Share Email Print
cover

Proceedings Paper

Detector output prediction for CT detector array manufacturing
Author(s): H. Zuo; Y. Lu; D. Xiang; J. Zhang; J. Peplinski; M. Adamak
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The detector panel on a typical CT machine today is made of more than 500 detector boards, nicknamed chiclets. Each chiclet contains a number of detectors (i.e., pixels). In the manufacturing process, the chiclets on the panel need to go through an iterative test, swap, and test (TST) process, till some image quality level is achieved. Currently, this process is largely manual and can take hours to several days to complete. This is inefficient and the results can also be inconsistent. In this work, we investigate techniques that can be used to automate the iterative TST process. Specifically, we develop novel prediction techniques that can be used to simulate the iterative TST process. Our results indicate that deep neural networks produce significantly better results than linear regression in the more difficult prediction scenarios.

Paper Details

Date Published: 9 March 2018
PDF: 7 pages
Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105735Z (9 March 2018); doi: 10.1117/12.2293169
Show Author Affiliations
H. Zuo, Univ. of Wisconsin-Milwaukee (United States)
Y. Lu, Univ. of Wisconsin-Milwaukee (United States)
D. Xiang, Univ. of Wisconsin-Milwaukee (United States)
J. Zhang, Univ. of Wisconsin-Milwaukee (United States)
J. Peplinski, GE Healthcare (United States)
M. Adamak, GE Healthcare (United States)


Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

© SPIE. Terms of Use
Back to Top