Automatic lung nodule detection in thick slice CT: a comparative study of different gating schemes in CAD

Common lung nodule detection workflows use 5mm slice thickness protocol whereas existing CAD systems require ?2mm data. A major challenge for widespread Lung CAD clinical use is thick and thin reconstruction availability for radiologist and CAD respectively. This is not always possible and applying current CAD algorithms on thick data outside their designed acquisition parameters may result in sensitivity degradation and high false-positives, hence clinically unacceptable. We propose a multi-stage classifier CAD system which works directly on thick scans. Exploring gating systems using wall-attachment and lesion location, we show significant improvement of CAD sensitivity at much better false positive rates.