Effect of CT reconstruction settings on the performance of a deep learning based lung nodule CAD system.

Purpose: To study the effect of different reconstruction parameter settings on the performance of a commercially available deep learning based pulmonary nodule CAD system.

Materials And Methods: We performed a retrospective analysis of 24 chest CT scans, reconstructed at 16 different reconstruction settings for two different iterative reconstruction algorithms (SAFIRE and ADMIRE) varying in slice thickness, kernel size and iterative reconstruction level strength using a commercially available deep learning pulmonary nodule CAD system. The DL-CAD software was evaluated at 25 different sensitivity threshold settings and nodules detected by the DL-CAD software were matched against a reference standard based on the consensus reading of three radiologists.