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PNMC: Four-dimensional conebeam CT reconstruction combining prior network and motion compensation.
Ou, Zhengwei; Xie, Jiayi; Teng, Ze; Wang, Xianghong; Jin, Peng; Du, Jichen; Ding, Mingchao; Li, HuiHui; Chen, Yang; Niu, Tianye.
Afiliación
  • Ou Z; School of Computer Science and Engineering, Southeast University, Nanjing, China; Shenzhen Bay Laboratory, Shenzhen, China.
  • Xie J; Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology, Peking University Third Hospital, Beijing, China; Department of Automation, Tsinghua University, Beijing, China.
  • Teng Z; Beijing Key Laboratory of Magnetic Resonance Imaging Devices and Technology, Peking University Third Hospital, Beijing, China; Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, China; Peking Union Medica
  • Wang X; Shenzhen Bay Laboratory, Shenzhen, China.
  • Jin P; Shenzhen Bay Laboratory, Shenzhen, China.
  • Du J; Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, China.
  • Ding M; Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, China.
  • Li H; Shenzhen Bay Laboratory, Shenzhen, China.
  • Chen Y; School of Computer Science and Engineering, Southeast University, Nanjing, China; Centre de Recherche en Information Biomedical SinoFrancais, Rennes, France.
  • Niu T; Shenzhen Bay Laboratory, Shenzhen, China; Peking University Aerospace School of Clinical Medicine, Aerospace Center Hospital, Beijing, China. Electronic address: niuty@szbl.ac.cn.
Comput Biol Med ; 171: 108145, 2024 Mar.
Article en En | MEDLINE | ID: mdl-38442553
ABSTRACT
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada de Haz Cónico / Tomografía Computarizada Cuatridimensional Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada de Haz Cónico / Tomografía Computarizada Cuatridimensional Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos