Adaptive SPECT for Tumor Necrosis Detection

In this paper, we consider a prototype of an adaptive SPECT system, and we use simulation to objectively assess the system's performance with respect to a conventional, non-adaptive SPECT system. Objective performance assessment is investigated for a clinically relevant task: the detection of tumor necrosis at a known location and in a random lumpy background. The iterative maximum-likelihood expectation-maximization (MLEM) algorithm is used to perform image reconstruction. We carried out human observer studies on the reconstructed images and compared the probability of correct detection when the data are generated with the adaptive system as opposed to the non-adaptive system. Task performance is also assessed by using a channelized Hotelling observer, and the area under the receiver operating characteristic curve is the figure of merit for the detection task. Our results show a large performance improvement of adaptive systems versus non-adaptive systems and motivate further research in adaptive medical imaging.