Loading...
Send to Friend
 Bookmark
 Review List
  1. Simple search
  2. Search result
  3. PARS-NET: A novel deep learning framework using parallel residual conventional neural networks for sparse-view CT reconstruction

PARS-NET: A novel deep learning framework using parallel residual conventional neural networks for sparse-view CT reconstruction

Khodajou Chokami, H ; Sharif University of Technology | 2022

91 Viewed
  1. Type of Document: Article
  2. DOI: 10.1088/1748-0221/17/02/P02011
  3. Publisher: IOP Publishing Ltd , 2022
  4. Abstract:
  5. Sparse-view computed tomography (CT) is recently proposed as a promising method to speed up data acquisition and alleviate the issue of CT high dose delivery to the patients. However, traditional reconstruction algorithms are time-consuming and suffer from image degradation when faced with sparse-view data. To address this problem, we propose a new framework based on deep learning (DL) that can quickly produce high-quality CT images from sparsely sampled projections and is able for clinical use. Our DL-based proposed model is based on the convolution, and residual neural networks in a parallel manner, named the parallel residual neural network (PARS-Net). Besides, our proposed PARS-Net model benefits from a loss based on the geodesic distance to effectively reflect image structures. Experiments have been performed on the combination of two large-scale CT datasets consisting of CT images of whole-body patients for different sparse projection views including 120, 60, and 30 views. Our experimental results show that PARS-Net is 4-5 times faster than the state-of-the-art DL-based models, with fewer memory requirements, better performance in other objective quality evaluations, and improved visual quality. Results showed that our PARS-Net model was superior to the latest methods, demonstrating the feasibility of using this model for high-quality CT image reconstruction from sparsely sampled projections. © 2022 IOP Publishing Ltd and Sissa Medialab
  6. Keywords:
  7. Parallel neural networks ; Residual neural networks ; Sparse-view CT reconstruction ; Data acquisition ; Image reconstruction ; Large dataset ; Computed tomography images ; Deep learning ; Euclidean metrics ; Log-Euclidean ; Log-euclidean metric ; Neural-networks ; Residual neural network ; Sparse-view computed tomography reconstruction ; Tomography reconstruction ; Computerized tomography
  8. Source: Journal of Instrumentation ; Volume 17, Issue 2 , 2022 ; 17480221 (ISSN)
  9. URL: https://iopscience.iop.org/article/10.1088/1748-0221/17/02/P02011