Reference-Based MRI

TL;DR

This paper introduces FASTMER, a flexible MRI reconstruction framework that leverages reference images to accelerate scans or enhance image quality across various clinical scenarios, even with low similarity.

Contribution

The authors develop an iterative weighted reconstruction method that adapts to varying similarity levels between reference and target images, broadening MRI acceleration applications.

Findings

Outperforms existing methods in SNR improvement for high-res brain MRI

Effectively exploits inter-contrast similarity for faster FLAIR scans

Utilizes baseline-follow-up similarity for rapid follow-up imaging

Abstract

Purpose: In many clinical MRI scenarios, existing imaging information can be used to significantly shorten acquisition time or to improve Signal to Noise Ratio (SNR). In this paper the authors present a framework for fast MRI by exploiting a reference image (FASTMER). Methods: The proposed approach utilizes the possible similarity of the reference image that exists in many clinical MRI imaging scenarios. Such scenarios include similarity between adjacent slices in high resolution MRI, similarity between various contrasts in the same scan and similarity between different scans of the same patient. The authors take into account that the reference image may exhibit low similarity with the acquired image and develop an iterative weighted approach for reconstruction, which tunes the weights according to the degree of similarity. Results: Experimental results demonstrate the performance of the method in three different clinical MRI scenarios: SNR improvement in high resolution brain MRI, exploiting similarity between T2-weighted and fluid-attenuated inversion recovery (FLAIR) for fast FLAIR scanning and utilizing similarity between baseline and follow-up scans for fast follow-up. Results outperform reconstruction results of existing state-of-the-art methods. Conclusions: The authors present a method for fast MRI by exploiting a reference image. The method is based on an iterative reconstruction approach that supports cases in which similarity to the reference scan is not guaranteed, which enables the applicability of the method to a variety of MRI applications. Thanks to the existence of reference images in various clinical imaging scenarios, the proposed framework can play a major part in improving reconstruction in many MR applications.