Simulated four-dimensional CT for markerless tumor tracking using a deep learning network with multi-task learning.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

PMID: 33189045

Abstract

INTRODUCTION: Our markerless tumor tracking algorithm requires 4DCT data to train models. 4DCT cannot be used for markerless tracking for respiratory-gated treatment due to inaccuracies and a high radiation dose. We developed a deep neural network (DNN) to generate 4DCT from 3DCT data.

Authors

Shinichiro Mori

Research Center for Charged Particle Therapy, National Institute of Radiological Sciences, Inage-ku, Chiba 263-8555, Japan.. Electronic address: mori.shinichiro@qst.go.jp.
Ryusuke Hirai

Corporate Research and Development Center, Toshiba Corporation, Kanagawa 212-8582, Japan. Electronic address: ryusuke.hirai@toshiba.co.jp.
Yukinobu Sakata

Corporate Research and Development Center, Toshiba Corporation, Kanagawa 212-8582, Japan.

Keywords

Algorithms Deep Learning Four-Dimensional Computed Tomography Humans Lung Neoplasms Neoplasms Neural Networks, Computer

External Resources

View on PubMed Access via DOI PubMed (33189045)

Simulated four-dimensional CT for markerless tumor tracking using a deep learning network with multi-task learning.

Abstract

Authors

Keywords

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