A Study on GPU-based Iterative ML-EM Reconstruction Algorithm for Emission Computed Tomographic Imaging Systems.
- Author:
Woo Seok HA
1
;
Soo Mee KIM
;
Min Jae PARK
;
Dong Soo LEE
;
Jae Sung LEE
Author Information
1. Department of Electrical Engineering, Seoul National University College of Engineering, Seoul, Korea.
- Publication Type:Original Article
- Keywords:
SPECT;
PET;
image reconstruction;
GPU;
CUDA
- MeSH:
Hand;
Image Processing, Computer-Assisted;
Memory;
Models, Statistical;
Tomography, Emission-Computed, Single-Photon
- From:Nuclear Medicine and Molecular Imaging
2009;43(5):459-467
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
PURPOSE: The maximum likelihood-expectation maximization (ML-EM) is the statistical reconstruction algorithm derived from probabilistic model of the emission and detection processes. Although the ML-EM has many advantages in accuracy and utility, the use of the ML-EM is limited due to the computational burden of iterating processing on a CPU (central processing unit). In this study, we developed a parallel computing technique on GPU (graphic processing unit) for ML-EM algorithm. MATERIALS AND METHODS: Using Geforce 9800 GTX+ graphic card and CUDA (compute unified device architecture) the projection and backprojection in ML-EM algorithm were parallelized by NVIDIA's technology. The time delay on computations for projection, errors between measured and estimated data and backprojection in an iteration were measured. Total time included the latency in data transmission between RAM and GPU memory. RESULTS: The total computation time of the CPU- and GPU-based ML-EM with 32 iterations were 3.83 and 0.26 sec, respectively. In this case, the computing speed was improved about 15 times on GPU. When the number of iterations increased into 1024, the CPU- and GPU-based computing took totally 18 min and 8 sec, respectively. The improvement was about 135 times and was caused by delay on CPU-based computing after certain iterations. On the other hand, the GPU-based computation provided very small variation on time delay per iteration due to use of shared memory. CONCLUSION: The GPU-based parallel computation for ML-EM improved significantly the computing speed and stability. The developed GPU-based ML-EM algorithm could be easily modified for some other imaging geometries.
