Comparative Performance Analysis of an Open Source Motion-Compensated Cardiac Diffusion Sequence Across Different MRI Systems and Gradient Strengths-A Pilot Study.

Validation of Compressed Sensing Accelerated Magnetization Prepared Rapid Acquisition Gradient Echo for Fast and Reliable Brain Volume Measurement

CS-MPRAGE provides high-quality 3D images and reliable volume data with significantly reduced acquisition time and comparable image quality by comparing its scan time and image quality with standard MPRAGE.

A multi-b-value test-retest diffusion MRI brain dataset for model validation and reproducibility assessment

Transparent assessment of diffusion magnetic resonance imaging (dMRI) techniques with empirical verification of confounding factors requires adequately designed protocols and collected datasets. Publicly available diffusion-weighted MR datasets often provide limited sampling across b-values, making it difficult to study optimal acquisition protocols or the relationships between different processes occurring in brain tissue. In this work, we introduce a new densely sampled longitudinal test-retest diffusion-weighted MR dataset of the brain. Our dataset was collected from eleven healthy volunteers, each scanned four times: two sessions on consecutive days, which form the test data, followed by two additional sessions completed one week later (retest data). The data were acquired using twenty-two b-values ranging from 10 to 3000 s/mm2, along with structural T1-weighted scans. Potential applications of the dataset include, but are not limited to, assessing longitudinal reproducibility and reliability of quantitative metrics, evaluating robust and outlier-resistant estimation techniques, investigating experimental factors affecting estimation procedures, and verifying optimal acquisition protocols for different signal models. The dataset is publicly available in raw and fully preprocessed variants.

BART Online Open-Source Sequence Toolbox for Computational MRI

This work enables reproducibility of advanced computational MRI methods within a comprehensive end-to-end open-source framework and proves that quantitative MRI methods consisting of acquisition and reconstruction were successfully implemented in BART.

Repeatable tract-based diffusion metrics on a portable 64 mT MRI: a foundation for global population neuroscience

Diffusion MRI underpins much of modern population neuroscience, yet scanner access remains concentrated in high-income settings, excluding the genetic, developmental, and disease diversity needed for generalizable discovery. Portable low-field MRI systems (field strengths below 0.1 T) offer a cheaper, infrastructure-light alternative that could extend diffusion imaging to under-represented populations, but only if the measurements are sufficiently repeatable. Here we provide the first systematic test-retest evaluation of tract-based diffusion tensor MRI (DT-MRI) metrics at 64 mT. Ten healthy participants were scanned at two time-points (median interval 13 days) with an 18-direction (b = 900s/mm2) diffusion protocol and a T2-weighted structural sequence on a portable 64 mT scanner. After correction for distortions and gradient imperfections, diffusion tensors were estimated and constrained spherical deconvolution was performed to enable bundle-specific tractography. Tract averaged fractional anisotropy, mean diffusivity, and radial diffusivity were extracted from a selection of white-matter tracts spanning projection, association, and commissural fiber categories. Bland-Altman analysis and within-subject coefficients of variation indicated strong scan-rescan agreement, while intraclass correlation coefficients were variable across tracts and metrics. Compared with high-field data, coefficients of variation were 3–10 times larger and standard deviations in the means were approximately an order of magnitude higher. Despite this, statistical power calculations yielded feasible sample-size estimates for detecting group differences in a two-tailed t-test: for a 4% difference in means, approximately 25 participants per group were sufficient for the majority of tracts in MD and RD, and around 65 for FA. Although absolute DT-MRI metric values diverge from high-field references due to partial volume effects and noise-floor bias, continued advances in acquisition, reconstruction, and processing are expected to further narrow this gap. The results demonstrate that portable 64 mT MRI can deliver repeatable DT-MRI metrics, establishing a foundation for democratizing advanced neuroimaging and enabling population-scale studies in regions that have historically been excluded from brain research.

Patch-Based Diffusion Reconstruction for Accelerated Cardiac Cine

Purpose: To develop and evaluate a diffusion-based reconstruction framework for highly accelerated 2D real-time (RT) cine cardiovascular magnetic resonance imaging (CMR). Methods: We trained an unconditional patch-based diffusion model and incorporated it into a reconstruction framework, termed CineDiff, using diffusion posterior sampling for data consistency. CineDiff was evaluated in four settings: (i) 30 retrospectively undersampled breath-held cine at 1.5T and 3T from healthy participants across multiple acceleration rates, (ii) 15 prospectively undersampled free-breathing RT cine at 1.5T and 3T from patients indicated for clinical CMR, and (iii) 10 prospectively undersampled mid-field (0.55T) free-breathing scans, including five from healthy subjects and five from porcine models. For retrospective undersampling, reconstruction quality was assessed using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), learned perceptual image patch similarity (LPIPS), and deep image structure and texture similarity (DISTS). For prospective undersampling, image quality was evaluated by blinded expert scoring on a 5-point Likert scale. Results: In retrospectively undersampled breath-held cine data, CineDiff achieved higher PSNR and SSIM and lower LPIPS and DISTS than the comparison methods across all evaluated acceleration rates. In prospectively undersampled free-breathing RT cine data, CineDiff received higher expert image-quality scores. Qualitatively, CineDiff reduced block-like artifacts and preserved finer anatomical detail compared with traditional compressed sensing and a variational network method, termed CineVN. Conclusion: CineDiff enabled high-quality reconstruction of highly accelerated 2D RT cine CMR. The method also demonstrated robustness to out-of-distribution data, including mid-field and porcine acquisitions.

An FA-based hybrid DTI-mixture model for multi-fiber reconstruction in diffusion MRI.