Description
Parallel Computing for Medical Imaging (PCMI)
Cyberinfrastructure-enabled, HPC-based parallel computing (parallel algorithm developments and implementations) for medical imaging. It includes (1) parallel algorithms for medical image reconstructions (parallel Fourier projection, parallel algebraic reconstruction, and parallel filered back-projection), (2) segmentation, (3) registration, (4) texturing and classification,(5) enhancement, etc. The current NIH sponsored research work are on the parallel reconstruction algorithms are Katsevich algorithm for XCT reconstruction, testing the deployment of one-sided communication technique, heterogeneous windows compute cluster system, parallel EM algorithms, distributed computing environment, local image reconstruction, and truncated Hilbert transform.
The current research topics:
- Parallel EM Algorithms for Medical Image Reconstruction in a Distributed Computing Environment (Tao He, Jun Ni, Ge Wang)
- Parallel Katsevich Algorithm for 3D-CT X-Ray Reconstruction (Junjun Deng, Hengyong Yu, Jun Ni, Lihe Wang, Ge Wang)
- Parallel Local Image Reconstruction (Junjun Deng, Hengyong Yu, Jun Ni, Lihe Wang, Ge Wang)
- Deployment of One-Sided Communication Technique for Parallel Computing in Katsevich 3D CT Image Reconstructions (Tao He, Jun Ni, Ge Wang)
- A Heterogeneous Windows Cluster System for Medical Image Reconstruction (Tao He, Jun Ni, Ge Wang)
- Parallel Algorithm and Implementation of the Truncated Hilbert Transform for image reconstruction (Ge Wang and Jun Ni)
- Parallel Algorithm and Implementation of large-scael Fourier Projection Recnstruction
- Parallel Algorithm and Implementation of Large-scale Algebraic Reconstruction
- Parallel Algorithm and Implementation of Large-scale Back-projection Reconstruction