AI-based reconstruction from low-resolution acquisitions for diffusion MRI: evaluation of FOD similarity
TL;DR
This study evaluated an AI-based reconstruction method, Precise IQ Engine (PIQE), and demonstrated that PIQE exhibited higher distributional similarity and directional agreement compared with ZIP+Advanced Intelligent Clear-IQ Engine (AiCE), with statistically significant similarity observed.
Abstract
MRI provides essential insights into tissue microstructure, and high angular resolution diffusion imaging (HARDI) enables detailed assessment of complex white matter architecture through fiber orientation distribution (FOD) analysis. However, HARDI requires high b-values and multiple diffusion directions, leading to reduced signal-to-noise ratio (SNR) and long scan times. Conventional zero-fill interpolation processing (ZIP) is widely used for super-resolution but is limited by edge blurring and artifacts. This study evaluated an AI-based reconstruction method, Precise IQ Engine (PIQE). Specifically, low-resolution diffusion data were reconstructed to standard resolution and compared with standard-resolution acquisitions. Ten healthy volunteers underwent HARDI at 3T, and FOD were estimated using constrained spherical deconvolution. Quantitative comparisons with reference data demonstrated that PIQE exhibited higher distributional similarity (lower Jensen–Shannon divergence) and directional agreement (higher angular correlation coefficient) compared with ZIP+Advanced Intelligent Clear-IQ Engine (AiCE), with statistically significant similarity observed. These findings indicate potential usefulness in advanced diffusion MRI applications.