Cited by 105Image and Signal Denoising MethodsMedical Imaging Techniques and ApplicationsGenerative Adversarial Networks and Image Synthesis

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Wavelet-Improved Score-Based Generative Model for Medical Imaging

Wu, Weiwen, Wang, Yanyang, Liu, Qiegen, Wang, Ge, Zhang, Jianjia·2024·IEEE Transactions on Medical Imaging

DOI 10.1109/tmi.2023.3325824

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Abstract

The score-based generative model (SGM) has demonstrated remarkable performance in addressing challenging under-determined inverse problems in medical imaging. However, acquiring high-quality training datasets for these models remains a formidable task, especially in medical image reconstructions. Prevalent noise perturbations or artifacts in low-dose Computed Tomography (CT) or under-sampled Magnetic Resonance Imaging (MRI) hinder the accurate estimation of data distribution gradients, thereby compromising the overall performance of SGMs when trained with these data. To alleviate this issue, we propose a wavelet-improved denoising technique to cooperate with the SGMs, ensuring effective and stable training. Specifically, the proposed method integrates a wavelet sub-network and the standard SGM sub-network into a unified framework, effectively alleviating inaccurate distribution of the data distribution gradient and enhancing the overall stability. The mutual feedback mechanism between the wavelet sub-network and the SGM sub-network empowers the neural network to learn accurate scores even when handling noisy samples. This combination results in a framework that exhibits superior stability during the learning process, leading to the generation of more precise and reliable reconstructed images. During the reconstruction process, we further enhance the robustness and quality of the reconstructed images by incorporating regularization constraint. Our experiments, which encompass various scenarios of low-dose and sparse-view CT, as well as MRI with varying under-sampling rates and masks, demonstrate the effectiveness of the proposed method by significantly enhanced the quality of the reconstructed images. Especially, our method with noisy training samples achieves comparable results to those obtained using clean data. Our code at https://zenodo.org/record/8266123.

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Authors: Wu, W., Wang, Y., Liu, Q., Wang, G., & Zhang, J.
Affiliations: Sun Yat-sen University (CN)
Nanchang University (CN)
Rensselaer Polytechnic Institute (US)
ORCID: Wu, Weiwen
Wang, Yanyang
Liu, Qiegen
Wang, Ge
Zhang, Jianjia
Venue: IEEE Transactions on Medical Imaging
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2024-03
Volume: 43
Issue: 3
Pages: 966-979
ISSN: 0278-0062 (ISSN-L)
1558-254X
DOI: 10.1109/tmi.2023.3325824
OpenAlex: W4387789684
Cited by: 105
References: 63
Citations by year: 2026: 23 · 2025: 32 · 2024: 45
FWCI: 9.53
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Medical Imaging (publishedVersion)
PubMed (publishedVersion)
Funders: National Natural Science Foundation of China (62201628, 62122033, 62101611)
National Key Research and Development Program of China (2022YFA1204201)
National Cancer Institute (NCI) of the National Institutes of Health (R01HL151561, R01EB031102, R01EB032716)
Basic and Applied Basic Research Foundation of Guangdong Province (2022A1515011375, 2023A1515012278, 2023A1515011780)
Shenzhen Science and Technology Program (JCYJ20220530145411027, JCYJ20220818102414031)
Key Research and Development Program of Jianxi Province (20210BBE53001)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
Keywords: score-based generative models, wavelet denoising, medical image reconstruction, low-dose CT, sparse-view CT, noisy training data, training stability, regularization, inverse problems
MeSH terms: Algorithms, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Tomography, X-Ray Computed, Artifacts, Neural Networks, Computer, Signal-To-Noise Ratio

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Wavelet-Improved Score-Based Generative Model for Medical Imaging

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