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Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function

15 Feb 2024arXiv:2402.10010archive 2025-07-28

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.

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