Projection-based deep learning super-resolution for CBCT dose reduction

被引：0

作者：

Thummerer, Adrian ^{[1
]}

Hofmaier, Jan ^{[1
]}

Belka, Claus ^{[1
,2
,3
]}

Landry, Guillaume ^{[1
]}

Kurz, Christopher ^{[1
]}

机构：

[1] Ludwig Maximilians Univ Munchen, LMU Univ Hosp, Dept Radiat Oncol, Munich, Germany

[2] German Canc Consortium DKTK, Partner Site Munich, Munich, Germany

[3] Bavarian Canc Res Ctr, BZKF, Munich, Germany

来源：

RADIOTHERAPY AND ONCOLOGY | 2024年 / 194卷

关键词：

Deep learning; dose reduction; CBCT;

D O I：

暂无

中图分类号：

R73 [肿瘤学];

学科分类号：

100214 ;

摘要：

2547

引用

页码：S3930 / S3934

页数：6

共 3 条

[1] Image guidance doses delivered during radiotherapy: Quantification, management, and reduction: Report of the AAPM Therapy Physics Committee Task Group 180 [J].

Ding, George X. ;

Alaei, Parham ;

Curran, Bruce ;

Flynn, Ryan ;

Gossman, Michael ;

Mackie, T. Rock ;

Miften, Moyed ;

Morin, Richard ;

Xu, X. George ;

Zhu, Timothy C. .

MEDICAL PHYSICS, 2018, 45 (05) :E84-E99

[2] Review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction [J].

Liu, Hefei ;

Schaal, David ;

Curry, Heather ;

Clark, Ryan ;

Magliari, Anthony ;

Kupelian, Patrick ;

Khuntia, Deepak ;

Beriwal, Sushil .

RADIATION ONCOLOGY, 2023, 18 (01)

[3]

Wang XP, 2018, IDEAS HIST MOD CHINA, V19, P1, DOI 10.1163/9789004385580_002

← 1 →