SDGNet: conversion from CT images to MR images based on dual branch generative adversarial networks

Chaiqi Guo; Zhibo Guo; Yu Chen; Meizhen Xia

doi:10.1117/12.3031031

19 July 2024 SDGNet: conversion from CT images to MR images based on dual branch generative adversarial networks

Chaiqi Guo, Zhibo Guo, Yu Chen, Meizhen Xia

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131812D (2024) https://doi.org/10.1117/12.3031031
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Magnetic resonance imaging (MRI) has high resolution for soft tissues and significant advantages in the examination of the bone and joint systems and central nervous system. However, due to cost and usage limitations, MRI cannot be applied in various scenarios. Computer tomography (CT), as a fast medical examination technique that does not require special preparation, has a wider range of applications, but its accuracy in organ imaging is lower than MRI. At present, image conversion methods suffer from low resolution and low signal-to-noise ratio in synthesizing MR images. In addition, these methods have limited capabilities and cannot accurately synthesize MR images with diverse anatomical and functional differences. Therefore, this article proposes a multimodal medical image conversion method based on Generative Adversarial Networks (GAN) to convert CT images into MR images. This method uses two parallel generative adversarial networks to generate two MR images to compensate for the missing generation of a single generative adversarial network. The experimental results demonstrate that this method is effective and can generate MR images close to the reference MR image.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chaiqi Guo, Zhibo Guo, Yu Chen, and Meizhen Xia "SDGNet: conversion from CT images to MR images based on dual branch generative adversarial networks", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131812D (19 July 2024); https://doi.org/10.1117/12.3031031

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KEYWORDS

Magnetic resonance imaging

Computed tomography

Education and training

Data modeling

Image fusion

Medical imaging

Voxels

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