Research On Algorithms Of GPU-Based 3D Medical Image Processing

Medical image processing and analyzing techniques not only greatly improve the clinic diagnosis level but also provide digital methods for medical training,teaching and researching, surgical navigation and so on,which becomes an important factor for promoting development of the modern medicine.Study on the medical image processing techniques is always the attention focus of many researchers at home and abroad,and this is very helpful to improve people's health condition and physical quality.In order to meet well the needs of practical application,simple 2D images are replaced by the intuitional 3D data that embodies rich information,which is the developing trend of medical image processing.However,nowadays 3D data processing on the common personal computer is very time consuming due to the large amount of data and the complexity of algorithms,which can not meet the real-time applications.So,how to improve further the performance efficiency of 3D medical data processing is a problem to be resolved urgently.In recent years,computer graphics processing unit(GPU) has been developing rapidly from initial graphics card used only for rendering to contemporary programmable parallel computing platform.Moreover,high-level programming languages such as Cg and HLSL make it easy to perform general-purpose computation expect for rendering such as solving partial differential equations and matrix operations by programming for GPU.Different with the serial computation model of CPU,GPU is a kind of parallelism stream processor with highly floating computing power.In many research areas such as physics simulations and signal analysis,researchers transfer the high intensity computation duties to GPU in a suitable style,and there is always an order of magnitude speedup through programming for GPU, which is also one of the hot topics for research.So,solving the problem of speed bottleneck of 3D medical image processing algorithms by programming for GPU and exploiting its highly processing power will be a good research topic with great application values.This dissertation firstly studies the architecture of GPU and the streaming programming model.Architecture of GPU is based on the graphics pipeline which provides programmable vertex processors and fragment processors that communicate with the main program through API such as OpenGL.GPU programs written by users are used as operation kernels which process simultaneously many elements of the data stream so as to realize highly parallel computing.However,according to the features of streaming parallel computing,operations of GPU programs on memory buffer are restricted.On this basis,the dissertation researches efficient algorithms of 3D medical image processing which include precise volume rendering method based on 3D texture mapping,mutual information based 3D image rigid registration on a CPU-GPU united platform,and GPU accelerated Katsevich cone-beam CT reconstruction algorithm for single circular arc trajectory.(1) 3D texture mapping based volume rendering methods can rapidly render the volume data,but slicing artifacts results are frequently observed in the rendered images because of low sampling rate.Piecewise ray integration is one of the effective methods to improve rendering quality,which applies volume rending integral equation firstly to each sampling segment and then blends all of them along the viewing ray.Color of each segment in the integral equation is always represented as a linear expression.However,the linear expression can not well represent color changes in each sampling segment because of complex correlation among human body tissues.A more precise parabola expression is presented in the dissertation for piecewise integration,and the volume rendering integral equation is further resolved so that it can be easily used in GPU shaders.The vertex shader calculates texture coordinates of three sampling positions of each sampling segment,and the fragment shader evaluates integral function on the sampling segment.Experimental results show that the proposed method can achieve excellent rendering images.On basis of the rendering method volumetric textures based clipping rendering algorithm is improved in order to decrease memory usage and increase clipping rendering speed.(2) An accelerating method of CPU-GPU united platform for mutual information based 3D medical image rigid registration is proposed,in which the optimization searching algorithm is performed on CPU because of its complex logical control and less intensity computation.In the process of optimization searching mutual information calculation function is evoked,which is divided into two steps,i.e.spatial transformation of floating image,and calculation of value of mutual information.Spatial transformation and tri-linear interpolation usually consume about 85%of the total mutual information calculation time and are suitable for parallel computation,so it can be performed on GPU.Tri-linear interpolation is performed on GPU by using flat 3D texture and render to texture techniques.The rendered results are transferred to main memory for further computation.Experimental results show that GPU accelerated method can achieve speedup about an order of magnitude with excellent registration results compared to the traditional software implementation on CPU.(3) The techniques of filtered backprojction algorithm performed on GPU are improved and used in the Katsevich cone-beam CT reconstruction algorithm for single circular arc trajectory.Katsevich method firstly filters the projection data,including partial derivative computation,Fourier transform,and Hilbert transform,which are performed on CPU.The filtered projection data are transferred to GPU memory for backprojection computation.The output of a given rendering pass is used as input in the next one by using multiple attachment points of frame buffer object(FBO),and then all the backprojection data gotten from different rotation angle are added on GPU.Floating texture mapping is used to guarantee excellent quality of reconstructed images.RGBA four channel textures are set to realize data-parallel computation.Quadtree structure is used to code the rectangular grid for accelerating rendering process.Compared with the traditional CPU applications,the proposed GPU accelerated method can achieve not only almost the same quality reconstruction images but also a speedup of more than ten times.Lastly,the dissertation summarize features of GPU programming method for the 3D medical image processing algorithms,which mainly include GPU memory mode,3D data structures,parallel strategies,and program optimization.