Self-consistent deep learning-based boosting of 4D cone-beam computed tomography reconstruction

Frederic Madesta; Tobias Gauer; Thilo Sentker; René Werner

doi:10.1117/12.2512980

15 March 2019 Self-consistent deep learning-based boosting of 4D cone-beam computed tomography reconstruction

Frederic Madesta, Tobias Gauer, Thilo Sentker, René Werner

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 1094902 (2019) https://doi.org/10.1117/12.2512980
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

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.

Citation Download Citation

Frederic Madesta, Tobias Gauer, Thilo Sentker, and René Werner "Self-consistent deep learning-based boosting of 4D cone-beam computed tomography reconstruction", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094902 (15 March 2019); https://doi.org/10.1117/12.2512980

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available