C^2RV: Cross-Regional and Cross-View Learning for Sparse-View CBCT Reconstruction

TL;DR

This paper introduces C^2RV, a novel method for sparse-view CBCT reconstruction that leverages multi-scale volumetric representations and cross-view attention to improve spatial consistency and reconstruction quality.

Contribution

The paper proposes a cross-regional and cross-view learning framework using explicit multi-scale volumetric features and a scale-view cross-attention module for better sparse-view CBCT reconstruction.

Findings

Significant improvement over state-of-the-art methods.

Effective handling of diverse anatomical structures.

Enhanced spatial consistency in reconstructed images.

Abstract

Cone beam computed tomography (CBCT) is an important imaging technology widely used in medical scenarios, such as diagnosis and preoperative planning. Using fewer projection views to reconstruct CT, also known as sparse-view reconstruction, can reduce ionizing radiation and further benefit interventional radiology. Compared with sparse-view reconstruction for traditional parallel/fan-beam CT, CBCT reconstruction is more challenging due to the increased dimensionality caused by the measurement process based on cone-shaped X-ray beams. As a 2D-to-3D reconstruction problem, although implicit neural representations have been introduced to enable efficient training, only local features are considered and different views are processed equally in previous works, resulting in spatial inconsistency and poor performance on complicated anatomies. To this end, we propose C^2RV by leveraging explicit multi-scale volumetric representations to enable cross-regional learning in the 3D space. Additionally, the scale-view cross-attention module is introduced to adaptively aggregate multi-scale and multi-view features. Extensive experiments demonstrate that our C^2RV achieves consistent and significant improvement over previous state-of-the-art methods on datasets with diverse anatomy.