Deep Learning–based Angiogram Generation Model for Cerebral Angiography without Misregistration Artifacts-Reference-Cited by-同舟云学术

Deep Learning–based Angiogram Generation Model for Cerebral Angiography without Misregistration Artifacts

Published:2021-06 Issue:3 Volume:299 Page:675-681
ISSN:0033-8419
Container-title:Radiology
language:en
Short-container-title:Radiology

Author:

Ueda Daiju¹^ORCID,Katayama Yutaka¹,Yamamoto Akira¹^ORCID,Ichinose Tsutomu¹,Arima Hironori¹^ORCID,Watanabe Yusuke¹^ORCID,Walston Shannon L.¹^ORCID,Tatekawa Hiroyuki¹^ORCID,Takita Hirotaka¹^ORCID,Honjo Takashi¹^ORCID,Shimazaki Akitoshi¹,Kabata Daijiro¹,Ichida Takao¹,Goto Takeo¹^ORCID,Miki Yukio¹^ORCID

Affiliation:

1. From the Departments of Diagnostic and Interventional Radiology (D.U., A.Y., S.L.W., H. Tatekawa, H. Takita, T.H., A.S., Y.M.), Neurosurgery (T. Ichinose, H.A., Y.W., T.G.), and Medical Statistics (D.K.), Graduate School of Medicine, Osaka City University, 1-4-3 Asahi-machi, Abeno-ku, Osaka 545-8585, Japan; and Department of Radiology, Osaka City University Hospital, 1-5-7 Asahi-machi, Abeno-ku, Osaka, 545-8586, Japan (Y.K., T. Ichida).

Funder

Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research

Publisher

Radiological Society of North America (RSNA)

Subject

Radiology, Nuclear Medicine and imaging

Link

http://pubs.rsna.org/doi/pdf/10.1148/radiol.2021203692

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