DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction

• URL: http://arxiv.org/abs/2505.02628v1
• Date: Mon, 05 May 2025 13:14:49 GMT
• Title: DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction
• Authors: Yiqun Lin, Hualiang Wang, Jixiang Chen, Jiewen Yang, Jiarong Guo, Xiaomeng Li,
• Abstract summary: Sparse-view reconstruction reduces radiation by using fewer X-ray projections while maintaining image quality.<n>Existing methods face challenges such as high computational demands and poor generalizability to different datasets.<n>We propose DeepSparse, the first foundation model for sparse-view CBCT reconstruction, featuring DiCE, a novel network that integrates multi-view 2D features and multi-scale 3D features.
• Score: 9.579390210009521
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Cone-beam computed tomography (CBCT) is a critical 3D imaging technology in the medical field, while the high radiation exposure required for high-quality imaging raises significant concerns, particularly for vulnerable populations. Sparse-view reconstruction reduces radiation by using fewer X-ray projections while maintaining image quality, yet existing methods face challenges such as high computational demands and poor generalizability to different datasets. To overcome these limitations, we propose DeepSparse, the first foundation model for sparse-view CBCT reconstruction, featuring DiCE (Dual-Dimensional Cross-Scale Embedding), a novel network that integrates multi-view 2D features and multi-scale 3D features. Additionally, we introduce the HyViP (Hybrid View Sampling Pretraining) framework, which pretrains the model on large datasets with both sparse-view and dense-view projections, and a two-step finetuning strategy to adapt and refine the model for new datasets. Extensive experiments and ablation studies demonstrate that our proposed DeepSparse achieves superior reconstruction quality compared to state-of-the-art methods, paving the way for safer and more efficient CBCT imaging.

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