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Design of Novel Loss Functions for Deep Learning in X-ray CT
Particularly in a field such as X-ray CT, where radiologists' subjective preferences in image characteristics are key to acceptance, it may be desirable to penalize differences in DL more creatively.
Statistically Adaptive Filtering for Low Signal Correction in X-ray Computed Tomography
But low dose comes with a cost of low signal artifacts such as streaks and low frequency bias in the reconstruction.
MBIR Training for a 2.5D DL network in X-ray CT
The cost we have to pay is that MBIR is computationally expensive.
A Gaussian Mixture MRF for Model-Based Iterative Reconstruction with Applications to Low-Dose X-ray CT
In this paper, we present a novel Gaussian mixture Markov random field model (GM-MRF) that can be used as a very expressive prior model for inverse problems such as denoising and reconstruction.
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