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Evaluation of a projection-domain lung nodule insertion technique in thoracic computed tomography.

Authors
  • Ma, Chi1
  • Yu, Lifeng1
  • Chen, Baiyu1
  • Koo, Chi Wan1
  • Takahashi, Edwin A1
  • Fletcher, Joel G1
  • Levin, David L1
  • Kuzo, Ronald S1
  • Viers, Lyndsay D1
  • Vincent-Sheldon, Stephanie A1
  • Leng, Shuai1
  • McCollough, Cynthia H1
  • 1 Mayo Clinic , Department of Radiology, Rochester, Minnesota, United States. , (United States)
Type
Published Article
Journal
Journal of Medical Imaging
Publisher
SPIE - International Society for Optical Engineering
Publication Date
Jan 01, 2017
Volume
4
Issue
1
Pages
13510–13510
Identifiers
DOI: 10.1117/1.JMI.4.1.013510
PMID: 28401176
Source
Medline
Keywords
Language
English
License
Unknown

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.

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