Super Resolution for Magnetic Resonance Images Using Self-Super Resolution Technique

Vasanth Raj, P T and Vijayaraj, A and Dhanagopal, R and Suresh Kumar, R and Ayyadurai, A (2022) Super Resolution for Magnetic Resonance Images Using Self-Super Resolution Technique. In: 2022 6th International Conference on Devices, Circuits and Systems (ICDCS), Coimbatore, India.

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Abstract

High resolution magnetic resonance~(MR) imaging~(MRI) is desirable in many clinical applications, however, there is a trade-off between resolution, speed of acquisition, and noise. It is common for MR images to have worse through-plane resolution~(slice thickness) than in-plane resolution. In these MRI images, high frequency information in the through-plane direction is not acquired, and cannot be resolved through interpolation. To address this issue, super-resolution methods have been developed to enhance spatial resolution. As an ill-posed problem, state-of-the-art super-resolution methods rely on the presence of external/training atlases to learn the transform from low resolution~(LR) images to high resolution~(HR) images. For several reasons, such HR atlas images are often not available for MRI sequences. This paper presents a self super-resolution~(SSR) algorithm, which does not use any external atlas images, yet can still resolve HR images only reliant on the acquired LR image. We use a blurred version of the input image to create training data for a state-of-the-art super-resolution deep network. The trained network is applied to the original input image to estimate the HR image. Our SSR result shows a significant improvement on through-plane resolution compared to competing SSR methods.

Item Type: Conference or Workshop Item (Paper)
Subjects: AC Rearch Cluster
Depositing User: Unnamed user with email techsupport@mosys.org
Date Deposited: 27 Dec 2023 10:03
Last Modified: 27 Dec 2023 10:03
URI: https://ir.vignan.ac.in/id/eprint/649

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