CONFERENCE PROCEEDINGS
Advanced Search >
Home > Proceedings > Volume 11072 > Article
Translator Disclaimer
Paper
28 May 2019 Reconstructing interior transmission tomographic images with an offset-detector using a deep-neural-network
Hoyeon Lee, Hyeongseok Kim, Seungryong Cho
Author Affiliations +
Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 1107230 (2019) https://doi.org/10.1117/12.2534888
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States
Abstract
Interior tomography that acquires truncated data of a specific interior region-of-interest (ROI) is an attractive option to low-dose imaging. However, image reconstruction from such measurement does not yield an accurate solution because of data insufficiency. There have been developed a host of approaches to getting an approximate useful solution including various weighting methods, iterative reconstruction methods, and methods with prior knowledge. In this study, we use a deep-neural-network, which has shown its potentials in various fields including medical imaging, to reconstruct interior tomographic images. We assumed an offset-detector geometry which has wide applications in cone-beam CT (CBCT) imaging for its extended field-of-view (FOV) in this work. We trained a network to synthesize ‘ramp-filtered’ data within the detector active area so that the corresponding ROI reconstruction would be truncation-artifact-free in the filteredbackprojection (FBP) reconstruction framework. We have compared the results with post- and pre-convolution weighting methods and shown outperformance of the neural network approach.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Hoyeon Lee, Hyeongseok Kim, and Seungryong Cho "Reconstructing interior transmission tomographic images with an offset-detector using a deep-neural-network", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107230 (28 May 2019); https://doi.org/10.1117/12.2534888
ACCESS THE FULL ARTICLE
PERSONAL SIGN IN
Full access may be available with your subscription
Forgot your username?
Forgot your password?
No SPIE account? Create an account
Institutional Access:
Sign in with your institutional credentials
PURCHASE THIS CONTENT
INTERESTED IN A FREE CORPORATE TRIAL?
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
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 5 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
SHARE
GET CITATION
< Previous Article
|
Next Article >
Advertisement
Advertisement
KEYWORDS
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top