Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging
This addresses the problem of slow MRI scans for clinical and research applications by accelerating acquisition while maintaining accuracy, though it is incremental as it applies an existing deep learning method to a specific domain.
The paper tackled the trade-off between high resolution and signal-to-noise ratio in MRI by using super-resolution to enable rapid simultaneous high-resolution imaging and accurate quantification of T2 relaxation time biomarkers, achieving results comparable to a standard reference method.
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.