Prior Image-Constrained Iterative Reconstruction with Adaptive Step Size for Limited-Angle CBCT

• Published in: IEEE transactions on instrumentation and measurement 2024-01, Vol.73, p.1-1
• Main Authors: Tao, Yiyuan, Zhang, Zhizhou, Hu, Dianlin, Wu, Zhan, Mao, Weilong, Zhu, Guojun, Fei, Xuanjia, Ji, Xu, Zhang, Yikun, Xie, Shipeng, Yao, Yi, Chen, Yang
• Format: Article
• Language: English
• Subjects: adaptive step size; Algorithms; Compressed sensing; Computed tomography; Convexity; Image acquisition; Image enhancement; Image quality; Image reconstruction; Imaging; iterative reconstruction; Limited-angle CT reconstruction; Medical imaging; Planning; prior image constrained; Radiation therapy
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2024.3396857

Cone-beam computed tomography (CBCT) has been widely used in image guided radiotherapy (IGRT). In order to avoid the possible collisions of the moving gantry with patients and devices, the limited-angle scanning protocol is often adopted for CBCT. However, the missing views of projection data will induce severe wedge artifacts to the reconstructed images, having a negative influence on the following therapeutic procedures. Compared to limited-angle CBCT images, the planning CT images (pCT) acquired earlier for the same patient are artifact-free, which has the potential to improve the quality of limited-angle CBCT. In this context, this paper proposes the prior image constrained adaptive step size iterative reconstruction (PICAS) method. PICAS builds on PICCS (Prior Image Constrained Compressed Sensing) but is improved after taking into account the characteristics of IGRT. In PICAS, the high-quality pCT images are regarded as prior images to enhance the performance in artifact removal. To address the mismatch between pCT images and CBCT images, the pCT images are reprojected and reconstructed using the same imaging geometry as CBCT. Then, a transformation matrix between the pseudo limited-angle pCT images and limited-angle images is obtained and it is further applied to the high-quality pCT images to generate the artifact-free prior images. In addition, an adaptive gradient descent optimization based on convex set projection and Lipschitz constant is adopted to accelerate the convergence of the algorithm. The proposed PICAS algorithm has been evaluated on the real data from different parts (head, lung, and abdomen) and under the limited-angle configurations with different scanning ranges (80°, 100° and 120°). Qualitative and quantitative results demonstrate that PICAS has the potential to quickly reconstruct high-quality CBCT images in limited-angle scanning scenes, and thereby improve the accuracy of radiotherapy and shorten the time of surgery.