Real-time robot motion planning using rasterizing computer graphics hardware
SIGGRAPH '90 Proceedings of the 17th annual conference on Computer graphics and interactive techniques
An approach to 2D/3D registration of a vertebra in 2D X-ray fluoroscopies with 3D CT images
CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
VMV '01 Proceedings of the Vision Modeling and Visualization Conference 2001
Linear algebra operators for GPU implementation of numerical algorithms
ACM SIGGRAPH 2003 Papers
Iterative x-ray/ct registration using accelerated volume rendering
Iterative x-ray/ct registration using accelerated volume rendering
Projection-based image registration in the presence of fixed-pattern noise
IEEE Transactions on Image Processing
3-D brain MRI tissue classification on FPGAs
IEEE Transactions on Image Processing
Extended global optimization strategy for rigid 2D/3D image registration
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Computer Methods and Programs in Biomedicine
Accelerating 3D nonrigid registration using the cell broadband engine processor
IBM Journal of Research and Development
CUDA optimization strategies for compute- and memory-bound neuroimaging algorithms
Computer Methods and Programs in Biomedicine
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In this paper, we propose a computationally efficient method for medical image registration. The centerpiece of the approach is to reduce the dimensions of each image via a projection operation. The two sequences of projection images corresponding to each image are used for estimating the registration parameters. Depending upon how the projection geometry is set up, the lower dimension registration problem can be parameterized and solved for a subset of parameters from the original problem. Computation of similarity metrics on the lower dimension projection images is significantly less complex than on the original volumetric images. Furthermore, depending on the type of projection operator used, one can achieve a better signal to noise ratio for the projection images than the original images. In order to further accelerate the process, we use Graphic Processing Units (GPUs) for generating projections of the volumetric data. We also perform the similarity computation on the graphics board, using a GPU with a programmable rendering pipeline. By doing that, we avoid transferring a large amount of data from graphics memory to system memory for computation. Furthermore, the performance of the more complex algorithms exploiting the graphics processor's capabilities is greatly improved. We evaluate the performance and the speed of the proposed projection based registration approach using various similarity measures and benchmark them against an SSE-accelerated CPU based implementation.