Fast 3D Deformable Image Registration on a GPU Computing Platform - Mousazadeh, Mohammad Hamed (My University, 2011)
Image registration has become an indispensable tool in medical diagnosis and intervention. The increasing need for speed and accuracy in clinical applications have motivated researchers to focus on developing fast and reliable registration algorithms. In particular, advanced deformable registration routines are emerging for medical applications involving soft-tissue organs such as brain, breast, kidney, liver, prostate, etc. Computational complexity of such algorithms are significantly higher than those of conventional rigid and affine methods, leading to substantial increases in execution time. In this thesis, we present a parallel implementation of a newly developed deformable image registration algorithm by Marami et al. [1] using the Computer Unified Device Architecture (CUDA). The focus of this study is on acceleration of the computations on a Graphics Processing Unit (GPU) to reduce the execution time to nearly real-time for diagnostic and interventional applications. The algorithm co-registers preoperative and intraoperative 3-dimensional magnetic resonance (MR) images of a deforming organ. It employs a linear elastic dynamic finite-element model of the deformation and distance measures such as mutual information and sum of squared difference to align volumetric image data sets. In this study, we report a parallel implementation of the algorithm for 3D-3D MR registration based on SSD on a CUDA capable NVIDIA GTX 480 GPU. Computationally expensive tasks such as interpolation, displacement and force calculation are significantly accelerated using the GPU. The result of the experiments carried out with a realistic breast phantom tissue shows a 37-fold speedup for the GPUbased implementation compared with an optimized CPU-based implementation in high resolution MR image registration. The CPU is a 3.20 GHz Intel core i5 650 processor with 4GB RAM that also hosts the GTX 480 GPU. This GPU has 15 streaming multiprocessors, each with 32 streaming processors, i.e. a total of 480 cores. The GPU implementation registers 3D-3D high resolution (512×512×136) image sets in just over 2 seconds, compared to 1.38 and 23.25 minutes for CPU and MATLAB-based implementations, respectively. Most GPU kernels which are employed in 3D-3D registration algorithm also can be employed to accelerate the 2D-3D registration algorithm in [1].
Keywords: GPU; Parallel Implementation; Image Registration; CUDA; Computer Engineering; Computer Engineering