Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Time-frequency domain prior constrained deep unfolding network for low-dose CT reconstruction. |
| Συγγραφείς: |
Zhang, Xiong, Zhang, Xinbo, Li, Xinzhong, Ali, Zulfiqur, Wang, Yue, Shangguan, Hong, Cui, Xueying |
| Πηγή: |
Journal of X-Ray Science & Technology; Sep2025, Vol. 33 Issue 5, p819-830, 12p |
| Θεματικοί όροι: |
Deep learning, Computed tomography, Iterative methods (Mathematics), Image processing, Convolutional neural networks, Image enhancement (Imaging systems) |
| Περίληψη: |
Background: Low-dose computed tomography (LDCT) effectively reduces the risk of malignant disease; however, reducing the radiation dose introduces additional noise and stripe artifacts in the CT imaging process. While Convolutional Neural Networks (CNN) have demonstrated performance advantages in LDCT imaging tasks, their end-to-end network architecture limits adaptability to CT reconstruction tasks, leaving room for further performance improvement. Objective: To propose a low-dose CT reconstruction network based on the iterative algorithms, incorporating an interpretable network architecture to achieve superior reconstruction performance. Methods: To better adapt to CT reconstruction tasks, we proposed an interpretable deep unfolding network leveraging time-frequency and image domain priors to fully exploit the features extracted in the transform domain. The iterative optimization process of the proposed algorithm is mapped into a deep unfolding network, and a Stage Information Memory Network (SIMN) is designed to address information loss between adjacent stages and within each stage. Results: Experimental results on Mayo and Piglet datasets show that the proposed model outperforms state-of-the-art techniques in both quantitative metrics and visual quality. Conclusions: The proposed network effectively removes artifacts and noise from low-dose CT images, achieving excellent reconstruction performance. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |