Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function
<|reference_start|>Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function: 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.<|reference_end|>
@article{lantz2024enhancing,
    title={Enhancing signal detectability in learning-based CT reconstruction with
  a model observer inspired loss function},
    author={Megan Lantz, Emil Y. Sidky, Ingrid S. Reiser, Xiaochuan Pan, Gregory
  Ongie},
    journal={arXiv preprint arXiv:2402.10010},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.10010},
    primaryClass={physics.med-ph cs.CV eess.IV}
}