Abstract
Optimizing complex imaging procedures within Computed Tomography,
considering both dose and image quality, presents significant challenges amidst rapid
technological advancements and the adoption of Machine Learning (ML) methods.
A crucial metric in this context is the Difference-Detailed Curve, which relies on
human observer studies. However, these studies are labor-intensive and prone to
both inter- and intra-observer variability. To tackle these issues, a ML-based model
observer utilizing the U-Net architecture and a Bayesian methodology is proposed. In
order to train a model observer unaffected by the spatial arrangement of low-contrast
objects, the image preprocessing incorporates a Gaussian Process-based noise model.
Additionally, Gradient-weighted Class Activation Mapping is utilized to gain insights
into the model observer's decision-making process. By training on data from a
diverse group of observers, well-calibrated probabilistic predictions that quantify
observer variability are achieved. Leveraging the principles of Beta regression,
the Bayesian methodology is used to derive a model observer performance metric,
effectively gauging the model observer's strength in terms of an 'effective number of
observers'. Ultimately, this framework enables to predict the DDC distribution by
applying thresholds to the inferred probabilities.
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