3D cine-magnetic resonance imaging using spatial and temporal implicit neural representation learning (STINR-MR)

TL;DR

This paper introduces STINR-MR, a neural network framework that reconstructs high-resolution 3D cine-MRI from highly undersampled data by learning spatial and temporal representations, improving image quality and motion accuracy.

Contribution

The paper presents a novel joint reconstruction and deformable registration framework using implicit neural representations for 3D cine-MRI, eliminating the need for external training data.

Findings

Achieves high temporal and spatial resolution in 3D cine-MRI reconstruction.

Outperforms traditional non-rigid motion estimation methods like MR-MOTUS.

Produces images with fewer artifacts and better tumor localization accuracy.

Abstract

The reconstruction of 3D cine-MRI is challenged by highly undersampled k-space data in each cine frame, due to the slow speed of MR signal acquisition. We proposed a machine learning-based framework, spatial and temporal implicit neural representation learning (STINR-MR), for accurate 3D cine-MRI reconstruction from highly undersampled data. STINR-MR used a joint reconstruction and deformable registration approach to address the ill-posed spatiotemporal reconstruction problem, by solving a reference-frame 3D MR image and a corresponding motion model which deforms the reference frame to each cine frame. The reference-frame image was reconstructed as a spatial implicit neural representation (INR) network, which learns the mapping from input 3D spatial coordinates to corresponding MR values. The dynamic motion model was constructed via a temporal INR, as well as basis deformation vector fields(DVFs) extracted from prior/onboard 4D-MRIs. The learned INR encodes input time points and outputs corresponding weighting factors to combine the basis DVFs into time-resolved motion fields. STINR-MR was evaluated using MR data simulated from the 4D extended cardiac-torso (XCAT) digital phantom and MR data acquired clinically from a healthy human subject. Its reconstruction accuracy was also compared with that of the model-based non-rigid motion estimation method (MR-MOTUS). STINR-MR can reconstruct 3D cine-MR images with high temporal (<100 ms) and spatial (3 mm) resolutions to accurately capture different irregular motion patterns. Compared with MR-MOTUS, STINR-MR consistently reconstructed images with better quality, fewer artifacts, and achieved superior tumor localization accuracy. STINR-MR provides a lightweight and efficient framework for accurate 3D cine-MRI reconstruction, and does not require external data for pre-training to avoid generalizability issues encountered in deep learning methods.