作者
Daiju Ueda, Yutaka Katayama, Akira Yamamoto, Tsutomu Ichinose, Hironori Arima, Yusuke Watanabe, Shannon L Walston, Hiroyuki Tatekawa, Hirotaka Takita, Takashi Honjo, Akitoshi Shimazaki, Daijiro Kabata, Takao Ichida, Takeo Goto, Yukio Miki
发表日期
2021/6
期刊
Radiology
卷号
299
期号
3
页码范围
675-681
出版商
Radiological Society of North America
简介
Background
Digital subtraction angiography (DSA) generates an image by subtracting a mask image from a dynamic angiogram. However, patient movement–caused misregistration artifacts can result in unclear DSA images that interrupt procedures.
Purpose
To train and to validate a deep learning (DL)–based model to produce DSA-like cerebral angiograms directly from dynamic angiograms and then quantitatively and visually evaluate these angiograms for clinical usefulness.
Materials and Methods
A retrospective model development and validation study was conducted on dynamic and DSA image pairs consecutively collected from January 2019 through April 2019. Angiograms showing misregistration were first separated per patient by two radiologists and sorted into the misregistration test data set. Nonmisregistration angiograms were divided into development and external test data sets at a ratio of 8:1 …
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D Ueda, Y Katayama, A Yamamoto, T Ichinose… - Radiology, 2021