Learning aspects and potential pitfalls regarding detection of pulmonary nodules in chest tomosynthesis and proposed related quality criteria

Abstract

In chest tomosynthesis, low-dose projections collected over a limited angular range are used for reconstruction of an arbitrary number of section images of the chest, resulting in a moderately increased radiation dose compared to chest radiography. To investigate the effects of learning with feedback on the detection of pulmonary nodules for observers with varying experience of chest tomosynthesis, to identify pitfalls regarding detection of pulmonary nodules, and present suggestions for how to avoid them, and to adapt the European quality criteria for chest radiography and computed tomography (CT) to chest tomosynthesis. Six observers analyzed tomosynthesis cases for presence of nodules in a jackknife alternative free-response receiver-operating characteristics (JAFROC) study. CT was used as reference. The same tomosynthesis cases were analyzed before and after learning with feedback, which included a collective learning session. The difference in performance between the two readings was calculated using the JAFROC figure of merit as principal measure of detectability. Significant improvement in performance after learning with feedback was found only for observers inexperienced in tomosynthesis. At the collective learning session, localization of pleural and subpleural nodules or structures was identified as the main difficulty in analyzing tomosynthesis images. The results indicate that inexperienced observers can reach a high level of performance regarding nodule detection in tomosynthesis after learning with feedback and that the main problem with chest tomosynthesis is related to the limited depth resolution.