Open Access

ARTICLE

Meta-Learning Multi-Scale Radiology Medical Image Super-Resolution

by Liwei Deng¹, Yuanzhi Zhang¹, Xin Yang^2,*, Sijuan Huang², Jing Wang^3,*

1 Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, 150080, Heilongjiang, China
2 Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, Guangdong, China
3 Faculty of Rehabilitation Medicine, Biofeedback Laboratory, Guangzhou Xinhua University, Guangzhou, 510520, Guangdong, China

* Corresponding Authors: Xin Yang. Email: ; Jing Wang. Email:

Computers, Materials & Continua 2023, 75(2), 2671-2684. https://doi.org/10.32604/cmc.2023.036642

Received 07 October 2022; Accepted 09 December 2022; Issue published 31 March 2023

Abstract

High-resolution medical images have important medical value, but are difficult to obtain directly. Limited by hardware equipment and patient’s physical condition, the resolution of directly acquired medical images is often not high. Therefore, many researchers have thought of using super-resolution algorithms for secondary processing to obtain high-resolution medical images. However, current super-resolution algorithms only work on a single scale, and multiple networks need to be trained when super-resolution images of different scales are needed. This definitely raises the cost of acquiring high-resolution medical images. Thus, we propose a multi-scale super-resolution algorithm using meta-learning. The algorithm combines a meta-learning approach with an enhanced depth of residual super-resolution network to design a meta-upscale module. The meta-upscale module utilizes the weight prediction property of meta-learning and is able to perform the super-resolution task of medical images at any scale. Meanwhile, we design a non-integer mapping relation for super-resolution, which allows the network to be trained under non-integer magnification requirements. Compared to the state-of-the-art single-image super-resolution algorithm on computed tomography images of the pelvic region. The meta-learning multiscale super-resolution algorithm obtained a surpassing of about 2% at a smaller model volume. Testing on different parts proves the high generalizability of our algorithm. Multi-scale super-resolution algorithms using meta-learning can compensate for hardware device defects and reduce secondary harm to patients while obtaining high-resolution medical images. It can be of great use in imaging related fields.

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Keywords

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