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17 October 2022 Evaluation of deep learning-based CT reconstruction with a signal-Laplacian model observer
Gregory Ongie, Emil Y. Sidky, Ingrid S. Reiser, Xiaochuan Pan
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Proceedings Volume 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography; 123040U (2022) https://doi.org/10.1117/12.2647039
Event: Seventh International Conference on Image Formation in X-Ray Computed Tomography (ICIFXCT 2022), 2022, Baltimore, United States
Abstract
Recent studies have proposed to optimize deep learning-based CT reconstruction methods for signal detectability performance. However, obtaining objective measures of signal detectability performance of the trained reconstruction networks is challenging due to the non-linear nature of the reconstruction. We propose a simple evaluation metric based on the model observer framework. The metric is based on the performance of a specific linear observer on signal-known-exactly/background-known-exactly task. The linear observer uses the signal Laplacian as a template, which we hypothesize is a better proxy for a human model observer than the ideal/Hotelling observer. We illustrate that the proposed metric can be used to select training hyper-parameters for a CNN-model used to reconstruct synthetic sparse-view breast CT data.
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Gregory Ongie, Emil Y. Sidky, Ingrid S. Reiser, and Xiaochuan Pan "Evaluation of deep learning-based CT reconstruction with a signal-Laplacian model observer", Proc. SPIE 12304, 7th International Conference on Image Formation in X-Ray Computed Tomography, 123040U (17 October 2022); https://doi.org/10.1117/12.2647039
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KEYWORDS
Signal detection
Interference (communication)
Signal to noise ratio
CT reconstruction
Smoothing
Performance modeling
Statistical modeling
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