Research Of 3D Cone-beam CT Image Reconstruction Accelerating Technology

CT technology applied in clinical medicine is an important symbol of medical technology progress in 20th century. Cone-beam CT has many advantages such as fast scanning, X-ray highly-usaged and image resolution isotropic. It is widely applied in medical diagnosis and industrial nondestructive testing fields, and has become one of the advanced reaserch subjects for the international CT field. However cone-beam CT 3D images reconstruction has great amount of computation and data transmission, so that image reconstruction is time-consuming. Only using CPU is impossible to meet the requirements of 3D image reconstruction in real-time. Therefore, improve the cone-beam CT reconstruction speed and find the right solution has important academic value and application prospect.Nowadays GPU (Graphic Processing Unit, GPU) has high level of parallelism, so this paper advances a method using GPU-based CUDA (Compute Unified Device Architecture, CUDA) technology to accelerate FDK reconstruction algorithm. There are two innovation points in this paper:on the one hand, a parallel FFT (Fast Fourier Transform, FFT) method with GPU to improve the image data filtering time is proposed; On the other hand, uses CUDA technology to realize FDK algorithm calculation, and according to the GPU hardware and memory characteristic puts forward the optimal methods.This paper firstly introduces physical and mathematical theory of CT imaging, and analyses parallel-beam projector reconstruction algorithm; Secondly, summarizes 2D fan-beam CT basic knowledge of reconstruction algorithms, and then focuses on analysing the 3D cone-beam image reconstruction algorithms, researching characteristics of FDK algorithm and its derivative algorithms; Thirdly, studies a new kind parallel FFT method to suit GPU computing, and uses CUDA technology to realize the method. Experimental results show that this method can achieve 46 times faster than the CPU-only method; Fourthly, this paper analyses the FDK 3D reconstruction algorithm parallel computing principle, proposes using GPU technology to accelerate FDK algorithm, designs methods respectively in weighting, filtering and backprojection stage of the FDK algorithm. Meanwhile, we employ kinds of CUDA memory to optimize both data transmission and memory access. The experiments show that the GPU method proposed by this paper is 150 times faster than the CPU method, the images of two methods are similar, and the error is less than 10(?).The CUDA technology makes the GPU programming more easily, it suitable for developers quickly grasp its programming method, and shorten the program development cycle. Considering the memory performance (data transmission and access) still influences execution speed of the algorithms greatly, the proposed parallel FFT and FDK reconstruction methods will have better effect if new kind of GPU can improve the efficiency of the memory performance. We may safely draw the conclusion that, along with CUDA architecture gradually maturing and GPU performance improvement, cone-beamCT 3D image reconstruction will be faster, and will be able to meet the requirement of real-time and accurate reconstruction.