31 March 2017 Evaluation of a projection-domain lung nodule insertion technique in thoracic computed tomography
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Abstract
Task-based assessment of computed tomography (CT) image quality requires a large number of cases with ground truth. Prospective case acquisition can be time-consuming. Inserting lesions into existing cases to simulate positive cases is a promising alternative. The aim was to evaluate a recently developed projection-based lesion insertion technique in thoracic CT. In total, 32 lung nodules of various attenuations were segmented from 21 patient cases, forward projected, inserted into projections, and reconstructed. Two experienced radiologists and two residents independently evaluated these nodules in two substudies. First, the 32 inserted and the 32 original nodules were presented in a randomized order and each received a 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. For the randomized evaluation, discrimination of real versus inserted nodules was poor with areas under the receiver operative characteristic curves being 0.57 [95% confidence interval (CI): 0.46 to 0.68], 0.69 (95% CI: 0.58 to 0.78), and 0.62 (95% CI: 0.54 to 0.69) for the two residents, two radiologists, and all four readers, respectively. Our projection-based lung nodule insertion technique provides a robust method to artificially generate positive cases that prove to be difficult to differentiate from real cases.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Chi Ma, Baiyu Chen, Lifeng Yu, Chi Wan Koo, Shuai Leng, Edwin A. Takahashi, Joel G. Fletcher, David L. Levin, Ronald S. Kuzo, Lyndsay D. Viers, Stephanie A. Vincent-Sheldon, Cynthia H. McCollough, "Evaluation of a projection-domain lung nodule insertion technique in thoracic computed tomography," Journal of Medical Imaging 4(1), 013510 (31 March 2017). https://doi.org/10.1117/1.JMI.4.1.013510 . Submission: Received: 3 August 2016; Accepted: 7 March 2017
Received: 3 August 2016; Accepted: 7 March 2017; Published: 31 March 2017
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