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Found 5 papers, 2 papers with code
Phy-Diff: Physics-guided Hourglass Diffusion Model for Diffusion MRI Synthesis
Our model introduces the physical principles of dMRI in the noise evolution in the diffusion process and introduce a query-based conditional mapping within the difussion model.
End-to-End Model-based Deep Learning for Dual-Energy Computed Tomography Material Decomposition
Dual energy X-ray Computed Tomography (DECT) enables to automatically decompose materials in clinical images without the manual segmentation using the dependency of the X-ray linear attenuation with energy.
Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction
In this work, we present a novel self-supervised method for Low Dose Computed Tomography (LDCT) reconstruction.
Multi-Channel Convolutional Analysis Operator Learning for Dual-Energy CT Reconstruction
Extensive experiments with simulated and real computed tomography (CT) data were performed to validate the effectiveness of the proposed methods and we reported increased reconstruction accuracy compared to CAOL and iterative methods with single and joint total-variation (TV) regularization.
Regularization by Denoising Sub-sampled Newton Method for Spectral CT Multi-Material Decomposition
Spectral Computed Tomography (CT) is an emerging technology that enables to estimate the concentration of basis materials within a scanned object by exploiting different photon energy spectra.
