Ultra-low-dose CT reconstructed with ASiR-V using SmartmA for pulmonary nodule detection and Lung-RADS...

Area of Science:

• Radiology
• Medical Imaging
• Oncology

Background:

• Lung cancer screening aims to detect nodules early using low-dose computed tomography (LDCT).
• Ultra-low-dose CT (ULDCT) offers reduced radiation exposure but requires validation for diagnostic accuracy.
• Advanced reconstruction algorithms like ASiR-V are crucial for maintaining image quality at lower doses.

Purpose of the Study:

• To assess the accuracy of ULDCT with ASiR-V and SmartmA for pulmonary nodule detection.
• To compare ULDCT's nodule detection and Lung-RADS classification performance against conventional LDCT.
• To evaluate the efficacy of ULDCT in lung cancer screening protocols.

Main Methods:

• A comparative study involving 210 patients undergoing both LDCT (0.80 mSv) and ULDCT (0.16 mSv).
• ULDCT utilized 120 kV/SmartmA with a noise index of 28 HU, reconstructed with ASiR-V 70%.
• Nodule characteristics, including type and diameter, were recorded, and Lung-RADS classifications were performed on both scan types.

Main Results:

• ULDCT achieved an overall sensitivity of 90.1% for nodule detection compared to LDCT.
• High sensitivity was observed for solid nodules (≥1 mm: 96.6%; ≥6 mm: 100%) and pure ground-glass nodules (≥6 mm: 93%).
• Good agreement was found in Lung-RADS classifications between LDCT and ULDCT, with nodule diameter and attenuation being key predictors.

Conclusions:

• ULDCT with ASiR-V and SmartmA is a viable option for lung cancer screening, providing a significantly reduced radiation dose (0.16 mSv).
• The technique demonstrates high sensitivity for detecting various nodule types and ensures reliable Lung-RADS classifications.
• ULDCT is suitable for screening individuals with a BMI ≤35 kg/m², balancing effective screening with minimized radiation exposure.