التفاصيل البيبلوغرافية
| العنوان: |
A Multi-Resolution Denoising Method for Low-Dose CT Based on the Reconstruction of Wavelet High-Frequency Channel. |
| المؤلفون: |
Hu J; Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China., Hu P; Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China., Gao Y; Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China., Zhao Y; Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China., Li J; Research Center for Healthcare Data Science, Zhejiang Laboratory, Hangzhou, China.; Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China. |
| المصدر: |
Studies in health technology and informatics [Stud Health Technol Inform] 2024 Jan 25; Vol. 310, pp. 750-754. |
| نوع المنشور: |
Journal Article |
| اللغة: |
English |
| بيانات الدورية: |
Publisher: IOS Press Country of Publication: Netherlands NLM ID: 9214582 Publication Model: Print Cited Medium: Internet ISSN: 1879-8365 (Electronic) Linking ISSN: 09269630 NLM ISO Abbreviation: Stud Health Technol Inform |
| أسماء مطبوعة: |
Original Publication: Amsterdam ; Washington, DC : IOS Press, 1991- |
| مواضيع طبية MeSH: |
Tomography, X-Ray Computed* , Algorithms*, Ambulatory Care Facilities ; Artifacts ; Industry |
| مستخلص: |
Computed tomography (CT) is widely applied in contemporary clinic. Due to the radiation risks carried by X-rays, the imaging and post-processing methods of low-dose CT (LDCT) become popular topics in academia and industrial community. Generally, LDCT presents strong noise and artifacts, while existing algorithms cannot completely overcome the blurring effects and meantime reduce the noise. The proposed method enables CT extend to independent frequency channels by wavelet transformation, then two separate networks are established for low-frequency denoising and high-frequency reconstruction. The clean signals from high-frequency channel are reconstructed through channel translation, which is essentially effective in preserving detailed structures. The public dataset from Mayo Clinic was used for model training and testing. The experiments showed that the proposed method achieves a better quantitative result (PSNR: 37.42dB, SSIM: 0.8990) and details recovery visually, which demonstrates our framework can better restore the detailed features while significantly suppressing the noise. |
| فهرسة مساهمة: |
Keywords: Low-does CT; denoising; reconstruction; wavelet transformation |
| تواريخ الأحداث: |
Date Created: 20240125 Date Completed: 20240126 Latest Revision: 20240126 |
| رمز التحديث: |
20240126 |
| DOI: |
10.3233/SHTI231065 |
| PMID: |
38269909 |
| قاعدة البيانات: |
MEDLINE |
