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Optimization of a chest computed tomography protocol for detecting pure ground glass opacity nodules: A feasibility study with a computer-assisted detection system and a lung cancer screening phantom

 Seongmin Kang  ;  Tae Hoon Kim  ;  Jae Min Shin  ;  Kyunghwa Han  ;  Ji Young Kim  ;  Baeggi Min  ;  Chul Hwan Park 
 PLOS ONE, Vol.15(5) : e0232688, 2020-05 
Journal Title
Issue Date
Adult ; Early Detection of Cancer ; Feasibility Studies ; Humans ; Image Interpretation, Computer-Assisted / methods ; Lung / diagnostic imaging* ; Lung Neoplasms / diagnostic imaging* ; Male ; Thorax / diagnostic imaging ; Tomography, X-Ray Computed / methods*
Objective: This study aimed to optimize computed tomography (CT) parameters for detecting ground glass opacity nodules (GGNs) using a computer-assisted detection (CAD) system and a lung cancer screening phantom. Methods: A lung cancer screening phantom containing 15 artificial GGNs (-630 Hounsfield unit [HU], 2-10 mm) in the left lung was examined with a CT scanner. Three tube voltages of 80, 100, and 120 kVp were used in combination with five tube currents of 25, 50, 100, 200, and 400 mA; additionally, three slice thicknesses of 0.625, 1.25, and 2.5 mm and four reconstruction algorithms of adaptive statistical iterative reconstruction (ASIR-V) of 30, 60, and 90% were used. For each protocol, accuracy of the CAD system was evaluated for nine target GGNs of 6, 8, or 10 mm in size. The cut-off size was set to 5 mm to minimize false positives. Results: Among the 180 combinations of tube voltage, tube current, slice thickness, and reconstruction algorithms, combination of 80 kVp, 200 mA, and 1.25-mm slice thickness with an ASIR-V of 90% had the best performance in the detection of GGNs with six true positives and no false positives. Other combinations had fewer than five true positives. In particular, any combinations with a 0.625-mm slice thickness had 0 true positive and at least one false positive result. Conclusion: Low-voltage chest CT with a thin slice thickness and a high iterative reconstruction algorithm improve the detection rate of GGNs with a CAD system in a phantom model, and may have potential in lung cancer screening.
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1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Tae Hoon(김태훈) ORCID logo https://orcid.org/0000-0003-3598-2529
Park, Chul Hwan(박철환) ORCID logo https://orcid.org/0000-0002-0004-9475
Shin, Jae Min(신재민)
Han, Kyung Hwa(한경화)
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