Synthetic breath-hold CT generation from free-breathing CT: a novel deep learning approach to predict cardiac dose reduction in deep-inspiration breath-hold radiotherapy.

Koide, Yutaro; Shimizu, Hidetoshi; Wakabayashi, Kohei; Kitagawa, Tomoki; Aoyama, Takahiro; Miyauchi, Risei; Tachibana, Hiroyuki; Kodaira, Takeshi

Koide, Yutaro; Shimizu, Hidetoshi; Wakabayashi, Kohei; Kitagawa, Tomoki; Aoyama, Takahiro; Miyauchi, Risei; Tachibana, Hiroyuki; Kodaira, Takeshi.

Affiliation

Koide Y; Department of Radiation Oncology, Aichi Cancer Center, Chikusa-ku, Nagoya 464-0021, Japan.
Shimizu H; Department of Radiation Oncology, Aichi Cancer Center, Chikusa-ku, Nagoya 464-0021, Japan.
Wakabayashi K; Department of Radiation Oncology, Aichi Cancer Center, Chikusa-ku, Nagoya 464-0021, Japan.
Kitagawa T; Department of Radiation Oncology, Aichi Cancer Center, Chikusa-ku, Nagoya 464-0021, Japan.
Aoyama T; Department of Radiation Oncology, Aichi Cancer Center, Chikusa-ku, Nagoya 464-0021, Japan.
Miyauchi R; Department of Radiation Oncology, Aichi Cancer Center, Chikusa-ku, Nagoya 464-0021, Japan.
Tachibana H; Department of Radiation Oncology, Aichi Cancer Center, Chikusa-ku, Nagoya 464-0021, Japan.
Kodaira T; Department of Radiation Oncology, Aichi Cancer Center, Chikusa-ku, Nagoya 464-0021, Japan.

J Radiat Res ; 2021 Aug 31.

Article in En | MEDLINE | ID: mdl-34467396

Key words

breast cancer; deep learning; deep-inspiration breath-hold (DIBH); machine learning (ML); predictive assay; radiotherapy

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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: J Radiat Res Year: 2021 Document type: Article

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