Enhancing signal detectability in learning-based CT reconstruction with
  a model observer inspired loss function
Megan Lantz Emil Y. Sidky Ingrid S. Reiser Xiaochuan Pan Gregory Ongie
Deep neural networks used for reconstructing sparse-view CT data are
typically trained by minimizing a pixel-wise mean-squared error or similar loss
function over a set of training images. However, networks trained with such
pixel-wise losses are prone to wipe out small, low-contrast features that are
critical for screening and diagnosis. To remedy this issue, we introduce a
novel training loss inspired by the model observer framework to enhance the
detectability of weak signals in the reconstructions. We evaluate our approach
on the reconstruction of synthetic sparse-view breast CT data, and demonstrate
an improvement in signal detectability with the proposed loss.