Investigation of different model observers for including signal-detectability in the training of CNNs for CT image reconstruction

Recent studies have proposed methods to preserve and enhance signal-detection performance in learning-based CT reconstruction with CNNs. Prior work has focused on optimizing for ideal observer (IO) or Hotelling observer (HO) performance during training. However, the performance of the IO or HO may not correlate well with the performance of human observers on the same task. In this work, we explore modified training procedures to optimize for a variety of model observers, such as the signal-Laplacian and non-prewhitening model observer with eye filter, that we hypothesize are a better proxy for a human model observer than the IO or HO. We illustrate the proposed training approach on a CNN-model used to reconstruct synthetic sparse-view breast CT data. Our results indicate that the proposed modified training allows one to preserve weak signals in the reconstructions while changing the overall noise characteristics in a way that may be beneficial to human observers.