Non paired CMR image enhancement based on improved CycleGAN

doi:10.3969/j.issn.1000-1565.2025.02.011

Abstract

Abstract: During the process of cardiac magnetic resonance imaging(CMR), patient misoperation, respiratory movement, and arrhythmia can cause a decrease in CMR image quality. To solve the problem of manually creating paired data and losing some tissue texture details after image enhancement in existing CMR image enhancement networks This paper proposes a cycle consistent generative adversarial network based on spatial frequency domain feature learning(SFFL CycleGAN). The research results show that this network does not require manually creating paired datasets, and the enhanced CMR images have rich texture details. It is superior to existing paired training networks and the original CycleGAN network in terms of Structural Similarity(SSIM)and Peak Signal to Noise Ratio(PSNR). The image enhancement effect is good and effectively assists in disease diagnosis.

Key words: cardiac magnetic resonance, image enhancement, spatial-frequency domain feature, cycle-consistent generative adversarial network

CLC Number:

TP391.9

ZHENG Wei, WU Yubo, FENG Xiaomeng, MA Zepeng,SONG Tierui. Non paired CMR image enhancement based on improved CycleGAN[J]. Journal of Hebei University(Natural Science Edition), 2025, 45(2): 204-215.

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