Title: Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging,
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.