Coarse-to-Fine Learning for Planning CT-Enhanced Limited-Angle CBCT Reconstruction

In image-guided radiation therapy, the on-board cone-beam computed tomography (CBCT) is usually used for volumetric imaging and the limited-angle scanning protocol is often adopted to avoid the possible collisions of the moving gantry with patients and devices. However, images directly reconstructed using incomplete projection data may suffer from severe artifacts, which cannot provide precise guidance for the following therapeutic procedures. Compared with CBCT, the planning CT (pCT) acquired for the treatment plan can provide high-quality images of the same patient, showing the potential to improve the limited-angle CBCT. In this context, we propose a Multi-dimensional Joint Cascaded Network (MJCNet) which can exploit the prior information from pCT. MJCNet improves the imaging quality of limited-angle CBCT through a coarse-to-fine strategy. In the coarse restoration stage, a 2D network with two encoders that could extract and exploit the information of pCT is used to remove limited-angle artifacts slice-by-slice. In the fine-tuning stage, a 3D network with dense attention mechanism is employed to further improve the image details and remove the inter-slice artifacts. The real data from different parts of human bodies are collected to evaluate the proposed method. Experimental results demonstrate the promising performance of MJCNet in reducing wedge artifacts, restoring image structures and correcting HU numbers.