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研究生(外文):Chang-Han Huang
論文名稱(外文):Iterative reconstruction with an edge-preserving prior forPET
指導教授(外文):Cheng-Ying Chou
口試委員(外文):Ching-Han HsuIng-Tsung Hsiao
外文關鍵詞:PETimage reconstructionimage registration
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在本研究中,我們使用OpenGATE軟體來模擬正子斷層掃描系統,並且比較不同正子斷層影像重建方法的效果。在重建的過程中,我們會使用Median root prior 方法計算影像資訊,並且運用全變異最小化方法(Total variation minimization algorithm)搭配Proximal Splitting方法做影像重建,希望藉由此方法能夠提高正子斷層影像的影像品質及解析度。

Positron emission tomography (PET) is a nuclear medicine technique that can help the doctor to diagnose the disease such as tumor, cancer, or brain disease. However, there are still some disadvantages in PET system. For example, comparing with the different medical imaging modalities, the spatial resolution of PET is relatively poor. If we can improve the image quality, PET can provide more image information for doctors diagnosing disease. In this work, we applied the OpenGATE package to simulate the PET system and compared the efficacy of different reconstruction algorithms. We took advantages of the edge-preserving prior for PET image reconstruction to improve PET image quality. In this work we also employed the medical image data from National Taiwan University Hospital. We developed a multi-modality imaging platform to register functional and anatomical images. This allowed for a quantitative and objective diagnosis of Alzheimer’s disease (AD). AD is an irreversible, progressive brain disease that slowly destroys memory and thinking skills. Early treatment could slow down the damage caused by AD. The knowledge of the anatomical features in MR and CT images can be exploited to better evaluate the tracer dynamics in PET images by first registering anatomical information from MRI with CT images and then with functional information contained in PET. With all of methods, an early diagnosis of may be anticipated.

致謝 i
摘要 ii
Abstract iii
1.1 Background 1
1.2 Purpose 3
1.3 Frameworks 4
2.1 Medical imaging 5
2.1.1 Positron Emission Tomography 5
2.1.2 Anatomical medical imaging 6
2.2 Simulation and Reconstruction Algorithms 8
2.2.1 A Simulation Tool for PET: GATE 8
2.2.2 Reconstruction Algorithms for PET image 9
2.2.3 Ray-tracing algorithm 10
2.2.4 Monte Carlo Simulation 11
2.2.5 Total Variation Minimization Algorithm 12
2.2.6 Proximal Splitting Method 13
2.3 Alzheimer’s disease 14
2.4 Registration Algorithms 15
3.1 Simulation Flowchart 17
3.2 Experimental data 19
3.2.1 Simulated data for PET reconstruction 19
3.2.2 Simulated data for registration 20
3.3 Research Methods 21
3.3.1 Reconstruction Methods 21 System Response Matrix 21 Ray-tracing Simulation 22 Total Variation Algorithm 22 Median Root Prior 23 Splitting-based fast iterative shrinkage-thresholding algorithm with prior 24
3.3.2 Registration method 26
4.1 Simulation Phantom and Source for PET Reconstruction 28
4.2 Reconstruction algorithms comparison 29
4.2.1 The parameters in EM-TV and PLS-TV 30
4.2.2 The performance of EM/EM-TV 33
4.2.3 The performance of EM-TV/PLS-TV algorithm 38
4.2.4 The performance of PLS-TV/PLS-TV-MRP algorithm 43
4.3 Registration Results 48
5.1 Research Summary 52
5.2 Future Work 53

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