Effects of Iterative Reconstruction Algorithms on Computer-assisted Detection (CAD) Software for Lung Nodules in Ultra-low-dose CT for Lung Cancer Screening

Rationale and Objectives This study aimed to evaluate the effects of iterative reconstruction (IR) algorithms on computer-assisted detection (CAD) software for lung nodules in ultra-low-dose computed tomography (ULD-CT) for lung cancer screening. Materials and Methods We selected 85 subjects who underwent both a low-dose CT (LD-CT) scan and an additional ULD-CT scan in our lung cancer screening program for high-risk populations. The LD-CT scans were reconstructed with filtered back projection (FBP; LD-FBP). The ULD-CT scans were reconstructed with FBP (ULD-FBP), adaptive iterative dose reduction 3D (AIDR 3D; ULD-AIDR 3D), and forward projected model-based IR solution (FIRST; ULD-FIRST). CAD software for lung nodules was applied to each image dataset, and the performance of the CAD software was compared among the different IR algorithms. Results The mean volume CT dose indexes were 3.02 mGy (LD-CT) and 0.30 mGy (ULD-CT). For overall nodules, the sensitivities of CAD software at 3.0 false positives per case were 78.7% (LD-FBP), 9.3% (ULD-FBP), 69.4% (ULD-AIDR 3D), and 77.8% (ULD-FIRST). Statistical analysis showed that the sensitivities of ULD-AIDR 3D and ULD-FIRST were significantly higher than that of ULD-FBP ( P Conclusions The performance of CAD software in ULD-CT was improved by using IR algorithms. In particular, the performance of CAD in ULD-FIRST was almost equivalent to that in LD-FBP.