Using a deep neural network for four-dimensional CT artifact reduction in image-guided radiotherapy.

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)

Published Date: Sep 1, 2019

Abstract

INTRODUCTION: Breathing artifact may affect the quality of four-dimensional computed tomography (4DCT) images. We developed a deep neural network (DNN)-based artifact reduction method.

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

Artifacts Deep Learning Four-Dimensional Computed Tomography Humans Image Processing, Computer-Assisted Neoplasms Quality Control Radiotherapy, Image-Guided

External Resources

View on PubMed Access via DOI PubMed (31430590)

Using a deep neural network for four-dimensional CT artifact reduction in image-guided radiotherapy.

Abstract

Authors

Keywords

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