Multisession Longitudinal Dynamic MRI Incorporating Patient-Specific Prior Image Information Across Time

TL;DR

This paper introduces a novel longitudinal dynamic MRI framework that leverages patient-specific prior images across multiple sessions to improve image quality and reduce scan time, demonstrating robustness and efficiency in clinical imaging.

Contribution

It proposes a new method to incorporate multi-session prior images into dynamic MRI reconstruction, enabling progressive acceleration and improved image consistency over time.

Findings

Longitudinal 4D GRASP outperforms single-session reconstruction in image quality.

The method is robust to anatomical changes, imaging intervals, and body contour variations.

It enables faster imaging as more sessions are acquired.

Abstract

Serial Magnetic Resonance Imaging (MRI) exams are often performed in clinical practice, offering shared anatomical and motion information across imaging sessions. However, existing reconstruction methods process each session independently without leveraging this valuable longitudinal information. In this work, we propose a novel concept of longitudinal dynamic MRI, which incorporates patient-specific prior images to exploit temporal correlations across sessions. This framework enables progressive acceleration of data acquisition and reduction of scan time as more imaging sessions become available. The concept is demonstrated using the 4D Golden-angle RAdial Sparse Parallel (GRASP) MRI, a state-of-the-art dynamic imaging technique. Longitudinal reconstruction is performed by concatenating multi-session time-resolved 4D GRASP datasets into an extended dynamic series, followed by a low-rank subspace-based reconstruction algorithm. A series of experiments were conducted to evaluate the feasibility and performance of the proposed method. Results show that longitudinal 4D GRASP reconstruction consistently outperforms standard single-session reconstruction in image quality, while preserving inter-session variations. The approach demonstrated robustness to changes in anatomy, imaging intervals, and body contour, highlighting its potential for improving imaging efficiency and consistency in longitudinal MRI applications. More generally, this work suggests a new context-aware imaging paradigm in which the more we see a patient, the faster we can image.