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2018 | OriginalPaper | Chapter

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

Authors : Akshay Chaudhari, Zhongnan Fang, Jin Hyung Lee, Garry Gold, Brian Hargreaves

Published in: Machine Learning for Medical Image Reconstruction

Publisher: Springer International Publishing

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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. However, 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 (SR) can be utilized to maintain adequate SNR for accurate quantification of the T\(_2\) 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 T\(_2\) 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.

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Metadata
Title
Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging
Authors
Akshay Chaudhari
Zhongnan Fang
Jin Hyung Lee
Garry Gold
Brian Hargreaves
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-030-00129-2_1

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