The DOI matches its Crossref record; 62 of the 63 references it cites were found in a registry or an index.
Wavelet-Improved Score-Based Generative Model for Medical Imaging
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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“Synthetic data generation using generative models can create realistic, diverse data sets that mimic real-world multimodal medical scenarios, thus expanding the training data set without compromising privacy or ethical standards [149-151].”
From Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook. (2024) · cites this paper as [149]
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Cites
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Research works printed but not linked: 1
- Batson, Noise2Self: Blind denoising by self-supervision, 524
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Multimodal Large Language Models in Health Care: Applications, Challenges, and Future Outlook.
AlSaad, R., Abd-alrazaq, A., Boughorbel, S., Ahmed, A., Renault, M.-A., Damseh, R., & Sheikh, J. (2024) · Journal of medical Internet research · doi:10.2196/59505 · Cited by 349
“Synthetic data generation using generative models can create realistic, diverse data sets that mimic real-world multimodal medical scenarios, thus expanding the training data set without compromising privacy or ethical standards [149-151].”
Iterative Residual Optimization Network for Limited-Angle Tomographic Reconstruction
Pan, J., Yu, H., Gao, Z., Wang, S., Zhang, H., & Wu, W. (2024) · IEEE Transactions on Image Processing · doi:10.1109/tip.2024.3351382 · Cited by 77
Self-citationStage-by-Stage Wavelet Optimization Refinement Diffusion Model for Sparse-View CT Reconstruction
Xu, K., Lu, S., Huang, B., Wu, W., & Liu, Q. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3355455 · Cited by 62
Self-citationKorkmaz, C., Tekalp, A., & Doğan, Z. (2024) · Computer Vision and Pattern Recognition · doi:10.1109/cvpr52733.2024.00566 · Cited by 56
Multi-Channel Optimization Generative Model for Stable Ultra-Sparse-View CT Reconstruction
Wu, W., Pan, J., Wang, Y., Wang, S., & Zhang, J. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3376414 · Cited by 55
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Metadata
- Authors
- Wu, W., Wang, Y., Liu, Q., Wang, G., & Zhang, J.
- Affiliations
- Sun Yat-sen University (CN)
- Nanchang University (CN)
- Rensselaer Polytechnic Institute (US)
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2024-03
- Volume
- 43
- Issue
- 3
- Pages
- 966-979
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- 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
- 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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