High Performance MBIR (parallel-beam)
This is super fast code for parallel beam CT reconstruction on single node, multi-core CPUs. The main idea to achieve fast computations and convergence is super-voxels. Experiments show that super-voxels can accelerate MBIR CT reconstructions by more than 100 times, compared to the state-of-the-art implementations. In addition, this code implements a plug-and-play framework that allows users to use advanced prior models, such as a Non-Local Mean denoiser, and a convlutional neural network prior.
Here is the github repository for the super-voxel code:
Here is two demos that accompany the code. Users can try the demos for reconstruction:
If you do use the code and demos, please cite the following papers:
 Xiao Wang, Amit Sabne, Sherman Kisner, Anand Raghunathan, Charles Bouman, and Samuel Midkiff, "High Performance Model Based Image Reconstruction," 21st ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP '16), March 12-16, 2016.
 Xiao Wang, Amit Sabne, Putt Sakdhnagool, Sherman J. Kisner, Charles A. Bouman, and Samuel P. Midkiff, "Massively Parallel 3D Image Reconstruction," Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'17), November 13-16, 2017. (One of three finalists for 2017 ACM Gordon Bell Prize.)
Baseline MBIR Open Source
My collaborators and I developed a baseline Model Based Iterative Reconstruction (MBIR) software for parallel-beam CT reconstruction. The github link to the open-source code can be found at:
Feel free to use the code for education and research purpose. You are also welcome to contribute to the ongoing MBIR software by creating a new branch on github.
Cloud-Based Inversion Engine
Over the years, my fellow co-workers and I develped a cloud server system for the high performance CT Model-Based Iterative Reconstruction (MBIR). This cloud server, named the " Inversion Engine", is developed based on the Super-Voxel research idea. By using the Inversion Engine, users can upload their own dataset and get image reconstruction result in real-time. At the same time, users can treat the image reconstruction system as a black box and don't need to know how reconstruction algorithm works.
Check out the Inversion Engine website below:
For quetions about this inversion engine, please contact me or Sid Parida (firstname.lastname@example.org)