Multi-angle Plane-wave Beamforming Algorithm Based on CUDA / 中国医疗器械杂志

Multi-angle plane-wave beamforming algorithm is the basis of ultra-fast ultrasonic imaging. It can be used to improve the imaging frame rate and resolution of traditional focused ultrasound. However, the existing multi-angle plane-wave technology can not satisfy the real-time imaging requirements due to the huge amount of computation required by CPU. In this paper, We proposed a parallel processing method to reduce the computation time based on compute unified device architecture(CUDA). Simulation analysis and contrast experiment were conducted to verify its performance. Experimental results show that the execution time based on GPU is much less than that based on CPU, thus the computational speed is accelerated significantly to satisfy the demand of ultrafast imaging.

Fast 3D Medical Image Segmentation Based on CUDA / 中国医学物理学杂志

Objective: 3D segmentation is an important part of medical image analysis and visualization. It also continues to be large challenge in the medical image segmentation. While level sets have demonstrated a great potential for 3D medical image segmentation, these algorithms have a large computational burden thus are not suitable for real time processing requirement. To solve this problem, we propose a parallel accerelated method based on CUDA. Methods: We implement C-V level set algorithm in the CUDA environment which is the NVIDIA's GPGPU model.The segmentation speed can greatly improved by using independence of image pixel and concurrence of partial differential equation .The paper shows the flow chart of the parallel computing and gives the detailed introduction of the C-V level set algorithm which is implemented in the CUDA environment. Results: Realizing the C-V level set parallel accerelated algorithm. This method has faster segmentation speed while preserving the qualitative results, Conclusions: This method is viable and makes the fast 3D medical image segmentation come hue.

2D, 3D Rigid Registration Algorithm Base on CUDA / 中国医学物理学杂志

Objective: Real time medical image registration technique is one of the key techniques in image based surgery navi-gation system. While in medical image analysis, image registration is usually a very time-cousuming operation, and this is not conducive to clinical real-time requirements. This paper studies and realizes the acceleration of the process of image registra-tion. Methods: In order to improve the regisWation rate, in this paper, we propose a new technology which is based on CUDA (Compute Unified Device Architecture) programming model to accelerate the process of registration in hardware, using paral-lel methods to achieve pixel coordinate transformation, linear interpolation, and calculate the corresponding pixel gray value residuals. Results: The registration is up to the sub-pixel level and the GPU-based registration is dozens or even hundreds of times faster than CPU-based registration. Conclusions: This method greatly enhances the speed of rigid registration without changing the alignment accuracy.

2D/3D registration system based on single X ray image and CT data / 医用生物力学

Objective To build a 2D/3D registration system based on the compute unified device architecture(CUDA) frame with single X-ray image and CT data of knee joints and apply it in the research of knee motion and stability of implanted prosthesis. Method The digital radiography(DR) equipment used in the study was calibrated by the Zhang zhengyou Calibration Method, and then digitally rendered radiographs(DRR) images were generated in the CUDA frame with light tracing algorithm, and the best 2D/3D registration parameters were calculated with a similarity operator of cross correlation; finally, the results were evaluated by using the method of 3D/3D registration with data obtained from a 3D laser scanner. Results With knee specimen X-ray images and CT data, in 6 degrees of freedom, the average errors of transform were below 1 mm, and those of rotation were below 1°. Conclusions The 2D/3D registration system can meet the precision requirement of motion detection and be used to study the knee motion and prosthesis location.

A Study on GPU-based Iterative ML-EM Reconstruction Algorithm for Emission Computed Tomographic Imaging Systems / 핵의학분자영상

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.