Effect of Scan Angle and Reconstruction Algorithm on Model Observer Performance in Tomosynthesis

Abstract

The goal of this work is to develop task-based figures of merit for the assessment of tomosynthesis imaging. Towards this aim, we have computed performance of a prewhitening model observer for a SKE detection task in uniform background, in the in-focus plane of a the signal in the reconstructed tomosynthesis volume, as well as signal contrast. Test images were computer generated and reconstructed using both iterative ML-EM and filtered backprojection. We found that in noiseless images, signal contrast was substantially higher for the FBP reconstruction. For ML-EM reconstruction, model observer performance decreased with iteration number. At 10 iterations, performance was similar to that of FBP reconstruction. We found an increase of model observer performance with scan angle in the ML-EM reconstructed images. FBP showed a slight decrease of observer performance with scan angle. The results of this study are limited by the task, which is not realistic because backgrounds do not contain structure noise. This will be addressed in the future.