Performance of MAP Reconstruction for Hot Lesion Detection in Whole-Body PET/CT: An Evaluation With Human and Numerical Observers

Abstract

For positron emission tomography (PET) imaging, different reconstruction methods can be applied, including maximum likelihood (ML ) and maximum a posteriori (MAP) reconstruction. Postsmoothed ML images have approximately position and object independent spatial resolution, which is advantageous for (semi-) quantitative analysis. However, the complex object dependent smoothing obtained with MAP might yield improved noise characteristics, beneficial for lesion detection. In this contribution, MAP and postsmoothed ML are compared for hot spot detection by human observers and by the channelized Hotelling observer (CHO). The study design was based on the "multiple alternative forced choice" approach. For the MAP reconstruction, the relative difference prior was used. For postsmoothed ML, a Gaussian smoothing kernel was used. Both the human observers and the CHO performed slightly better on MAP images than on postsmoothed ML images. The average CHO performance was similar to the best human performance. The CHO was then applied to evaluate the performance of priors with reduced penalty for large differences. For these priors, a poorer detection performance was obtained.