Optimizing model observer performance in learning-based CT reconstruction

Gregory Ongie; Emil Y. Sidky; Ingrid S. Reiser; Xiaochuan Pan

doi:10.1117/12.2613050

4 April 2022 Optimizing model observer performance in learning-based CT reconstruction

Gregory Ongie, Emil Y. Sidky, Ingrid S. Reiser, Xiaochuan Pan

Proceedings Volume 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment; 120350A (2022) https://doi.org/10.1117/12.2613050
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

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 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.

Conference Presentation

Citation Download Citation

Gregory Ongie, Emil Y. Sidky, Ingrid S. Reiser, and Xiaochuan Pan "Optimizing model observer performance in learning-based CT reconstruction", Proc. SPIE 12035, Medical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment, 120350A (4 April 2022); https://doi.org/10.1117/12.2613050

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