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.
Keywords
- Super-resolution
- Quantitative MRI
- T\(_2\) relaxation
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Chaudhari, A., Fang, Z., Hyung Lee, J., Gold, G., Hargreaves, B. (2018). Deep Learning Super-Resolution Enables Rapid Simultaneous Morphological and Quantitative Magnetic Resonance Imaging. In: Knoll, F., Maier, A., Rueckert, D. (eds) Machine Learning for Medical Image Reconstruction. MLMIR 2018. Lecture Notes in Computer Science(), vol 11074. Springer, Cham. https://doi.org/10.1007/978-3-030-00129-2_1
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DOI: https://doi.org/10.1007/978-3-030-00129-2_1
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