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On the efficiency of iterative ordered subset reconstruction algorithms for acceleration on GPUs.
1Center for Visual Computing, Computer Science Department, Stony Brook University, NY 11794-4400, United States.
Computer Methods and Programs in Biomedicine
|October 24, 2009
Summary
Ordered subsets algorithms like OS-EM and SART accelerate computed tomography reconstruction. However, GPU architecture impacts performance, necessitating new optimization strategies for GPU-accelerated iterative methods.
Area of Science:
- Medical Imaging
- Computational Science
- Image Reconstruction
Background:
- Iterative algorithms like Expectation Maximization (EM) and Simultaneous Iterative Reconstruction Technique (SIRT) are crucial for computed tomography (CT) reconstruction, especially with noisy or limited data.
- Ordered subsets methods (OS-EM, SART) enhance convergence rates by processing data in subsets.
- Graphics Processing Units (GPUs) offer computational acceleration for these demanding algorithms.
Purpose of the Study:
- To investigate the impact of GPU architecture on the performance of ordered subsets iterative reconstruction algorithms.
- To identify optimal parameters for GPU-accelerated iterative algebraic reconstruction methods.
- To introduce and evaluate Ordered Subsets SIRT for optimized computational performance on GPUs.
Main Methods:
- Implementation and analysis of Ordered Subsets EM (OS-EM) and Simultaneous Algebraic Reconstruction Technique (SART) on GPUs.
- Investigating the relationship between subset size, relaxation factor, and wall-clock time on GPU architectures.
- Generalizing SIRT to Ordered Subsets SIRT (OS-SIRT) for GPU acceleration.
Main Results:
- GPU architecture imposes constraints that can counteract the benefits of smaller subsets observed on CPUs for ordered subsets algorithms.
- New relationships were identified for optimal subset numbers and relaxation factors to minimize reconstruction time on GPUs.
- Ordered Subsets SIRT demonstrated optimized computational performance for GPU-accelerated iterative reconstruction.
Conclusions:
- GPU-specific architectural constraints require tailored optimization strategies for ordered subsets iterative reconstruction algorithms.
- The choice of subset number and relaxation factor is critical for achieving optimal GPU performance and is application-dependent.
- Ordered Subsets SIRT provides an effective approach to enhance the computational efficiency of GPU-accelerated iterative CT reconstruction.
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