Rapid Whole Brain Motion-robust Mesoscale In-vivo MR Imaging using Multi-scale Implicit Neural Representation
Journal:
arXiv
Published Date:
Feb 12, 2025
Abstract
High-resolution whole-brain in vivo MR imaging at mesoscale resolutions
remains challenging due to long scan durations, motion artifacts, and limited
signal-to-noise ratio (SNR). This study proposes Rotating-view super-resolution
(ROVER)-MRI, an unsupervised framework based on multi-scale implicit neural
representations (INR), enabling efficient recovery of fine anatomical details
from multi-view thick-slice acquisitions. ROVER-MRI employs coordinate-based
neural networks to implicitly and continuously encode image structures at
multiple spatial scales, simultaneously modeling anatomical continuity and
correcting inter-view motion through an integrated registration mechanism.
Validation on ex-vivo monkey brain data and multiple in-vivo human datasets
demonstrates substantially improved reconstruction performance compared to
bicubic interpolation and state-of-the-art regularized least-squares
super-resolution reconstruction (LS-SRR) with 2-fold reduction in scan time.
Notably, ROVER-MRI achieves an unprecedented whole-brain in-vivo T2-weighted
imaging at 180 micron isotropic resolution in only 17 minutes of scan time on a
7T scanner with 22.4% lower relative error compared to LS-SRR. We also
demonstrate improved SNR using ROVER-MRI compared to a time-matched 3D GRE
acquisition. Quantitative results on several datasets demonstrate better
sharpness of the reconstructed images with ROVER-MRI for different
super-resolution factors (5 to 11). These findings highlight ROVER-MRI's
potential as a rapid, accurate, and motion-resilient mesoscale imaging
solution, promising substantial advantages for neuroimaging studies.
