Frontiers in Nuclear Medicine · Published 2026-07-06 · DOI 10.3389/fnume.2026.1711533
Junling Wang, Chunhua Zou, Hongjie Yang, Peixi Liao, Xiaojun Mo, Yingcui Gao, Yanan Ning, Jiahao Lai
IntroductionSparse-view computed tomography (CT) reconstruction is crucial for clinical diagnostics, as reducing radiation exposure is essential to minimize risks to patients. Existing dual-domain reconstruction methods leverage both image and projection domains but often process them sequentially, overlooking their implicit correlations.MethodsTo address this limitation, we propose Cross-Domain TransNet, a Transformer-based dual-domain framework for sparse-view CT reconstruction. The proposed model captures long-range dependencies within each domain and integrates image and sinogram representations through a hybrid self-attention mechanism. In addition, a Convolution Fusion Layer (CFL) is introduced to enhance feature interactions and facilitate more effective utilization of dual-domain information.ResultsExtensive experiments on the NIH-AAPM dataset demonstrate the superior performance and generalization capability of the proposed method under various sparse-view settings. The results show that Cross-Domain TransNet consistently improves reconstruction quality, effectively suppresses noise, and reduces artifacts, outperforming both conventional reconstruction algorithms and state-of-the-art deep learning approaches.ConclusionCross-Domain TransNet provides an effective and robust solution for sparse-view CT reconstruction. By fully exploiting complementary information from both image and projection domains, the proposed framework enhances diagnostic image quality while supporting radiation dose reduction.
Abstract from DOAJ. Public domain (CC0 1.0).
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Wang, J., Zou, C., Yang, H., et al. (2026). Cross-Domain TransNet for sparse-view CT reconstruction. Frontiers in Nuclear Medicine. https://doi.org/10.3389/fnume.2026.1711533