Evaluation of a projection-domain lung nodule insertion technique in thoracic CT.
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.
Department of Radiology, Mayo Clinic, Rochester, MN.
Chi
C
Ma
Department of Radiology, Mayo Clinic, Rochester, MN.
Baiyu
B
Chen
Department of Radiology, Mayo Clinic, Rochester, MN.
Chi Wan
CW
Koo
Department of Radiology, Mayo Clinic, Rochester, MN.
Edwin A
EA
Takahashi
Department of Radiology, Mayo Clinic, Rochester, MN.
Joel G
JG
Fletcher
Department of Radiology, Mayo Clinic, Rochester, MN.
Cynthia H
CH
McCollough
Department of Radiology, Mayo Clinic, Rochester, MN.
David L
DL
Levin
Department of Radiology, Mayo Clinic, Rochester, MN.
Ronald S
RS
Kuzo
Department of Radiology, Mayo Clinic, Rochester, MN.
Lyndsay D
LD
Viers
Department of Radiology, Mayo Clinic, Rochester, MN.
Stephanie A Vincent
SA
Sheldon
Department of Radiology, Mayo Clinic, Rochester, MN.
Shuai
S
Leng
Department of Radiology, Mayo Clinic, Rochester, MN.
Lifeng
L
Yu
Image quality assessment; lesion insertion; lesion simulation; lung nodules; observer study; radiation dose reduction; thoracic CT
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