SCRED-Distillation: Improving Low-Dose CT Image Quality via Feature Fusion and Mutual Learning
The substantial noise inherent in low-dose CT (LDCT) significantly impedes diagnostic accuracy. Although deep learning techniques, particularly CNNs, have offered promise for LDCT denoising, their inherent focus on local features and the scarcity of extensive training data can limit their performanc...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11062456/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849320938355556352 |
|---|---|
| author | Yanqing Wang Xinru Zhan Wanquan Liu Yingying Li Kexin Guo Huafeng Wang |
| author_facet | Yanqing Wang Xinru Zhan Wanquan Liu Yingying Li Kexin Guo Huafeng Wang |
| author_sort | Yanqing Wang |
| collection | DOAJ |
| description | The substantial noise inherent in low-dose CT (LDCT) significantly impedes diagnostic accuracy. Although deep learning techniques, particularly CNNs, have offered promise for LDCT denoising, their inherent focus on local features and the scarcity of extensive training data can limit their performance and ability to generalize effectively. To address these critical shortcomings, we introduce SCRED-Distillation, a novel denoising method. This approach synergistically integrates the global contextual awareness of Transformer architectures with the efficiency and regularization benefits of Knowledge Distillation. By effectively leveraging both local and global image characteristics, SCRED-Distillation achieves demonstrably superior denoising results. Furthermore, to enhance the model’s capacity for robust generalization across diverse datasets, we employ a mutual learning framework during training. Extensive quantitative evaluations conducted on the challenging Mayo Clinic LDCT Grand Challenge dataset reveal remarkable improvements in key image quality metrics: the Peak Signal-to-Noise Ratio (PSNR) increased significantly from 29.2489 to 33.2103, the Structural Similarity Index Measure (SSIM) steadily rose from 0.8759 to 0.9132, and the Root Mean Squared Error (RMSE) was effectively reduced from 14.2416 to 8.9377. Notably, SCRED-Distillation effectively suppresses noise artifacts while crucially preserving fine diagnostic details, leading to clearer and more reliable medical images and ultimately facilitating more accurate clinical diagnoses. |
| format | Article |
| id | doaj-art-4bc0ac968b7a41bc9209d63d504092f2 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4bc0ac968b7a41bc9209d63d504092f22025-08-20T03:49:55ZengIEEEIEEE Access2169-35362025-01-011311822411823610.1109/ACCESS.2025.358500111062456SCRED-Distillation: Improving Low-Dose CT Image Quality via Feature Fusion and Mutual LearningYanqing Wang0https://orcid.org/0009-0003-2765-4434Xinru Zhan1Wanquan Liu2https://orcid.org/0000-0003-4910-353XYingying Li3Kexin Guo4https://orcid.org/0000-0002-0459-5027Huafeng Wang5https://orcid.org/0000-0002-8267-672XDepartment of Radiology, Changzhi People’s Hospital, Changzhi, Shanxi, ChinaSchool of Information Technology, North China University of Technology, Beijing, ChinaSchool of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, ChinaSchool of Information Technology, North China University of Technology, Beijing, ChinaHangzhou Innovation Institute, Beihang University, Hangzhou, ChinaSchool of Information Technology, North China University of Technology, Beijing, ChinaThe substantial noise inherent in low-dose CT (LDCT) significantly impedes diagnostic accuracy. Although deep learning techniques, particularly CNNs, have offered promise for LDCT denoising, their inherent focus on local features and the scarcity of extensive training data can limit their performance and ability to generalize effectively. To address these critical shortcomings, we introduce SCRED-Distillation, a novel denoising method. This approach synergistically integrates the global contextual awareness of Transformer architectures with the efficiency and regularization benefits of Knowledge Distillation. By effectively leveraging both local and global image characteristics, SCRED-Distillation achieves demonstrably superior denoising results. Furthermore, to enhance the model’s capacity for robust generalization across diverse datasets, we employ a mutual learning framework during training. Extensive quantitative evaluations conducted on the challenging Mayo Clinic LDCT Grand Challenge dataset reveal remarkable improvements in key image quality metrics: the Peak Signal-to-Noise Ratio (PSNR) increased significantly from 29.2489 to 33.2103, the Structural Similarity Index Measure (SSIM) steadily rose from 0.8759 to 0.9132, and the Root Mean Squared Error (RMSE) was effectively reduced from 14.2416 to 8.9377. Notably, SCRED-Distillation effectively suppresses noise artifacts while crucially preserving fine diagnostic details, leading to clearer and more reliable medical images and ultimately facilitating more accurate clinical diagnoses.https://ieeexplore.ieee.org/document/11062456/Image denoisingdeep learningtransformermutual learning |
| spellingShingle | Yanqing Wang Xinru Zhan Wanquan Liu Yingying Li Kexin Guo Huafeng Wang SCRED-Distillation: Improving Low-Dose CT Image Quality via Feature Fusion and Mutual Learning IEEE Access Image denoising deep learning transformer mutual learning |
| title | SCRED-Distillation: Improving Low-Dose CT Image Quality via Feature Fusion and Mutual Learning |
| title_full | SCRED-Distillation: Improving Low-Dose CT Image Quality via Feature Fusion and Mutual Learning |
| title_fullStr | SCRED-Distillation: Improving Low-Dose CT Image Quality via Feature Fusion and Mutual Learning |
| title_full_unstemmed | SCRED-Distillation: Improving Low-Dose CT Image Quality via Feature Fusion and Mutual Learning |
| title_short | SCRED-Distillation: Improving Low-Dose CT Image Quality via Feature Fusion and Mutual Learning |
| title_sort | scred distillation improving low dose ct image quality via feature fusion and mutual learning |
| topic | Image denoising deep learning transformer mutual learning |
| url | https://ieeexplore.ieee.org/document/11062456/ |
| work_keys_str_mv | AT yanqingwang screddistillationimprovinglowdosectimagequalityviafeaturefusionandmutuallearning AT xinruzhan screddistillationimprovinglowdosectimagequalityviafeaturefusionandmutuallearning AT wanquanliu screddistillationimprovinglowdosectimagequalityviafeaturefusionandmutuallearning AT yingyingli screddistillationimprovinglowdosectimagequalityviafeaturefusionandmutuallearning AT kexinguo screddistillationimprovinglowdosectimagequalityviafeaturefusionandmutuallearning AT huafengwang screddistillationimprovinglowdosectimagequalityviafeaturefusionandmutuallearning |