4D Real-Time GRASP MRI at Sub-Second Temporal Resolution

TL;DR

This paper introduces a novel 4D real-time MRI technique that achieves sub-second temporal resolution without motion gating, reducing intra-frame motion blurring in free-breathing abdominal imaging using a new basis estimation strategy.

Contribution

The work presents a new basis estimation method for subspace reconstruction that enables high temporal resolution MRI without additional sequence modifications or motion correction.

Findings

Achieved sub-second 3D volume imaging in MRI.

Reduced intra-frame respiratory motion blurring.

Compatible with existing MRI sequences.

Abstract

Intra-frame motion blurring, as a major challenge in free-breathing dynamic MRI, can be reduced if high temporal resolution can be achieved. To address this challenge, this work proposes a highly-accelerated 4D (3D+time) real-time MRI framework with sub-second temporal resolution combining standard stack-of-stars golden-angle radial sampling and tailored GRASP-Pro (Golden-angle RAdial Sparse Parallel) reconstruction. Specifically, 4D real-time MRI acquisition is performed continuously without motion gating or sorting. The k-space centers in stack-of-stars radial data are organized to guide estimation of a temporal basis, with which GRASP-Pro reconstruction is employed to enforce joint low-rank subspace and sparsity constraints. This new basis estimation strategy is the new feature proposed for subspace-based reconstruction in this work to achieve high temporal resolution (e.g., sub-second/3D volume). It does not require sequence modification to acquire additional navigation data, is compatible with commercially available stack-of-stars sequences, and does not need an intermediate reconstruction step. The proposed 4D real-time MRI approach was tested in abdominal motion phantom, free-breathing abdominal MRI, and dynamic contrast-enhanced MRI (DCE-MRI). With the ability to acquire each 3D image in less than one second, intra-frame respiratory blurring can be intrinsically reduced for body applications with our approach, which also eliminates the need for motion detection and motion compensation.