Prior-image-based low-dose CT reconstruction for adaptive radiation therapy.

Autor:	Xu Y; Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, People's Republic of China.; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China.; Shanghai Clinical Research Center for Radiation Oncology, Shanghai 200032, People's Republic of China.; Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, People's Republic of China., Wang J; Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, People's Republic of China.; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China.; Shanghai Clinical Research Center for Radiation Oncology, Shanghai 200032, People's Republic of China.; Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, People's Republic of China., Hu W; Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, People's Republic of China.; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China.; Shanghai Clinical Research Center for Radiation Oncology, Shanghai 200032, People's Republic of China.; Shanghai Key Laboratory of Radiation Oncology, Shanghai 200032, People's Republic of China.
Jazyk:	angličtina
Zdroj:	Physics in medicine and biology [Phys Med Biol] 2024 Oct 16; Vol. 69 (21). Date of Electronic Publication: 2024 Oct 16.
DOI:	10.1088/1361-6560/ad7b9b
Abstrakt:	Objective . The study aims to reduce the imaging radiation dose in Adaptive Radiotherapy (ART) while maintaining high-quality CT images, critical for effective treatment planning and monitoring. Approach . We developed the Prior-aware Learned Primal-Dual Network (pLPD-UNet), which uses prior CT images to enhance reconstructions from low-dose scans. The network was separately trained on thorax and abdomen datasets to accommodate the unique imaging requirements of each anatomical region. Main results . The pLPD-UNet demonstrated improved reconstruction accuracy and robustness in handling sparse data compared to traditional methods. It effectively maintained image quality essential for precise organ delineation and dose calculation, while achieving a significant reduction in radiation exposure. Significance . This method offers a significant advancement in the practice of ART by integrating prior imaging data, potentially setting a new standard for balancing radiation safety with the need for high-resolution imaging in cancer treatment planning. (© 2024 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)
Databáze:	MEDLINE
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