Stabilizing deep tomographic reconstruction: Part A. Hybrid framework and experimental results
Weiwen Wu,
Dianlin Hu,
Wenxiang Cong,
Hongming Shan,
Shaoyu Wang,
Chuang Niu,
Pingkun Yan,
Hengyong Yu,
Varut Vardhanabhuti,
Ge Wang
Affiliations
Weiwen Wu
Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA; School of Biomedical Engineering, Sun Yat-sen University, Shenzhen, Guangdong, China; Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China
Dianlin Hu
The Laboratory of Image Science and Technology, Southeast University, Nanjing, China
Wenxiang Cong
Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
Hongming Shan
Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA; Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
Shaoyu Wang
Department of Electrical & Computer Engineering, University of Massachusetts Lowell, Lowell, MA, USA
Chuang Niu
Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
Pingkun Yan
Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
Hengyong Yu
Department of Electrical & Computer Engineering, University of Massachusetts Lowell, Lowell, MA, USA; Corresponding author
Varut Vardhanabhuti
Department of Diagnostic Radiology, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China; Corresponding author
Ge Wang
Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA; Corresponding author
Summary: A recent PNAS paper reveals that several popular deep reconstruction networks are unstable. Specifically, three kinds of instabilities were reported: (1) strong image artefacts from tiny perturbations, (2) small features missed in a deeply reconstructed image, and (3) decreased imaging performance with increased input data. Here, we propose an analytic compressed iterative deep (ACID) framework to address this challenge. ACID synergizes a deep network trained on big data, kernel awareness from compressed sensing (CS)-inspired processing, and iterative refinement to minimize the data residual relative to real measurement. Our study demonstrates that the ACID reconstruction is accurate, is stable, and sheds light on the converging mechanism of the ACID iteration under a bounded relative error norm assumption. ACID not only stabilizes an unstable deep reconstruction network but also is resilient against adversarial attacks to the whole ACID workflow, being superior to classic sparsity-regularized reconstruction and eliminating the three kinds of instabilities. The bigger picture: Tomographic image reconstruction with deep learning has been a rapidly emerging field since 2016. Recently, a PNAS paper revealed that several well-known deep reconstruction networks are unstable for computed tomography (CT) and magnetic resonance imaging (MRI), and, in contrast, compressed sensing (CS)-inspired reconstruction methods are stable because of their theoretically proven property known as “kernel awareness.” Therefore, for deep reconstruction to realize its full potential and become a mainstream approach for tomographic imaging, it is critically important to stabilize deep reconstruction networks. Here, we propose an analytic compressed iterative deep (ACID) framework to synergize deep learning and compressed sensing through iterative refinement. We anticipate that this integrative model-based data-driven approach will promote the development and translation of deep tomographic image reconstruction networks.
