雙頭平面PET系統的影像重建與質子治療中的深度學習應用��

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爾拉(Atiq Ur Rahman) 查詢紙本館藏

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雙頭平面PET系統的影像重建與質子治療中的深度學習應用"
(Image Reconstruction for Dual Head Plane PET System and Deep Learning Based Application in Proton Therapy)

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摘要(中)

本論文的第一部分探討了正子發射斷層掃描（PET）成像的前沿技術，通過開發一種新型旋轉雙頭PET系統，旨在克服緊湊型PET探測器中固有的空間分辨率限制。該方法的核心是引入一種基於最大似然估計方法（MLEM）的三角重建技術。我們使用GATE/Geant4 10.4模擬工具包進行探測器模擬，並開發了自己的圖像重建框架。估計了圖像分辨率和探測器靈敏度。使用三角重建方法，沿有限角度軸的分辨率從3.6毫米提高到1.7毫米。

論文的第二部分深入研究了通過深度學習提高粒子治療劑量分佈的準確性。我們關注從探測器數據到內在劑量分佈的轉換，使用GATE/Geant4對暴露於高能質子的人類CT模型進行蒙特卡羅模擬。採用條件生成對抗網絡，我們開發了一個神經網絡模型，從PET符合分佈中推斷劑量圖。我們的模型通過平均相對誤差和布拉格峰位置偏差進行評估，顯示在單能量輻照中，劑量偏差在1％以內，範圍在2％以內，且在實際的展寬布拉格峰條件下性能保持穩定。這項工作證明了深度學習用於將低計數數據映射到劑量分佈的可行性，為粒子治療成像帶來了進展。

項目的第三部分檢查了質子治療劑量沉積的精確度，重點是使用中研院PET的模塊化設計以及SiPMs和STiC asic讀出，進行範圍驗證。該研究展示了130 MeV質子輻照PMMA時正子發射體的深度分佈，並將長庚紀念醫院緊湊的32通道設置的測量結果與Geant4模擬進行比較。通過計時能力和不同同位素的多指數擬合分析的結果驗證了解剖變化估計在連續治療期間的實施。此外，我們介紹了AS-PET的模塊化設計及其模擬成像性能，突出其在質子治療範圍驗證中的潛力。這項論文研究可以幫助改善平板PET系統的PET圖像質量，可作為粒子治療中治療計劃和質量保證的有價值工具。

摘要(英)

The first part of the thesis explores the frontier of Positron Emission Tomography (PET) imaging through the development of a novel rotating dual-head PET system, aimed at overcoming the inherent spatial resolution limitations within compact PET detectors. Central to this method is the introduction of a three-angle reconstruction technique utilizing the basic Maximum Likelihood Estimation Method (MLEM). We use GATE/Geant4 10.4 simulation toolkit to perform detector simulation and have developed our own image reconstruction framework.Image resolution and detector sensitivity are estimated. The resolution along limited-angle axis is improved from 3.6 mm to 1.7 mm using three-angle reconstruction approach.

The second part of the thesis delves into enhancing dose distribution accuracy in particle therapy through deep learning. Focusing on the transition from detector data to intrinsic dose distributions, we utilize Monte Carlo simulations with GATE/Geant4 on a human CT phantom exposed to high-energy protons. Employing a conditional generative adversarial network, we develop a neural network model to infer dose maps from PET coincidence distributions. Our model, evaluated by mean relative error and deviations in Bragg peak position, demonstrates deviations within 1% for dose and 2% for range in mono-energetic irradiations, with performance sustained under realistic spread-out Bragg peak conditions. This work underscores the feasibility of deep learning for mapping low count data to dose distributions, promising advancements in particle therapy imaging.

The third part of project examines the precision of proton therapy dose deposition, focusing on range verification using the Academia Sinica PET′s modular design with SiPMs and STiC asic readout. This study presents the positron emitter depth distribution in PMMA irradiated by 130 MeV protons, comparing measurements from a compact 32-channel setup at Chang Gung Memorial Hospital with Geant4 simulations. The results, validated by timing capabilities and multi-exponential fit analysis of different isotopes, confirms the implementation on anatomical change estimation during successive treatment sessions. Additionally, we introduce the AS-PET′s modular design and its simulated imaging performance, highlighting its potential in range verification for proton therapy. This thesis research can help to improve the PET image quality for flat-panel PET systems and can be a valuable tool for treatment planning and quality assurance in particle therapy.

關鍵字(中)

★ 正子發射斷層掃描
★ 正子發射斷層掃描（PET）成像
★ 三角度影像重建
★ 質子劑量預測
★ 醫學成像中的深度學習

關鍵字(英)

★ Positron Emission Tomography
★ PET Imaging
★ Three angle image reconstruction
★ Proton Dose Prediction
★ Deep Learning in Medical Imaging

論文目次

Contents
1 Introduction 1
1.1 Positron Emission Tomography . . . . . . . . . . . . . . . . . . . . 3
1.2 A Brief History of PET Imaging . . . . . . . . . . . . . . . . . . . . 5
1.3 Background of flat-Panel PET systems . . . . . . . . . . . . . . . . 6
1.3.1 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . 9
2 Physics of Positron Emission Tomography 11
2.1 FDG in PET Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.1.1 Radiotracer Decay in PET . . . . . . . . . . . . . . . . . . . 13
2.1.2 Annihilation Phenomenon . . . . . . . . . . . . . . . . . . 14
2.1.3 Implications of Positron Range in PET Imaging . . . . . . 15
2.1.4 Photon Non-Collinearity . . . . . . . . . . . . . . . . . . . . 16
2.1.5 Gamma Detection in PET Imaging . . . . . . . . . . . . . . 16
2.1.6 Processes of Light Matter Interaction . . . . . . . . . . . . . 17
Photoelectric Absorption . . . . . . . . . . . . . . . . . . . 17
Compton Scattering . . . . . . . . . . . . . . . . . . . . . . 19
Rayleigh Scattering . . . . . . . . . . . . . . . . . . . . . . . 19
Pair production . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.1.7 Photon Interaction Cross-Section. . . . . . . . . . . . . . . 21
2.2 Scintillation Process and Detectors . . . . . . . . . . . . . . . . . . 22
2.2.1 Inorganic Scintillators . . . . . . . . . . . . . . . . . . . . . 22
2.2.2 Organic Scintillators . . . . . . . . . . . . . . . . . . . . . . 22
2.2.3 Scintillation Crystal Properties for 511 keV Gamma Pho-
ton Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 22
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2.3 Photodetectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3.1 Photo-Multiplier Tubes (PMT) . . . . . . . . . . . . . . . . 24
2.3.2 Block Detectors . . . . . . . . . . . . . . . . . . . . . . . . . 25
Spatial Resolution Limitations . . . . . . . . . . . . . . . . 26
2.3.3 Solid State Photodetectors . . . . . . . . . . . . . . . . . . . 27
2.3.4 Spatial Resolution in PET Systems . . . . . . . . . . . . . . 28
3 Image Reconstruction of Plane PET System 29
3.1 ASPET geometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Monte-carlo simulations . . . . . . . . . . . . . . . . . . . . . . . . 29
3.2.1 Monte Carlo Method in PET Imaging . . . . . . . . . . . . 29
3.2.2 Geant4/Gate . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3 GATE Simulation Architecture . . . . . . . . . . . . . . . . . . . . 31
3.3.1 World volume . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.3.2 Choosing a PET system . . . . . . . . . . . . . . . . . . . . 32
3.3.3 First Volume . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3.4 Seond Volume . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3.5 Third volume . . . . . . . . . . . . . . . . . . . . . . . . . . 33
3.3.6 Fourth Volume . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.7 Fifth Volume . . . . . . . . . . . . . . . . . . . . . . . . . . 34
3.3.8 Crystal mapping . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3.9 Physics list . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
3.3.10 Reduction Cuts in Simulations . . . . . . . . . . . . . . . . 36
3.3.11 Readout chain and digitizer . . . . . . . . . . . . . . . . . . 37
Digitization . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Energy blurring . . . . . . . . . . . . . . . . . . . . . . . . . 38
Time resolution . . . . . . . . . . . . . . . . . . . . . . . . . 38
Time of Flight . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Coincidence Sorter . . . . . . . . . . . . . . . . . . . . . . . 40
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Detector calibration . . . . . . . . . . . . . . . . . . . . . . . 40
Output format . . . . . . . . . . . . . . . . . . . . . . . . . . 41
ASCII and Binary Outputs . . . . . . . . . . . . . . . . . . . 41
3.4 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . . 41
3.4.1 Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.4.2 Evaluation of Scatter Fraction and Count Rate Performance
using a Rat-Sized Phantom . . . . . . . . . . . . . . . . . . 45
3.5 Approaches for Image Reconstruction . . . . . . . . . . . . . . . . 48
3.5.1 Back Projection in PET Imaging . . . . . . . . . . . . . . . . 48
3.5.2 Filtered Back Projection Method . . . . . . . . . . . . . . . 50
3.5.3 Iterative Reconstruction . . . . . . . . . . . . . . . . . . . . 50
Maximum Likelihood Estimation . . . . . . . . . . . . . . . 51
Application of Maximum Likelihood Estimation to Pois-
son Distribution . . . . . . . . . . . . . . . . . . . 52
Forward Projection . . . . . . . . . . . . . . . . . . . . . . . 54
Ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Backward Projection . . . . . . . . . . . . . . . . . . . . . . 55
Update . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.6 Calculating System Response Matrix . . . . . . . . . . . . . . . . . 57
3.6.1 Technical Challenges in Image Reconstruction . . . . . . . 62
3.7 Symmetry properties of ASPET . . . . . . . . . . . . . . . . . . . . 63
3.7.1 Response of detector to a point source . . . . . . . . . . . . 63
Shift Invariance . . . . . . . . . . . . . . . . . . . . . . . . . 64
Interchangeability . . . . . . . . . . . . . . . . . . . . . . . 65
Axial Reflection . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.7.2 Pseudo-code of SRM calculation . . . . . . . . . . . . . . . 67
3.7.3 Structure of the SRM . . . . . . . . . . . . . . . . . . . . . . 68
3.7.4 Approach to reduce parallax error on image resolution . . 69
3.7.5 Siddon ’s raytracing method . . . . . . . . . . . . . . . . . 69
xi
Similarity in SRM voxels . . . . . . . . . . . . . . . . . . . . 71
3.8 Image evaluation phantom . . . . . . . . . . . . . . . . . . . . . . 72
3.9 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
3.9.1 Spatial resolutions evaluated by single point source . . . . 72
3.9.2 Spatial Resolution in Multi-Source Environments . . . . . 73
Average resolution . . . . . . . . . . . . . . . . . . . . . . . 74
Spatial resolution as function of distance . . . . . . . . . . 74
3.9.3 Effect of iterations on resolution . . . . . . . . . . . . . . . 74
3.9.4 Reconstructed point sources . . . . . . . . . . . . . . . . . . 75
3.9.5 Derenzo phantom: cylinders were along x-direction . . . . 75
3.9.6 Derenzo phantom: cylinders were along y-direction . . . . 75
4 Three Angle Reconstruction 87
4.1 Three Angle Reconstruction . . . . . . . . . . . . . . . . . . . . . . 87
4.1.1 Scheme-1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.1.2 Scheme-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.1.3 Change in detector setup . . . . . . . . . . . . . . . . . . . 90
Schematic of three angle reconstruction . . . . . . . . . . . 91
MLEM for Three Angle Reconstruction . . . . . . . . . . . 91
4.1.4 Generalized MLEM Equation for Multi-angle Reconstruc-
tion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.2 Image Rotation Around the Y-Axis . . . . . . . . . . . . . . . . . . 94
4.3 Forward and Inverse Image Rotation: Significance and Drawbacks 95
4.3.1 Forward Rotation . . . . . . . . . . . . . . . . . . . . . . . . 95
4.3.2 Inverse Rotation . . . . . . . . . . . . . . . . . . . . . . . . 96
4.4 Derenzo Phantom for PET Imaging Performance Evaluation . . . 97
4.5 Practical Challenges in Implementing a Rotary ASPET . . . . . . 97
4.6 Artifacts and Limitations of MLEM in PET Imaging . . . . . . . . 99
4.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
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4.8 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.9 Results:Three angle reconstruction schemes . . . . . . . . . . . . . 101
4.9.1 Scheme1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.9.2 Scheme-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.9.3 Detector resolutions . . . . . . . . . . . . . . . . . . . . . . 102
Resolution along x-axis . . . . . . . . . . . . . . . . . . . . 102
Resolution along y-axis . . . . . . . . . . . . . . . . . . . . 102
Resolution along z-axis . . . . . . . . . . . . . . . . . . . . 108
4.9.4 Relative Loss . . . . . . . . . . . . . . . . . . . . . . . . . . 110
4.9.5 Cylindrical arrangement of point sources . . . . . . . . . . 110
4.9.6 Derenzo phantom . . . . . . . . . . . . . . . . . . . . . . . 111
4.9.7 Hoffman phantom . . . . . . . . . . . . . . . . . . . . . . . 111
Comparison of ground truth with reconstructed slices . . 111
4.9.8 Reconstructed Hoffman Phantom . . . . . . . . . . . . . . 111
4.9.9 Limitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
4.10 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
4.11 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5 Positron Emitter Depth Distribution in PMMA Irradiated with 130 MeV
Protons Measured using TOF-PET Detectors 131
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
5.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
5.2 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . 134
5.2.1 Coincidence testing setup . . . . . . . . . . . . . . . . . . . 135
5.2.2 Experimental design for the beam test . . . . . . . . . . . . 137
5.2.3 AS-PET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
5.2.4 Simulation setup . . . . . . . . . . . . . . . . . . . . . . . . 138
5.2.5 Exponential distribution calculations . . . . . . . . . . . . 139
5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
xiii
5.3.1 Depth distribution of β+ emitters . . . . . . . . . . . . . . . 141
5.3.2 Ratio of the β+ emitting isotopes . . . . . . . . . . . . . . . 143
5.3.3 Range shifts using simulated data . . . . . . . . . . . . . . 146
5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
6 Direct mapping from PET coincidence data to proton-dose and positron
activity using a deep learning approach 151
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
6.2 Material and Methods . . . . . . . . . . . . . . . . . . . . . . . . . 154
6.2.1 Proton Beam Irradiation Simulations . . . . . . . . . . . . 154
6.2.2 Configuration for Detector Simulation . . . . . . . . . . . . 155
6.2.3 Framework for Deep Learning . . . . . . . . . . . . . . . . 157
6.2.4 Conditional Generative Adversarial Network Structure . . 158
6.2.5 Preparation of Training Data . . . . . . . . . . . . . . . . . 160
6.2.6 Criteria for Model Performance Evaluation . . . . . . . . . 161
6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
6.3.1 Assessment of Dose Distribution and Isotope Mapping . . 162
6.3.2 Dose Distribution and Isotope Mapping Efficacy . . . . . . 163
6.3.3 Evaluating Model Efficacy . . . . . . . . . . . . . . . . . . . 164
6.3.4 Coincidence Data Evaluation . . . . . . . . . . . . . . . . . 167
6.3.5 Influence of Detection Threshold on Model Efficacy . . . . 169
6.3.6 Impact of Cross-section Variability and Material Compo-
sition on Model Accuracy . . . . . . . . . . . . . . . . . . . 170
6.3.7 Evaluation of Sensitivity Factors . . . . . . . . . . . . . . . 171
6.3.8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
6.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
Bibliography 175
xiv

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指導教授

李世昌(Lee Shih Chang)

審核日期

2024-4-29

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