Rapid Reconstruction of Extremely Accelerated Liver 4D MRI via Chained Iterative Refinement
Journal:
arXiv
Published Date:
Dec 14, 2024
Abstract
Abstract Purpose: High-quality 4D MRI requires an impractically long scanning
time for dense k-space signal acquisition covering all respiratory phases.
Accelerated sparse sampling followed by reconstruction enhancement is desired
but often results in degraded image quality and long reconstruction time. We
hereby propose the chained iterative reconstruction network (CIRNet) for
efficient sparse-sampling reconstruction while maintaining clinically
deployable quality. Methods: CIRNet adopts the denoising diffusion
probabilistic framework to condition the image reconstruction through a
stochastic iterative denoising process. During training, a forward Markovian
diffusion process is designed to gradually add Gaussian noise to the densely
sampled ground truth (GT), while CIRNet is optimized to iteratively reverse the
Markovian process from the forward outputs. At the inference stage, CIRNet
performs the reverse process solely to recover signals from noise, conditioned
upon the undersampled input. CIRNet processed the 4D data (3D+t) as temporal
slices (2D+t). The proposed framework is evaluated on a data cohort consisting
of 48 patients (12332 temporal slices) who underwent free-breathing liver 4D
MRI. 3-, 6-, 10-, 20- and 30-times acceleration were examined with a
retrospective random undersampling scheme. Compressed sensing (CS)
reconstruction with a spatiotemporal constraint and a recently proposed deep
network, Re-Con-GAN, are selected as baselines. Results: CIRNet consistently
achieved superior performance compared to CS and Re-Con-GAN. The inference time
of CIRNet, CS, and Re-Con-GAN are 11s, 120s, and 0.15s. Conclusion: A novel
framework, CIRNet, is presented. CIRNet maintains useable image quality for
acceleration up to 30 times, significantly reducing the burden of 4DMRI.
