# Rapid Reconstruction of Extremely Accelerated Liver 4D MRI via Chained Iterative Refinement

arXiv: https://arxiv.org/abs/2412.10629 · HTML: https://mlsanity.com/paper/2412.10629/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2024.

## Built on by (most cited first, 2 of 2)

- [Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations](https://mlsanity.com/paper/2503.05051/) (2025)
- [B-FIRE: Binning-Free Diffusion Implicit Neural Representation for Hyper-Accelerated Motion-Resolved MRI](https://mlsanity.com/paper/2601.06166/) (2026)

## Abstract (arXiv)

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.