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This is the code for the MICCAI 2021 paper Self-Supervised Vessel Enhancement Using Flow-Based Consistencies.
📹 A more flexible framework that can generate videos at any resolution and creates videos from images.
C/C++ library and Python bindings for x-ray cross sections, x-ray source spectra modeling, multi-material beam hardening correction, and dual energy decomposition algorithms
python implementation of normalized metal artifact reduction
Convolutional neural network based metal artifact reduction (CNN-MAR) in x-ray computed tomography
Extension for 3D Slicer that allows the application to be used from Jupyter notebook
Set of tools to help developers when creating new Slicer extensions
A python code for multi-class semantic segmentation for 3D reconstruction of 2D DSA images using CNN
Sharpen your low-resolution pictures with the power of AI upscaling
Cross-platform C++ library providing a simple API to read and write INI-style configuration files
CT Recon in Python: An all-in-one tool for Data IO, Pre/Post-process, Physics, Dual Energy, Low Dose and everything.
Fixed some common artifacts in CT images.
MATLAB toolbox for grazing-incidence X-ray scattering data visualization and reduction, and indexing of buried three-dimensional periodic nanostructured films
Code for our IJCV paper "HumanLiff: Layer-wise 3D Human Generation with Diffusion Model"
A GUI software to remove stripe or ring artifact in medical images
Matlab toolbox for polyenergetic quantitative (polyquant) X-ray CT reconstruction with demos.
One click to access some specified websites safely.
ITK filters to reduce ring artifacts common in x-ray computed tomography.
Master Thesis project to reduce artefacts in Cone-Bean CT using deep learning
A prototype an algorithm to calibrate the CBCT geometry
common method to enhance some cbct image
"Neural Attenuation Fields for Sparse-View CBCT Reconstruction" (MICCAI 2022 Oral)
Numerical integration for "Delta(1232) contribution to real photon radiative corrections for elastic electron-proton scattering" (http://arxiv.org/abs/1604.07960)