Airway quantification using adaptive statistical iterative reconstruction-V on wide-detector low-dose CT: a...

Area of Science:

• Radiology
• Pulmonary Imaging
• Image Reconstruction

Background:

• Low-dose CT (LDCT) reduces radiation exposure but often compromises image quality.
• Accurate airway quantification is crucial for diagnosing and monitoring lung diseases.

Purpose of the Study:

• To assess the accuracy of airway quantification using ASIR-V reconstruction on LDCT images.
• To evaluate the impact of varying ASIR-V levels on airway measurements in a human lung specimen.

Main Methods:

• Human lung specimen scanned at standard and low radiation doses (20-60 mAs).
• CT images reconstructed with varying ASIR-V levels (0-100%).
• Airway wall area percentage (%WA) and wall thickness (WT) were measured.

Main Results:

• LDCT significantly reduced radiation dose and image noise, while improving signal-to-noise ratio (SNR).
• ASIR-V reconstruction improved SNR and reduced noise across all dose levels.
• Airway measurements (%WA and WT) showed minimal variation (<2-3%) compared to standard dose, irrespective of ASIR-V level.

Conclusions:

• ASIR-V enhances image quality in LDCT scans.
• Airway quantification accuracy is maintained at low radiation doses with ASIR-V.
• Dose reduction and ASIR-V strength had minimal impact on airway measurement values.