Statistical image reconstruction for lesion detection

Detecting cancerous lesions is one major application in emission tomography. In this paper, we study statistical image reconstruction for this important clinical task. Compared to analytical reconstruction methods, statistical approaches can improve the image quality by accurately modeling the photon detection process and data noise. To explore the full potential of statistical reconstruction for lesion detection, we derived simplified theoretical expressions that allow fast evaluation of the detectability of a random lesion. The theoretical results are used to design the regularization parameters for the maximum lesion detectability. Results are validated using Monte Carlo simulations.