Prediction of the motion of chest internal points using a recurrent neural network trained with real-time recurrent learning for latency compensation in lung cancer radiotherapy.

Pohl, Michel; Uesaka, Mitsuru; Demachi, Kazuyuki; Bhusal Chhatkuli, Ritu

Pohl, Michel; Uesaka, Mitsuru; Demachi, Kazuyuki; Bhusal Chhatkuli, Ritu.

Afiliação

Pohl M; The University of Tokyo, Graduate School of Engineering, Department of Bioengineering, Tokyo, Japan. Electronic address: michel.pohl@centrale-marseille.fr.
Uesaka M; The University of Tokyo, Graduate School of Engineering, Department of Bioengineering, Tokyo, Japan; The University of Tokyo, Graduate School of Engineering, Department of Nuclear Engineering and Management, Tokyo, Japan.
Demachi K; The University of Tokyo, Graduate School of Engineering, Department of Nuclear Engineering and Management, Tokyo, Japan.
Bhusal Chhatkuli R; National Institute for Quantum and Radiological Science and Technology, Chiba, Japan.

Comput Med Imaging Graph ; 91: 101941, 2021 07.

Article em En | MEDLINE | ID: mdl-34265553

Assuntos

Neoplasias Pulmonares; Redes Neurais de Computação; Algoritmos; Humanos; Pulmão; Neoplasias Pulmonares/diagnóstico por imagem; Neoplasias Pulmonares/radioterapia; Movimento (Física)

Palavras-chave

Deformable image registration; Latency compensation; Lucas-Kanade optical flow; Lung cancer radiotherapy; Real-time recurrent learning; Recurrent neural network

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Redes Neurais de Computação / Neoplasias Pulmonares Idioma: En Ano de publicação: 2021 Tipo de documento: Article

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