Share Email Print
cover

Proceedings Paper

Evaluation of a projection-domain lung nodule insertion technique in thoracic CT
Author(s): Chi Ma; Baiyu Chen; Chi Wan Koo; Edwin A. Takahashi; Joel G. Fletcher; Cynthia H. McCollough; David L. Levin; Ronald S. Kuzo; Lyndsay D. Viers; Stephanie A. Vincent Sheldon; Shuai Leng; Lifeng Yu
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

Task-based assessment of computed tomography (CT) image quality requires a large number of cases with ground truth. Inserting lesions into existing cases to simulate positive cases is a promising alternative approach. The aim of this study was to evaluate a recently-developed raw-data based lesion insertion technique in thoracic CT. Lung lesions were segmented from patient CT images, forward projected, and reinserted into the same patient CT projection data. In total, 32 nodules of various attenuations were segmented from 21 CT cases. Two experienced radiologists and 2 residents blinded to the process independently evaluated these inserted nodules in two sub-studies. First, the 32 inserted and the 32 original nodules were presented in a randomized order and each received a rating score from 1 to 10 (1=absolutely artificial to 10=absolutely realistic). Second, the inserted and the corresponding original lesions were presented side-by-side to each reader, who identified the inserted lesion and provided a confidence score (1=no confidence to 5=completely certain). For the randomized evaluation, discrimination of real versus artificial nodules was poor with areas under the receiver operative characteristic curves being 0.69 (95% CI: 0.58-0.78), 0.57 (95% CI: 0.46-0.68), and 0.62 (95% CI: 0.54-0.69) for the 2 radiologists, 2 residents, and all 4 readers, respectively. For the side-by-side evaluation, although all 4 readers correctly identified inserted lesions in 103/128 pairs, the confidence score was moderate (2.6). Our projection-domain based lung nodule insertion technique provides a robust method to artificially generate clinical cases that prove to be difficult to differentiate from real cases.

Paper Details

Date Published: 4 April 2016
PDF: 6 pages
Proc. SPIE 9783, Medical Imaging 2016: Physics of Medical Imaging, 97835Y (4 April 2016); doi: 10.1117/12.2217009
Show Author Affiliations
Chi Ma, Mayo Clinic (United States)
Baiyu Chen, Mayo Clinic (United States)
Chi Wan Koo, Mayo Clinic (United States)
Edwin A. Takahashi, Mayo Clinic (United States)
Joel G. Fletcher, Mayo Clinic (United States)
Cynthia H. McCollough, Mayo Clinic (United States)
David L. Levin, Mayo Clinic (United States)
Ronald S. Kuzo, Mayo Clinic (United States)
Lyndsay D. Viers, Mayo Clinic (United States)
Stephanie A. Vincent Sheldon, Mayo Clinic (United States)
Shuai Leng, Mayo Clinic (United States)
Lifeng Yu, Mayo Clinic (United States)


Published in SPIE Proceedings Vol. 9783:
Medical Imaging 2016: Physics of Medical Imaging
Despina Kontos; Thomas G. Flohr, Editor(s)

© SPIE. Terms of Use
Back to Top