Submillimeter diffusion MRI using an in-plane segmented 3D multi-slab acquisition and denoiser-regularized reconstruction

Abstract

High-resolution diffusion MRI (dMRI) provides valuable insights into brain microstructure, particularly at submillimeter resolutions, where it enables more precise delineations of curved and crossing white matter pathways. However, achieving high-quality submillimeter dMRI in-vivo poses significant challenges due to the intrinsically low signal-to-noise ratio (SNR), along with the long echo spacing, readout time, and TE required for the large matrix size, leading to significant image distortion, T2* blurring, and T2 signal decay. In this study, we propose a novel acquisition and reconstruction framework to overcome these challenges. Based on numerical simulations, we introduce an in-plane segmented 3D multi-slab acquisition that leverages the optimal SNR efficiency of 3D multi-slab imaging while reducing echo spacing, readout times, and TE using in-plane segmentation. This approach minimizes distortion, improves image sharpness, and enhances SNR. Additionally, we develop a denoiser-regularized reconstruction to suppress noise while maintaining data fidelity, which reconstructs high-SNR images without introducing substantial blurring or bias. Comprehensive in-vivo experiments demonstrate that our method consistently produces high-quality dMRI data at 0.65 mm and 0.53 mm isotropic resolutions on a 3T scanner. The submillimeter dMRI datasets reveal richer microstructural details, reduce gyral bias, and improve U-fiber mapping compared to prospectively acquired 1.22 mm diffusion data. Our method demonstrates robustness at 7T and generates high-SNR 0.61 mm diffusion datasets, showing excellent agreement with previous post-mortem studies at the same scanner. Implemented using the open-source, scanner-agnostic framework Pulseq, our approach may facilitate broader adoption across different scanner platforms to benefit a wider range of applications. These results underscore the potential of our method to advance medical image analysis and neuroscientific research on human brain connectivity.