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[Artificial intelligence evaluation of simulated phantom lung nodules with different pre-adaptive iteration
1Nation Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen 518116, China.
Zhonghua Yi Xue Za Zhi
|November 23, 2019
Summary
Increasing adaptive statistical iterative reconstruction-V (ASIR-V) weight in CT scans reduces radiation dose without compromising lung nodule detection rates. Artificial intelligence accurately measures nodules, maintaining performance at lower doses.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
Background:
- Pulmonary nodules require accurate detection and measurement for diagnosis.
- Wide-spectrum CT scanning offers detailed imaging but can involve significant radiation exposure.
- Iterative reconstruction techniques aim to reduce radiation dose while maintaining image quality.
Purpose of the Study:
- To evaluate the impact of varying adaptive statistical iterative reconstruction-V (ASIR-V) weights on radiation dose in wide-spectrum CT scanning.
- To assess the detection rate and measurement accuracy of pulmonary nodules using artificial intelligence (AI)-aided detection with different ASIR-V levels.
- To compare AI performance against physician detection rates.
Main Methods:
- A chest simulation phantom with 16 pulmonary nodules was scanned using Revolution CT with ASIR-V weights of 0%, 20%, 30%, 40%, and 50%.
- Radiation dose metrics (CTDIvol, DLP) were analyzed using Spearman correlation.
- Nodule detection and characterization were performed using Tuma Shenwei AI software, with intraclass correlation coefficients (ICCs) used for accuracy assessment.
Main Results:
- Increasing ASIR-V weight significantly reduced radiation dose (CTDIvol and DLP) with a linear negative correlation (r = -0.969, P < 0.01).
- AI demonstrated no significant difference in pulmonary nodule detection rates compared to physicians (P > 0.05).
- High intraclass correlation coefficients (0.981-1.000) confirmed the accuracy of AI in measuring nodule diameter, volume, CT value, and malignancy percentage.
Conclusions:
- Higher ASIR-V settings in wide-detector spectral chest CT scans lead to decreased radiation effective dose.
- AI-powered pulmonary nodule detection and evaluation performance remain robust even with reduced radiation doses achieved through ASIR-V reconstructions.
