVD-B1 GRAPPA, achieved similar results for QSMs generated from progressively more accelerated acquisition data. These results show that SVD-B1 can improve the sensitivity of high-resolution QSM to subtle changes in fine-grained tissue structures (e.g., in neurodegenerative disease) and help reduce scan times in clinical settings where shorter scans are imperative.

The fibrous microstructure of tendons and ligaments is an important determinant of their mechanical behavior and integrity. Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that enables the inference of microstructural features within fibrous tissues and has recently been used to characterize the microstructure of dense connective tissues such as tendon and ligament. However, the effect of microstructural variations in tendon and ligament on DTI metrics remains unclear. To address this gap, we simulated diffusion MRI of second harmonic generation (SHG) image-informed square lattice fiber networks to determine which microstructural features have the strongest influence on DTI metrics. Then, we performed a second set of diffusion MRI simulations for randomly dispersed fibers within synthetic tendon volumes to relate DTI metrics to the influential microstructural features, including fiber dispersion. All DTI metrics were insensitive to collagen fiber crimp. Fiber dispersion did not affect mean diffusivity, decreased axial diffusivity, increased radial diffusivity, and decreased fractional anisotropy. These results provide valuable insight into the relationships between DTI metrics and microstructural properties of tendon and ligament, which is particularly relevant for inferring microstructural changes in impaired tissue using DTI. Furthermore, our findings are an important step in the translation of DTI for clinical and computational studies of dense connective tissues such as tendon and ligament.

Aceruloplasminemia (ACP) is a rare neurodegenerative disorder characterized by extreme cerebral iron overload and a shift towards larger iron aggregates, providing a unique possibility to study how iron aggregation shapes MRI contrast in vivo. We introduce a clinically feasible, multi-parametric quantitative MRI (qMRI) framework that combines quantitative susceptibility mapping (QSM), [Formula], and R2 to disentangle changes in total iron concentration from alterations in iron aggregation and its spatial organization at the cellular scale. Our biophysical model links the microstructure sensitive [Formula] ratio and the slope of the susceptibility-relaxation relationship (iron) to iron aggregation size and distribution. In a 3T qMRI study of three patients with ACP and three matched controls, we observe a marked increase in [Formula] and a pronounced increase of the [Formula]-QSM slope (iron: controls 154.09 {+/-} 52.89 s-1ppm-1; patients 296.68 {+/-} 57.18 s-1ppm-1; p = 0.016), consistent with enhanced iron aggregation and altered spatial organization. Model-based decomposition of transverse relaxation indicates that up to approximately 40% of the observed R2* elevation in ACP is attributable to changes in iron distribution beyond increased iron concentration alone. These findings establish a robust, translational qMRI approach for quantitative in vivo assessment of iron aggregation, revealing microstructural drivers of iron-related neurodegeneration that extend beyond bulk iron load.

PurposeTo develop a data-efficient deep learning framework for rapid reconstruction of highly accelerated 4D arterial spin labeling (ASL) magnetic resonance angiography (MRA) with robust generalization using extremely limited acquired data, addressing the challenges of prolonged acquisition and reconstruction time. MethodsA simulation-driven, few-shot transfer learning approach was adopted by leveraging publicly available 3D time-of-flight (TOF)-MRA data to generate realistic multi-coil complex-valued pseudo-ASL k-space datasets for large-scale pre-training. A 3D unrolled reconstruction network was trained on this simulated data using a histogram-weighted loss and subsequently extended to 4D using lightweight temporal fusion modules. Fine-tuning was performed using only two experimentally acquired 4D ASL-MRA datasets. The method was evaluated on retrospectively and prospectively undersampled Cartesian 4D ASL-MRA data acquired at 3T and compared with compressed sensing (CS) and locally low-rank (LLR) reconstructions. ResultsThe proposed method achieved superior reconstruction quality compared with CS and LLR, with improved vessel depiction, particularly in distal branches, and enhanced temporal fidelity. Quantitative evaluation demonstrated higher vessel-masked peak signal-to-noise ratio and structural similarity index measure, along with increased error entropy, indicating reduced noise and structured artifacts. The initial pre-trained model already outperformed conventional methods, while additional 4D fine-tuning further improved performance. Robust reconstruction was demonstrated in prospectively undersampled data and multi-slab acquisitions, enabling large-coverage, time-resolved angiography within clinically feasible scan times (4-6 min). ConclusionsSimulation-driven pre-training combined with few-shot fine-tuning enables accurate and rapid reconstruction of highly accelerated 4D ASL-MRA in data-limited settings. The proposed framework provides a practical pathway toward clinically feasible, non-contrast dynamic cerebrovascular imaging.

IntroductionPostmortem human brain magnetic resonance imaging (MRI) offers a unique opportunity to study finer neuroanatomical details and enables direct correlations with gold standard histological and immunohistochemical assessments. However, to prevent tissue decay, postmortem brains are preserved in fixative solutions which can alter tissue properties and exert substantial impacts on the MRI signals. The present study investigates the impact of formalin fixation, the most commonly used solution for postmortem human brain preservation, on different quantitative MRI contrasts. Methods142 intact human brain hemispheres immersed in 10% formalin for a range of fixation durations (between 0 days and 20 years) were imaged in a 3T MRI scanner. A subset of 10 brains were further scanned repeatedly at days 0, 3, 10, 20, 30, 60, 90, and 120 to allow for better characterization of the initial transient effects of fixation. Voxel-wise T1 and T2* relaxation, T1/T2 ratio, and myelin water fraction (MWF) maps were generated for each specimen and timepoint, and linear and nonlinear models were used to examine the spatiotemporal changes associated with progressive fixation. ResultsAll investigated metrics were significantly impacted by formalin fixation, albeit at different rates and with differing regional patterns. T1 and T2* relaxation time decreased as a result of progressive fixation, whereas T1/T2 ratio and MWF measures increased. T1 relaxation and T1/T2 ratio showed nonlinear patterns with initially accelerated changes that decelerate in the first few months, whereas T2* relaxation and MWF changes followed a more linear trend. ConclusionFormaldehyde fixation exerts systematic changes on quantitative MRI signals that can be modeled and adjusted for to allow for harmonized comparisons of MRI metrics across brains fixed for differing durations. The distinct temporal trajectories observed across metrics highlight the need to account for fixation duration in study design and downstream analyses, particularly when integrating datasets acquired under heterogeneous conditions. Our findings provide a quantitative framework for correcting fixation-induced biases, thereby improving the interpretability and reproducibility of postmortem MRI studies.

PurposeTo address the limitations of single-distance, 1D performance metrics in RF coil design. This work introduces a multi-objective, volume-of-interest (VOI) based analysis to systematically characterize the trade-offs between power efficiency, pSAR efficiency, and homogeneity as a function of dipole length (l) and distance-to-load (d) for multiple dipole geometries and target anatomies. MethodsElectromagnetic simulations of straight and end-meandered dipole antennas were performed with varying lengths (100-500 mm) and distance-to-load (1-81 mm) over three anatomical targets (prostate, kidney, heart). Homogeneity, power efficiency, pSAR efficiency, and load sensitivity performance metrics were calculated within each anatomical VOI. Inter-element coupling at variable d was assessed in a 3-element array, and a subset of single-element simulations was experimentally validated using B1+ mapping. ResultsA fundamental trade-off was found between power efficiency and pSAR efficiency. Optimal power efficiency was achieved with shorter dipoles (150 mm < l < 300 mm) closer to the sample (d < 30 mm), while optimal pSAR efficiency and homogeneity were achieved with longer dipoles at further from the sample (d > 60 mm). Inter-element coupling increased with distance-to-load but could be managed by increasing element spacing. Experimental measurements were in good agreement with simulation trends. ConclusionIncreasing distance-to-load to 40-60 mm, compared with commonly used distances of 20-30 mm, offers a practical strategy for improving pSAR efficiency and homogeneity with a minimal decrease in power efficiency. This work provides a quantitative analysis that enables RF coil designers to make informed, data-driven decisions when developing next-generation body arrays and suggests that unshielded end-meandered dipoles could be an optimal transmit element geometry.

Quantitative microstructural magnetic resonance imaging (MRI) can noninvasively characterize tissue configuration at micrometer scales, but clinical uptake is limited by validation and optimization in human-relevant scenarios. Organoids are powerful human-relevant tissue models, yet translation is hampered by lack of non-destructive, longitudinal microstructural assessment. Bridging these gaps, microstructural MRI of living organoids can accelerate MRI biomarker and organoid development and validation. Here, we address key obstacles to enable organoid microstructural MRI. First, we use a unique 28.2 T MRI system to achieve spatial resolution with adequate signal-to-noise ratio and feasible scan times. Second, we implement flexible acquisitions with fast readouts to expand multivariate experimental capacity. Third, we develop a workflow combining 3D MRI and 3D lightsheet microscopy for cross-modality anatomical comparison beyond 2D. Using this platform, we demonstrate microstructural MRI of cortical organoids with resolutions down to (20 {micro}m)3, revealing anisotropy, heterogeneity, maturation-dependent differences, and temporal changes in cortical organoids. Correlative lightsheet microscopy confirms correspondence to axonal and nuclear architecture. This platform enables live-organoid MRI as a complementary tool to human- and animal imaging for robust microstructural assessment.

PurposeHigh-density RF receive arrays are required to realize the inherently available SNR and parallel imaging advantages at ultrahigh field strengths, which are essential for high-resolution functional and anatomical brain MRI. This study aims to systematically assess the impacts of often-overlooked parasitic losses associated with various RF coil components, as these losses can degrade the realized SNR and cause significant deviation from the ultimate intrinsic SNR (uiSNR; the theoretical upper bound of available SNR). In addition, we seek to detail engineering solutions to each of these loss mechanisms in pursuit of achieving a higher fraction of the uiSNR limit. MethodsA 16-channel loop-folded dipole transceiver array was developed for 10.5T human head applications and paired with a fully-updated 64-channel receive-only loop array. The optimization of the receive array considered several factors, including (but not limited to) coil dimensions to accommodate a larger population, the size and number of loops to enhance SNR and parallel imaging performance, and circuit design strategies to minimize parasitic losses. The SNR and parallel imaging performance of the receive array were quantitatively assessed by comparison with the uiSNR, as well as existing high-channel-count receive arrays at 7T and 10.5T. Finally, the complete 16-channel transmit, 80-channel receive coil array was safety validated for human use and employed for high-resolution functional and anatomical MRI at 10.5T. ResultsInitial results show that the 80-channel array, featuring larger loops in an overlapped layout with optimized circuitry, significantly improves the SNR and approaches the uiSNR limit in a large fraction of the head, while maintaining or enhancing the parallel imaging performance compared to previously used non-overlap layout. ConclusionThis study suggests that, although the traditionally used high-channel-count loop receive array technology can approach the uiSNR limit in the >10T regime, meticulous design optimization--including systematic assessment and minimization of parasitic losses--has become increasingly critical for achieving this goal in this new field-strength territory.

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.