Search Results for author:
Found 4 papers, 0 papers with code
A Global Constraint to Improve CT Reconstruction Under Non-Ideal Conditions
Conclusions: Different from most current constraints focusing on the local prior, our proposed constraint only utilizes the global prior of CT images.
Residual Back Projection With Untrained Neural Networks
In this paper, we present a framework for iterative reconstruction (IR) in CT that leverages the hierarchical structure of neural networks, without the need for training.
Gram filtering and sinogram interpolation for pixel-basis in parallel-beam X-ray CT reconstruction
The key aspect of parallel-beam X-ray CT is forward and back projection, but its computational burden continues to be an obstacle for applications.
A Convolutional Forward and Back-Projection Model for Fan-Beam Geometry
Iterative methods for tomographic image reconstruction have great potential for enabling high quality imaging from low-dose projection data.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
