An interpretable cascaded residual iterative network for sparse-view spectral CT imaging
This study established a general iterative framework, referred to as ICRIN, and discussed its advantages in terms of interpretability, generalizability, and data consistency in a mathematical modeling context. ICRIN could be applied across a wider range of spectral computed tomography (CT) imaging tasks, enabling clinical multi-task imaging and material quantification.
Authors
- Ningning Liang (ORCID: https://orcid.org/0000-0002-6548-8030)
- Ailong Cai (ORCID: https://orcid.org/0000-0001-6128-0876)
- Hengyong Yu
- Lei Li
- Zhizhong Zheng
- Xinrui Zhang
- Shaoyu Wang
- Bin Yan
Institutions
- University of Massachusetts Lowell (US)
- Nanchang University (CN)
- PLA Information Engineering University (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2026-02-13
- DOI
- https://doi.org/10.21037/qims-2025-1895
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- University of Hong Kong
- Natural Science Foundation of Henan Province