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Proceedings Paper

Assessing nodule detection on lung cancer screening CT: the effects of tube current modulation and model observer selection on detectability maps
Author(s): J. M. Hoffman; F. Noo; K. McMillan; S. Young; M. McNitt-Gray; Eloisa Rodriguez-Mena
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Paper Abstract

Lung cancer screening using low dose CT has been shown to reduce lung cancer related mortality and been approved for widespread use in the US. These scans keep radiation doses low while maximizing the detection of suspicious lung lesions. Tube current modulation (TCM) is one technique used to optimize dose, however limited work has been done to assess TCM’s effect on detection tasks. In this work the effect of TCM on detection is investigated throughout the lung utilizing several different model observers (MO). 131 lung nodules were simulated at 1mm intervals in each lung of the XCAT phantom. A Sensation 64 TCM profile was generated for the XCAT phantom and 2500 noise realizations were created using both TCM and a fixed TC. All nodules and noise realizations were reconstructed for a total of 262 (left and right lungs) nodule reconstructions and 10 000 XCAT lung reconstructions. Single-slice Hotelling (HO) and channelized Hotelling (CHO) observers, as well as a multislice CHO were used to assess area-under-the-curve (AUC) as a function of nodule location in both the fixed TC and TCM cases. As expected with fixed TC, nodule detectability was lowest through the shoulders and leveled off below mid-lung; with TCM, detectability was unexpectedly highest through the shoulders, dropping sharply near the mid-lung and then increasing into the abdomen. Trends were the same for all model observers. These results suggest that TCM could be further optimized for detection and that detectability maps present exciting new opportunities for TCM optimization on a patient-specific level.

Paper Details

Date Published: 24 March 2016
PDF: 6 pages
Proc. SPIE 9787, Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment, 97870Q (24 March 2016); doi: 10.1117/12.2217348
Show Author Affiliations
J. M. Hoffman, Univ. of California, Los Angeles (United States)
F. Noo, The Univ. of Utah (United States)
K. McMillan, Univ. of California, Los Angeles (United States)
S. Young, Univ. of California, Los Angeles (United States)
M. McNitt-Gray, Univ. of California, Los Angeles (United States)
Eloisa Rodriguez-Mena, Univ. of California, Los Angeles (United States)

Published in SPIE Proceedings Vol. 9787:
Medical Imaging 2016: Image Perception, Observer Performance, and Technology Assessment
Craig K. Abbey; Matthew A. Kupinski, Editor(s)

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