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

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

doi:10.1117/12.3008784

29 March 2024 Investigation of different model observers for including signal-detectability in the training of CNNs for CT image reconstruction

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

Author Affiliations +

Proceedings Volume 12929, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment; 129290E (2024) https://doi.org/10.1117/12.3008784
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Gregory Ongie, Megan Lantz, Emil Y. Sidky, Ingrid S. Reiser, and Xiaochuan Pan "Investigation of different model observers for including signal-detectability in the training of CNNs for CT image reconstruction", Proc. SPIE 12929, Medical Imaging 2024: Image Perception, Observer Performance, and Technology Assessment, 129290E (29 March 2024); https://doi.org/10.1117/12.3008784

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