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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.
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.
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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