PNMC: Four-dimensional conebeam CT reconstruction combining prior network and motion compensation

人工智能 计算机科学 计算机视觉 迭代重建 图像质量 保险丝(电气) 运动(物理) 图像(数学) 模式识别(心理学) 电气工程 工程类
作者
Zhengwei Ou,Jiayi Xie,Ze Teng,Xianghong Wang,Peng Jin,Jichen Du,Mingchao Ding,Huihui Li,Yang Chen,Tianye Niu
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:171: 108145-108145
标识
DOI:10.1016/j.compbiomed.2024.108145
摘要

Four-dimensional conebeam computed tomography (4D CBCT) is an efficient technique to overcome motion artifacts caused by organ motion during breathing. 4D CBCT reconstruction in a single scan usually divides projections into different groups of sparsely sampled data based on the respiratory phases. The reconstructed images within each group present poor image quality due to the limited number of projections. To improve the image quality of 4D CBCT in a single scan, we propose a novel reconstruction scheme that combines prior knowledge with motion compensation. We apply the reconstructed images of the full projections within a single routine as prior knowledge, providing structural information for the network to enhance the restoration structure. The prior network (PN-Net) is proposed to extract features of prior knowledge and fuse them with the sparsely sampled data using an attention mechanism. The prior knowledge guides the reconstruction process to restore the approximate organ structure and alleviates severe streaking artifacts. The deformation vector field (DVF) extracted using deformable image registration among different phases is then applied in the motion-compensated ordered-subset simultaneous algebraic reconstruction algorithm to generate 4D CBCT images. Proposed method has been evaluated using simulated and clinical datasets and has shown promising results by comparative experiment. Compared with previous methods, our approach exhibits significant improvements across various evaluation metrics.
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