Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging

TL;DR

This paper introduces a deep learning super-resolution technique that enables rapid MRI scans to produce high-resolution images and accurate tissue biomarkers, improving clinical and research MRI efficiency.

Contribution

The study demonstrates how deep learning-based super-resolution can simultaneously enhance MRI resolution and preserve biomarker accuracy, addressing the trade-off between resolution and SNR.

Findings

Super-resolution maintains high SNR for accurate T2 biomarker quantification.

High-resolution images are generated with improved structural similarity.

Biomarker measurements closely match standard reference methods.

Abstract

Obtaining magnetic resonance images (MRI) with high resolution and generating quantitative image-based biomarkers for assessing tissue biochemistry is crucial in clinical and research applications. How- ever, acquiring quantitative biomarkers requires high signal-to-noise ratio (SNR), which is at odds with high-resolution in MRI, especially in a single rapid sequence. In this paper, we demonstrate how super-resolution can be utilized to maintain adequate SNR for accurate quantification of the T2 relaxation time biomarker, while simultaneously generating high- resolution images. We compare the efficacy of resolution enhancement using metrics such as peak SNR and structural similarity. We assess accuracy of cartilage T2 relaxation times by comparing against a standard reference method. Our evaluation suggests that SR can successfully maintain high-resolution and generate accurate biomarkers for accelerating MRI scans and enhancing the value of clinical and research MRI.