指導教授：周呈霙臺灣大學：生物產業機電工程學研究所周宏屹Chou, Hung-YiHung-YiChou2014-11-262018-07-102014-11-262018-07-102014http://ntur.lib.ntu.edu.tw//handle/246246/261777Dual-head small-animal positron emission tomography (DHAPET) has characteristics of high detection sensitivity and flexible system configurations. However, the systemgeometry may result in severe parallax errors and undetected events along the trans-axial directions, thus reducing the image quality.With the advancement of detector hardware, time-of-flight (TOF) of photons can be detected, which has demonstrated significant improvement in image quality, and can greatly benefit its use in research studies and clinics. Algorithms like total variation (TV) minimization methods that aim to compensate the effects of data truncation can be employed to reconstruct images with better resolution and quality. In this work, we will numerically compute the DHAPET system response matrix that includes TOF information and propose a TOF-PET reconstructionalgorithm based on TV-minimization. By applying both TOF information and TV minimization in the iterative algorithm, the reconstructed images can be anticipated to have improved quality with better spatial resolution.誌謝 Abstract TABLE OF CONTENTS i LIST OF FIGURES iii LIST OF TABLES iv Chapter 1 1 1.1 Background 1 1.2 Frameworks 5 CHAPTER 2 6 2.1 TOF PET and Small Animal PET 6 2.1.1 Positron Emission Tomography 6 2.1.2 Dual-Head Flat-Panel Small Animal PET System 7 2.1.3 Time-of-Flight PET System 9 2.2 Simulation and Reconstruction Algorithms 12 2.2.1 GATE: A Simulation Tool for PET 12 2.2.2 Numerical Methods for PET Image Reconstruction 14 2.2.3 Monte Carlo Simulation 15 2.2.4 Voxel-Based Ray-Tracing Simulation 16 2.2.5 Optimization Algorithm 18 2.2.6 Total Variation Minimization Algorithm 19 Chapter 3 21 3.1 Simulation Flowchart 21 3.2 Instruments 22 3.2.1 System Configuration 22 3.3 Research Methods 23 3.3.1 System Property 23 3.3.2 System Response Matrix 25 3.3.3 Ray-tracing Simulation of TOF-based System Matrix 25 3.3.3 The implement of GPU acceleration 30 3.3.4 Total Variation Algorithm 31 3.3.5 Fast Iterative Shrinkage-Thresholding (FISTA) Algorithm 32 3.3.5 Burring Operator and FISTA De-blurring Model 35 3.3.6 Non-local Means-FISTA (NLM-FISTA) Algorithm 36 CHAPTER 4 38 4.1 Simulation Efficiency of System Response Matrix 38 4.2 Performance of Time-of-Flight 39 4.3 Performance of TV/NLM minimization 43 4.3.1 The performance of TV/NLM- FISTA denoising model 44 4.3.2The performance of TV/NLM FISTA deblurring model 46 CHAPTER 5 49 5.1 Research Summary 49 Reference 501258523 bytesapplication/pdf論文公開時間：2014/03/09論文使用權限：同意有償授權(權利金給回饋學校)正子斷層掃描時間飛行信息全域變異數最小化thesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/261777/1/ntu-103-R00631040-1.pdf