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Self-consistent deep learning-based boosting of 4D cone-beam computed tomography reconstruction
Author(s): Frederic Madesta; Tobias Gauer; Thilo Sentker; René Werner
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Paper Abstract

Inter-fractional magnitude and trajectory changes are of great importance for radiotherapy (RT) of moving targets. In order to verify the amount and characteristics of patient-specific respiratory motion prior to each RT treatment session, a time-resolved cone-beam computed tomography (4D CBCT) is necessary. However, due to sparse view artifacts, the resulting image quality is limited when applying current 4D CBCT reconstruction approaches. In this study, a new deep learning-based boosting approach for 4D CBCT reconstruction is presented that does not rely on any a-priori information (e.g. 4D CT images) and is applicable to arbitrary reconstruction algorithms. It is shown that the overall image quality is significantly improved after boosting; in particular, sparse view sampling artifacts are suppressed.

Paper Details

Date Published: 15 March 2019
PDF: 6 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094902 (15 March 2019); doi: 10.1117/12.2512980
Show Author Affiliations
Frederic Madesta, Universitätsklinikum Hamburg-Eppendorf (Germany)
Tobias Gauer, Universitätsklinikum Hamburg-Eppendorf (Germany)
Thilo Sentker, Universitätsklinikum Hamburg-Eppendorf (Germany)
René Werner, Universitätsklinikum Hamburg-Eppendorf (Germany)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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