DeepSparse: A Foundation Model for Sparse-View CBCT Reconstruction
Journal:
arXiv
Published Date:
May 5, 2025
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.
