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Found 3 papers, 2 papers with code
Enhancing signal detectability in learning-based CT reconstruction with a model observer inspired loss function
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.
Solution existence, uniqueness, and stability of discrete basis sinograms in multispectral CT
In this work, we consider a {\it discrete} form of the nonlinear system in step (1), and then carry out theoretical and numerical analyses of conditions on the existence, uniqueness, and stability of a solution to the discrete nonlinear system for accurately estimating the discrete basis sinograms, leading to quantitative reconstruction of VMIs in MSCT.
How do we generalize?
Here, as the first step, we introduce some of these candidate mechanisms and we discuss the issues currently hindering better synthesis of generalization research.
