コイケ　ユウヘイ   KOIKE YUHEI   小池　優平    所属   関西医科大学  放射線科学講座    職種   助教
論文種別	原著(症例報告除く)
言語種別	英語
査読の有無	査読あり
表題	Improvement of image quality for pancreatic cancer using deep learning-generated virtual monochromatic images: Comparison with single-energy computed tomography
掲載誌名	正式名：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) 略　 称：Phys Med ISSNコード：11201797/1724191X
掲載区分	国外
巻・号・頁	85,pp.8-14
著者・共著者	Ohira S, Koike Y, Akino Y, Kanayama N, Wada K, Ueda Y, Masaoka A, Washio H, Miyazaki M, Koizumi M, Ogawa K, Teshima T.
発行年月	2021/05
概要	PURPOSE: To construct a deep convolutional neural network that generates virtual monochromatic images (VMIs) from single-energy computed tomography (SECT) images for improved pancreatic cancer imaging quality. MATERIALS AND METHODS: Fifty patients with pancreatic cancer underwent a dual-energy CT simulation and VMIs at 77 and 60 keV were reconstructed. A 2D deep densely connected convolutional neural network was modeled to learn the relationship between the VMIs at 77 (input) and 60 keV (ground-truth). Subsequently, VMIs were generated for 20 patients from SECT images using the trained deep learning model. RESULTS: The contrast-to-noise ratio was significantly improved (p < 0.001) in the generated VMIs (4.1 ± 1.8) compared to the SECT images (2.8 ± 1.1). The mean overall image quality (4.1 ± 0.6) and tumor enhancement (3.6 ± 0.6) in the generated VMIs assessed on a five-point scale were significantly higher (p < 0.001) than that in the SECT images (3.2 ± 0.4 and 2.8 ± 0.4 for overall image quality and tumor enhancement, respectively). CONCLUSIONS: The quality of the SECT image was significantly improved both objectively and subjectively using the proposed deep learning model for pancreatic tumors in radiotherapy.
DOI	10.1016/j.ejmp.2021.03.035
PMID	33940528