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研究生： 爾拉
Atiq Ur Rahman
論文名稱： 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
指導教授： 李世昌
Lee Shih Chang
口試委員:
學位類別： 博士
Doctor
系所名稱： 理學院 - 物理學系
Department of Physics
論文出版年： 2024
畢業學年度： 112
語文別： 英文
論文頁數： 192
中文關鍵詞： 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
外文關鍵詞： Positron Emission Tomography, PET Imaging, Three angle image reconstruction, Proton Dose Prediction, Deep Learning in Medical Imaging
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    • 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。

    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.

    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 ix 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 x 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 xii 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

    Bibliography
    [1] Eric M. Rohren, Timothy G. Turkington, and R. Edward Coleman. “Clinical
    Applications of PET in Oncology”. In: Radiology 231.2 (2004), pp. 305–
    332.
    [2] Carl K Hoh et al. “Cancer Detection with Whole-Body PET Using 2-
    [18F]Fluoro-2-Deoxy-D-Glucose”. In: Journal of Computer Assisted Tomography
    17.4 (July 1993), pp. 582–589.
    [3] P S Conti. The applications of PET in clinical oncology. 1991.
    [4] Michael A Nader et al. “PET imaging of dopamine D2 receptors during
    chronic cocaine self-administration in monkeys”. In: Nature neuroscience
    9.8 (2006), p. 1050.
    [5] Jinyi Qi et al. “High-resolution 3D Bayesian image reconstruction using
    the microPET small-animal scanner”. In: Physics in Medicine & Biology
    43.4 (1998), p. 1001.
    [6] Alberto Motta et al. “Fast 3D-EM reconstruction using Planograms for
    stationary planar positron emission mammography camera”. In: Computers
    in Medical Imaging and Graphics 29.5 (2005), pp. 587–596.
    [7] S. Surti and J. S. Karp. “Advances in time-of-flight PET”. In: Physics in
    Medicine and Biology 52.5 (2007), R1.
    [8] J. Zhang et al. “Advanced DOI Correction Methods in PET Imaging”. In:
    Journal of Clinical Imaging Science 10.3 (2007), pp. 45–52.
    [9] Chih-Ming Kao et al. “A high-sensitivity small-animal PET scanner: Development
    and initial performance measurements”. In: IEEE Transactions
    on Nuclear Science 56.5 (2009), pp. 2678–2688.
    [10] Lawrence R MacDonald et al. “Clinical imaging characteristics of
    the positron emission mammography/tomography breast imaging and
    biopsy system (PEM/PET): Design, construction, and phantom-based
    measurements”. In: Journal of Nuclear Medicine 50.10 (2009), pp. 1666–
    1675.
    [11] Hao Peng and Craig S Levin. “Design study of a high-resolution breastdedicated
    PET system built from cadmium zinc telluride detectors”. In:
    Physics in Medicine and Biology 55.11 (2010), p. 2761.
    173
    [12] Hui Zhang et al. “Performance characteristics of BGO detectors for a low
    cost preclinical PET scanner”. In: IEEE Transactions on Nuclear Science 57.3
    (2010), pp. 1038–1044.
    [13] WilliamWMoses and Jinyi Qi. “Fundamental limits of positron emission
    mammography”. In: Nuclear Instruments and Methods in Physics Research
    Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
    497.1 (2003), pp. 82–89.
    [14] Pablo Crespo, Georgy Shakirin, and Wolfgang Enghardt. “On the detector
    arrangement for in-beam PET for hadron therapy monitoring”. In:
    Physics in Medicine and Biology 51.11 (2006), p. 2143.
    [15] Raymond R Raylman et al. “The positron emission mammography/tomography
    breast imaging and biopsy system (PEM/PET): design, construction
    and phantom-based measurements”. In: Physics in Medicine and
    Biology 53.3 (2008), p. 637.
    [16] Charles J Thompson et al. “Feasibility study for positron emission mammography”.
    In: Medical Physics 21.4 (1994), pp. 529–538.
    [17] Wen Luo, Evgeniy Anashkin, and Charles G Matthews. “Performance
    evaluation of a PEM scanner using the NEMA NU 4 — 2008 small animal
    PET standards”. In: IEEE Transactions on Nuclear Science 57.1 (2010),
    pp. 94–100.
    [18] Martin F Smith et al. “Positron emission mammography with tomographic
    acquisition using dual planar detectors: initial evaluations”. In:
    Physics in Medicine and Biology 49.11 (2004), p. 2437.
    [19] Hui Zhang et al. “Performance evaluation of PETbox: a low cost bench
    top preclinical PET scanner”. In: Molecular Imaging and Biology 13.5 (2011),
    pp. 949–961.
    [20] Alberto Guerra et al. “Performance evaluation of the fully engineered
    YAP-(S)PET scanner for small animal imaging”. In: IEEE Transactions on
    Nuclear Science 53.3 (2006), pp. 1078–1084.
    [21] Peter Bruyndonckx et al. “Initial characterization of a nonpixelated scintillator
    detector in a PET prototype demonstrator”. In: IEEE Transactions
    on Nuclear Science 53.3 (2006), pp. 2543–2548.
    [22] Peter Bruyndonckx et al. “Initial Characterization of a Nonpixelated Scintillator
    Detector in a PET Prototype Demonstrator”. In: IEEE Transactions
    on Nuclear Science 53.3 (2006), pp. 2543–2548.
    [23] Hector Alva-Sanchez et al. “A Small-Animal PET System Based on LYSO
    Crystal Arrays, PS-PMTs and a PCI DAQ Board”. In: IEEE Transactions on
    Nuclear Science 57.3 (2010), pp. 85–91.
    174
    [24] Chih-Ming Kao et al. “Image reconstruction of a dual-head small-animal
    PET system by using Monte-Carlo computed system response matrix”.
    In: 9th International Meeting on Fully Three-Dimensional Image Reconstruction
    in Radiology and Nuclear Medicine (2007), p. 398.
    [25] Qiang Bao et al. “Image Reconstruction for PETbox, a Benchtop Preclinical
    PET Tomograph”. In: IEEE Nuclear Science Symposium Conference
    Record 2009 (2009), pp. 2733–2737.
    [26] Ying Liu et al. “System response matrix calculation using symmetries for
    dual-head PET scanners”. In: International Journal of Imaging Systems and
    Technology 23.3 (2013), pp. 205–210.
    [27] Chih-Ming Kao et al. “A High-Sensitivity Small-Animal PET Scanner:
    Development and Initial Performance Measurements”. In: IEEE Transactions
    on Nuclear Science 56.5 (2009), pp. 2678–2688.
    [28] Chenxi Zhang et al. “Performance evaluation of a 90 degrees-rotating
    dual-head small animal PET system”. In: Physics in Medicine and Biology
    60.20 (2015), pp. 5873–5886.
    [29] Stefan Siegel et al. “Initial results from a PET planar small animal imaging
    system”. In: IEEE Transactions on Nuclear Science 46.3 (1999), pp. 571–
    577.
    [30] Sascha Moehrs et al. “Multi-ray-based system matrix generation for 3D
    PET reconstruction”. In: Physics in Medicine and Biology 53.24 (2008),
    pp. 6925–6947.
    [31] Chih-Ming Kao, Yang Dong, and Qingguo Xie. “Evaluation of 3D image
    reconstruction methods for a dual-head small-animal PET scanner”. In:
    IEEE Nuclear Science Symposium Conference Record 2007 (2007), p. 072007.
    [32] Feng Meng et al. “Influence of Rotation Increments on Imaging Performance
    for a Rotatory Dual-Head PET System”. In: BioMed Research International
    2017 (2017), p. 8615086.
    [33] William H. Sweet. “The uses of nuclear disintegration in the diagnosis
    and treatment of brain tumor”. In: N. Engl. J. Med. 245.23 (1951), pp. 875–
    878. DOI: 10.1056/NEJM195112062452301.
    [34] F. R. J.Wrenn, M. L. Good, and P. Handler. “The use of positron-emitting
    radioisotopes for the localization of brain tumors”. In: Science 113.2940
    (1951), pp. 525–527. DOI: 10.1126/science.113.2940.525.
    [35] Michael E. Phelps et al. “Application of annihilation coincidence detection
    to transaxial reconstruction tomography”. In: J. Nucl. Med. 16.3
    (1973), pp. 210–224.
    175
    [36] Michael E. Phelps et al. “Design Considerations for a Positron Emission
    Transaxial Tomograph (PETT III)”. In: IEEE Trans. Nucl. Sci. 23.1 (1976),
    pp. 516–522. DOI: 10.1109/TNS.1976.4328298.
    [37] Michel M. Ter-Pogossian et al. “A positron-emission transaxial tomograph
    for nuclear imaging (PETT)”. In: Radiology 114.1 (1975), pp. 89–98.
    DOI: 10.1148/114.1.89.
    [38] C. Bohm et al. “A Computer Assisted Ringdetector Positron Camera System
    for Reconstruction Tomography of the Brain”. In: IEEE Trans. Nucl.
    Sci. 25.1 (1978), pp. 624–637. DOI: 10.1109/TNS.1978.4329384.
    [39] Donald L. Snyder and David G. Politte. “Image Reconstruction from List-
    Mode Data in an Emission Tomography System Having Time-of-Flight
    Measurements”. In: IEEE Trans. Nucl. Sci. 30.3 (1983), pp. 1843–1849. DOI:
    10.1109/TNS.1983.4332660.
    [40] Michel M. Ter-Pogossian et al. “The super PET 3000-E: a PET scanner
    designed for high count rate cardiac applications”. In: J. Comput. Assist.
    Tomogr. 18.4 (1994), pp. 661–669.
    [41] M. E. Casey and R. Nutt. “A Multicrystal Two Dimensional BGO Detector
    System for Positron Emission Tomography”. In: IEEE Trans. Nucl. Sci. 33.1
    (1986), pp. 460–463. DOI: 10.1109/TNS.1986.4337143.
    [42] C. L. Melcher and J. S. Schweitzer. “Cerium-doped lutetium oxyorthosilicate:
    a fast, efficient new scintillator”. In: IEEE Trans. Nucl. Sci. 39.4 (1992),
    pp. 502–505. DOI: 10.1109/23.159655.
    [43] S. R. Cherry et al. “MicroPET: a high resolution PET scanner for imaging
    small animals”. In: IEEE Trans. Nucl. Sci. 44.3 (1997), pp. 1161–1166. DOI:
    10.1109/23.596981.
    [44] D. Visvikis, C. Cheze-Le Rest, and P. Jarritt. “PET technology: current
    trends and future developments”. In: Journal of Nuclear Medicine and
    Molecular Imaging 31 (2004), pp. 208–221.
    [45] W. Luo, E. Anashkin, and C. Matthews. “Performance evaluation of a
    PEM scanner using the NEMA NU 4 — 2008 small animal PET standards”.
    In: IEEE Transactions on Nuclear Science 57.1 (2010), p. 94.
    [46] P. Crespo, G. Shakirin, and W. Enghardt. “On the detector arrangement
    for in-beam PET for hadron therapy monitoring”. In: Physics in Medicine
    and Biology 51 (2006), p. 2143.
    [47] J. S. Karp. “Against: Is LSO the future of PET?” In: European Journal of
    Nuclear Medicine 29 (2002), pp. 1525–1528.
    176
    [48] M. F. Smith et al. “Positron emission mammography with tomographic
    acquisition using dual planar detectors: initial evaluations”. In: Physics in
    Medicine and Biology 49 (2004), p. 2437.
    [49] H. Zhang et al. “Performance evaluation of PETbox: a low cost bench
    top preclinical PET scanner”. In: Molecular Imaging and Biology 13 (2011),
    p. 949.
    [50] A. Guerra et al. “Performance evaluation of the fully engineered YAP-
    (S)PET scanner for small animal imaging”. In: IEEE Transactions on Nuclear
    Science 53 (2006), p. 1078.
    [51] R. R. Raylman et al. “The positron emission mammography/tomography
    breast imaging and biopsy system (PEM/PET): design, construction
    and phantom-based measurements”. In: Physics in Medicine and Biology
    53 (2008), p. 637.
    [52] C. J. Thompson et al. “Feasibility study for positron emission mammography”.
    In: Medical Physics 21 (1994), p. 529.
    [53] Sascha Moehrs et al. “A small animal PET scanner based on LYSO crystal
    arrays, PS-PMTs and a PCI DAQ board”. In: IEEE Transactions on Nuclear
    Science 55.6 (2008), pp. 3134–3140.
    [54] M. F. Smith et al. “Positron emission mammography with tomographic
    acquisition using dual planar detectors: initial evaluations”. In: Physics in
    Medicine and Biology 49 (2004), p. 2437.
    [55] C. M. Kao et al. “A Monte Carlo approach to eliminate DOI blurring in
    PET”. In: Journal of Instrumentation 12.9011 (2017).
    [56] T. Beyer, D. W. Townsend, and T. M. Blodgett. “Dual modality PET/CT
    tomography for clinical oncology”. In: Quarterly Journal of Nuclear
    Medicine 46 (2002), pp. 24–34.
    [57] Y. Shao et al. “Simultaneous PET and MR imaging”. In: Physics in
    Medicine and Biology 42 (1997), pp. 1965–1970.
    [58] Y.C. Tai et al. “Performance Evaluation of the MicroPET P4: A PET System
    Dedicated to Animal Imaging”. In: Physics in Medicine & Biology 46.7
    (2001), p. 1845.
    [59] Christof Knoess et al. “Performance evaluation of the microPET R4 PET
    scanner for rodents”. In: European journal of nuclear medicine and molecular
    imaging 30.5 (2003), pp. 737–747.
    [60] Marc C Huisman et al. “Performance evaluation of the Philips MOSAIC
    small animal PET scanner”. In: European journal of Nuclear Medicine and
    Molecular imaging 34.4 (2007), pp. 532–540.
    177
    [61] Klaus P. Schäfers et al. “Performance Evaluation of the 32-Module
    quadHI-DAC Small-Animal PET Scanner”. In: Journal of Nuclear Medicine
    46.6 (2005), pp. 996–1004.
    [62] Richard Laforest et al. “Performance evaluation of the microPET R
    —FOCUS-F120”. In: IEEE Transactions on Nuclear Science 54.1 (2007),
    pp. 42–49.
    [63] Andrew L Goertzen et al. “NEMA NU 4-2008 comparison of preclinical
    PET imaging systems”. In: Journal of Nuclear Medicine 53.8 (2012),
    pp. 1300–1309.
    [64] Karl Ziemons et al. “The ClearPETTM Project: Development of a 2nd
    Generation High-Performance Small Animal PET Scanner”. In: Nuclear
    Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers,
    Detectors and Associated Equipment 537.1-2 (2005), pp. 307–311.
    [65] Yuchuan Wang et al. “Performance Evaluation of the GE Healthcare eXplore
    VISTA Dual-Ring Small-Animal PET Scanner”. In: Journal of Nuclear
    Medicine 47.11 (2006), pp. 1891–1900.
    [66] E Lage et al. “Design and performance evaluation of a coplanar multimodality
    scanner for rodent imaging”. In: Physics in Medicine & Biology
    54.18 (2009), p. 5427.
    [67] Melanie Bergeron et al. “Imaging performance of LabPET APD-based
    digital PET scanners for pre-clinical research”. In: Physics in Medicine &
    Biology 59.3 (2014), p. 661.
    [68] Melanie Bergeron et al. “Performance evaluation of the LabPET APDbased
    digital PET scanner”. In: IEEE Transactions on Nuclear Science 56.1
    (2009), pp. 10–16.
    [69] K. Sato et al. “Performance Evaluation of the Small-Animal PET Scanner
    ClairvivoPET Using NEMA NU 4-2008 Standards”. In: Physics in
    Medicine & Biology 61.2 (2015), p. 696.
    [70] Rameshwar Prasad, Osman Ratib, and Habib Zaidi. “Performance evaluation
    of the FLEX triumph X-PET scanner using the national electrical
    manufacturers association NU-4 standards”. In: Journal of Nuclear
    Medicine 51.10 (2010), pp. 1608–1615.
    [71] Kálmán Nagy et al. “Performance evaluation of the small-animal
    nanoScan PET/MRI system”. In: Journal of Nuclear Medicine 54.10 (2013),
    pp. 1825–1832.
    [72] T.J. Spinks et al. “Quantitative PET and SPECT Performance Characteristics
    of the Albira Trimodal Pre-clinical Tomograph”. In: Physics in
    Medicine & Biology 59.3 (2014), p. 715.
    178
    [73] Luyao Wang et al. “Performance Evaluation of the Trans-PET R BioCaliburn
    R LH System: A Large FOV Small-Animal PET System”. In: Physics
    in Medicine & Biology 60.1 (2014), p. 137.
    [74] Wai-Hoi Wong et al. “Engineering and Performance (NEMA and Animal)
    of a Lower-Cost Higher-Resolution Animal PET/CT Scanner Using
    Photomultiplier-Quadrant-Sharing Detectors”. In: Journal of Nuclear
    Medicine 53.11 (2012), pp. 1786–1793.
    [75] Istvan Szanda et al. “National Electrical Manufacturers Association NU-
    4 Performance Evaluation of the PET Component of the NanoPET/CT
    Preclinical PET/CT Scanner”. In: Journal of Nuclear Medicine 52.11 (2011),
    pp. 1741–1747.
    [76] Cristian C Constantinescu and Jogeshwar Mukherjee. “Performance
    evaluation of an Inveon PET preclinical scanner”. In: Physics in Medicine
    & Biology 54.9 (2009), p. 2885.
    [77] Nicola Belcari et al. “NEMA NU-4 performance evaluation of the IRIS
    PET/CT preclinical scanner”. In: IEEE Transactions on Radiation and
    Plasma Medical Sciences 1.4 (2017), pp. 301–309.
    [78] Matthew G Vander Heiden, Lewis C Cantley, and Craig B Thompson.
    “Understanding the Warburg effect: the metabolic requirements of cell
    proliferation”. In: science 324.5930 (2009), pp. 1029–1033.
    [79] Khalid O Alfarouk et al. “Glycolysis, tumor metabolism, cancer growth
    and dissemination. A new pH-based etiopathogenic perspective and
    therapeutic approach to an old cancer question”. In: Oncoscience 1.12
    (2014), p. 777.
    [80] Angela M Otto. “Warburg effect (s)—a biographical sketch of Otto Warburg
    and his impacts on tumor metabolism”. In: Cancer & metabolism 4.1
    (2016), pp. 1–8.
    [81] The Nobel Prize in Physiology or Medicine 1931. NobelPrize.org. Accessed:
    2nd February 2024. URL: https://www.nobelprize.org/prizes/
    medicine/1931/summary/.
    [82] Didier Le Bars. “Fluorine-18 and medical imaging: Radiopharmaceuticals
    for positron emission tomography”. In: Journal of Fluorine Chemistry
    127.11 (2006), pp. 1488–1493.
    [83] Orit Jacobson, Dale O Kiesewetter, and Xiaoyuan Chen. “Fluorine-18 radiochemistry,
    labeling strategies and synthetic routes”. In: Bioconjugate
    chemistry 26.1 (2014), pp. 1–18.
    179
    [84] Wim JG Oyen et al. “Role of FDG-PET in the diagnosis and management
    of lung cancer”. In: Expert Review of Anticancer Therapy 4.4 (Aug. 2004),
    pp. 561–567.
    [85] R.A. Schmid et al. “Staging of Recurrent and Advanced Lung Cancer
    with 18F-FDG PET in a Coincidence Technique (Hybrid PET)”. In: Nuclear
    Medicine Communications 24.1 (2003), pp. 37–45.
    [86] Rodney J Hicks et al. “The utility of 18F-FDG PET for suspected recurrent
    non–small cell lung cancer after potentially curative therapy: impact on
    management and prognostic stratification”. In: Journal of Nuclear Medicine
    42.11 (2001), pp. 1605–1613.
    [87] Rodney J Hicks. “Role of 18F-FDG PET in assessment of response in
    non–small cell lung cancer”. In: Journal of Nuclear Medicine (2009).
    [88] F Castell and GJR Cook. “Quantitative techniques in 18 FDG PET scanning
    in oncology”. In: British Journal of Cancer 98.10 (2008), p. 1597.
    [89] James H.F. Rudd et al. “Imaging Atherosclerotic Plaque Inflammation by
    Fluorodeoxyglucose with Positron Emission Tomography”. In: Journal of
    the American College of Cardiology 55.23 (June 2010), pp. 2527–2535.
    [90] RR Trinder et al. “Theragnostics-Alternative production of terbium isotopes
    at the University of Birmingham using an MC40 cyclotron”. In:
    Journal of Physics: Conference Series. Vol. 1643. 1. IOP Publishing. 2020,
    p. 012209.
    [91] Michael E Phelps, ed. PET. Springer New York, 2006.
    [92] Patries M Herst and Michael V Berridge. “Cell hierarchy, metabolic flexibility
    and systems approaches to cancer treatment”. In: Current pharmaceutical
    biotechnology 14.3 (2013), pp. 289–299.
    [93] Jim Rees. PET Scan of a Healthy Brain Compared to a Brain at an Early Stage
    of Alzheimer’s Disease. http://www.douglas.qc.ca/. Image courtesy
    of Douglas Mental Health University Institute, available on https:
    //www.flickr.com/people/institut-douglas/. Accessed on
    February 2, 2024. 2024.
    [94] Simon R. Cherry and Sanjiv Sam Gambhir. “Use of positron emission
    tomography in animal research.” In: ILAR journal 423 (2001), pp. 219–32.
    URL: https://api.semanticscholar.org/CorpusID:15585648.
    [95] William W Moses. “Fundamental limits of spatial resolution in PET”. In:
    Nuclear Instruments and Methods in Physics Research Section A: Accelerators,
    Spectrometers, Detectors and Associated Equipment 648 (2011), pp. 236–240.
    180
    [96] Craig S Levin and Edward J Hoffman. “Calculation of positron range
    and its effect on the fundamental limit of positron emission tomography
    system spatial resolution”. In: Physics in Medicine & Biology 44.3 (1999),
    p. 781.
    [97] C Le Loirec and C Champion. “Track structure simulation for positron
    emitters of physical interest. Part II: The case of the radiometals”. In: Nuclear
    Instruments and Methods in Physics Research Section A: Accelerators,
    Spectrometers, Detectors and Associated Equipment 582.2 (2007), pp. 654–664.
    [98] Lars Jødal, Cindy Le Loirec, and Christophe Champion. “Positron range
    in PET imaging: Non-conventional isotopes”. In: Physics in medicine and
    biology 59 (Nov. 2014), pp. 7419–7434.
    [99] Simon R. Cherry, James A. Sorenson, and Michael E. Phelps. “Physics in
    Nuclear Medicine”. In: (2012).
    [100] Glenn F. Knoll. Radiation Detection and Measurement. John Wiley & Sons,
    2010.
    [101] J. M. Ollinger. “Model based scatter correction for fully 3D PET”. In:
    Physics in Medicine and Biology 41 (1996), pp. 153–176.
    [102] Thomas K. Lewellen. “Recent developments in PET detector technology”.
    In: Physics in Medicine and Biology 53.17 (2008), pp. 287–300.
    [103] Habib Zaidi and Christopher M. Thompson. “Quantitative analysis in
    nuclear medicine imaging”. In: Journal of Nuclear Medicine 48.1 (2007),
    pp. 61–67.
    [104] D. W. Townsend. “Physical Principles and Technology of Clinical PET
    Imaging”. In: Annals of the Academy of Medicine, Singapore 33 (2004),
    pp. 133–145.
    [105] D. L. Bailey et al. “Positron emission tomography: basic sciences”. In:
    Springer Science & Business Media (2005).
    [106] Miles N. Wernick and John N. Aarsvold. Emission Tomography: The Fundamentals
    of PET and SPECT. Academic Press, 2004.
    [107] Frank Herbert Attix. Introduction to Radiological Physics and Radiation
    Dosimetry. John Wiley & Sons, 1986.
    [108] J. H. Hubbell. “Review of photon interaction cross section data in the
    medical and biological context”. In: Physics in Medicine and Biology 51.13
    (2006), R245.
    [109] Harold Elford Johns and John Robert Cunningham. The Physics of Radiology.
    Charles C Thomas Publisher, 1983.
    [110] Maziar Montazerian et al. “Radiopaque Crystalline, Non-Crystalline and
    Nanostructured Bioceramics”. In: Materials 15.21 (2022), p. 7477.
    181
    [111] John L. Humm, Anatoly Rosenfeld, and Alberto Del Guerra. “From PET
    detectors to PET scanners”. In: European Journal of Nuclear Medicine and
    Molecular Imaging 30.11 (2003), pp. 1574–1597.
    [112] William R. Leo. Techniques for Nuclear and Particle Physics Experiments: A
    How-to Approach. Springer-Verlag, 1994.
    [113] John B. Birks. The Theory and Practice of Scintillation Counting. Pergamon
    Press, 1964.
    [114] Charles L. Melcher. “Scintillation crystals for PET”. In: Journal of Nuclear
    Medicine 41.6 (2000), pp. 1051–1055.
    [115] Michael Schmand et al. “Brain PET using LSO detectors”. In: Journal of
    Nuclear Medicine 39.5 (1998), 63P.
    [116] Saint-Gobain Ceramics & Plastics Inc. “Saint-Gobain, LYSO Data Sheet”.
    In: (2004-2020), pp. 11–20. URL: https://www.luxiumsolutions.
    com/sites/default/files/2021-09/Array-Brochure.pdf.
    [117] Tom K Lewellen. “Recent developments in PET detector technology”. In:
    Physics in Medicine & Biology 53.17 (2008), R287.
    [118] Carel WE Van Eijk. “Inorganic scintillators in medical imaging”. In:
    Physics in medicine & biology 47.8 (2002), R85.
    [119] Dieter Renker. “Geiger-mode avalanche photodiodes, history, properties
    and problems”. In: Nuclear Instruments and Methods in Physics Research
    Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
    567.1 (2009), pp. 48–56.
    [120] Dennis R. Schaart et al. “LaBr3:Ce and SiPMs for time-of-flight PET:
    achieving 100 ps coincidence resolving time”. In: Physics in Medicine and
    Biology 55.7 (2010), N179.
    [121] Ron Ziegler. “The future of digital imaging”. In: Healthcare Imaging
    (2010), pp. 22–25.
    [122] Hamamatsu Photonics. Photomultiplier Tubes: Basics and Applications.
    Tech. rep. Third Edition. Hamamatsu Photonics K.K., 2007.
    [123] M.E. Casey and R. Nutt. “A new high resolution detector for positron
    emission tomography”. In: European Journal of Nuclear Medicine 12 (1986),
    S5–S7.
    [124] ND Volkow, NA Mullani, and B Bendriem. “Positron emission tomography
    instrumentation: an overview.” In: American journal of physiologic
    imaging 3.3 (1988), pp. 142–153.
    [125] J.P. Pansart. “Avalanche Photodiodes”. In: Nuclear Instruments and Methods
    in Physics Research Section A: Accelerators, Spectrometers, Detectors and
    Associated Equipment 392.1-3 (1997), pp. 349–356.
    182
    [126] Samuel España et al. “Avalanche Photodiodes for PET Imaging: A Review”.
    In: IEEE Transactions on Nuclear Science 57.3 (2010), pp. 1230–1241.
    [127] N. Dinu, M. Schmand, and S. Tavernier. “Geiger-mode Avalanche Photodiodes
    for PET”. In: Nuclear Instruments and Methods in Physics Research
    Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
    729 (2013), pp. 3–15.
    [128] V. Golovin and Z. Sadygov. “New Silicon Photomultiplier”. In: Nuclear
    Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers,
    Detectors and Associated Equipment 518.1-2 (2004), pp. 560–564.
    [129] Z. Sadygov and V. Golovin. “Silicon Photomultipliers and their Bio-
    Medical Applications”. In: Nuclear Instruments and Methods in Physics Research
    Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
    767 (2014), pp. 252–267.
    [130] Paolo Castro et al. “Performance of the latest generation of silicon photomultipliers”.
    In: Nuclear Instruments and Methods in Physics Research Section
    A: Accelerators, Spectrometers, Detectors and Associated Equipment 787
    (2015), pp. 207–210.
    [131] Instite of Physics, Academia Sinica, Taiwan. URL: https://www.sinica.
    edu.tw/.
    [132] Mythra Varun Nemallapudi et al. “Positron emitter depth distribution
    in PMMA irradiated with 130 MeV protons measured using TOF-PET
    detectors”. In: IEEE Transactions on Radiation and Plasma Medical Sciences
    (2021), pp. 1–1. DOI: 10.1109/TRPMS.2021.3084953.
    [133] P. Andreo. “Monte Carlo techniques in medical radiation physics”. In:
    Phys. Med. Biol. 36.7 (1991), pp. 861–920.
    [134] M. Ljungberg. Monte Carlo Calculations in Nuclear Medicine: Applications in
    Diagnostic Imaging. Bristol: IOP Publishing, 1998.
    [135] H. Zaidi. Quantitative Analysis in Nuclear Medicine Imaging. New York:
    Springer, 2005.
    [136] B. Bai et al. “Evaluation of MAP image reconstruction with positron
    range modeling for 3D PET”. In: 2005 IEEE Nuclear Science Symposium
    Conference Record. Vol. 1-5. 2005, pp. 2686–2689.
    [137] Y.K. Dewaraja, M. Ljungberg, and K.F. Koral. “Characterization of scatter
    and penetration using Monte Carlo simulation in 131I imaging”. In: J.
    Nucl. Med. 41.1 (2000), pp. 123–130.
    [138] U. Bottigli et al. “Monte-Carlo Simulation and Experimental Tests on
    BGO, CSF and NaI(Tl) Crystals for Positron Emission Tomography”. In:
    Journal of Nuclear Medicine and Allied Sciences 29.3 (1985), pp. 221–227.
    183
    [139] S.E. Derenzo. “Monte-Carlo Calculations of the Detection Efficiency of
    Arrays of NaI(Tl), BGO, CSF, Ge, and Plastic Detectors for 511-KeV Photons”.
    In: IEEE Transactions on Nuclear Science 28.1 (1981), pp. 131–136.
    [140] M. Ljungberg and S.E. Strand. “Scatter and attenuation correction in
    SPECT using density maps and Monte Carlo simulated scatter functions”.
    In: J. Nucl. Med. 31.9 (1990), pp. 1560–1567.
    [141] G. Poludniowski et al. “An efficient Monte Carlo-based algorithm for
    scatter correction in keV cone-beam CT”. In: Phys. Med. Biol. 54.12 (2009),
    pp. 3847–3864.
    [142] OpenGATE Collaboration. http://www.opengatecollaboration.org. Accessed
    2023.
    [143] GATE User Guide, Version 4.0.0. 2008.
    [144] S. Agostinelli et al. “GEANT4-a simulation toolkit”. In: Nuclear Instruments
    & Methods in Physics Research Section A-Accelerators Spectrometers
    Detectors and Associated Equipment 506.3 (2003), pp. 250–303.
    [145] Sébastien Jan et al. “GATE: a simulation toolkit for PET and SPECT”. In:
    Physics in Medicine & Biology 49.19 (2004), p. 4543.
    [146] GATE User Guide, Version 4.0.0. 2008.
    [147] Michel Defrise, Paul E Kinahan, and Christian J Michel. “Image reconstruction
    algorithms in PET”. In: Positron emission tomography: basic sciences.
    Springer, 2005, pp. 60–68.
    [148] TH Farquhar et al. “An investigation of filter choice for filtered backprojection
    reconstruction in PET”. In: IEEE transactions on nuclear science
    45.3 (1998), pp. 1133–1137.
    [149] Arthur P Dempster, Nan MLaird, and Donald B Rubin. “Maximum likelihood
    from incomplete data via the EM algorithm”. In: Journal of the royal
    statistical society: series B (methodological) 39.1 (1977), pp. 1–22.
    [150] Lawrence A Shepp and Yehuda Vardi. “Maximum likelihood reconstruction
    for emission tomography”. In: IEEE transactions on medical imaging
    1.2 (1982), pp. 113–122.
    [151] Yilong Liu et al. “System response matrix calculation using symmetries
    for dual-head PET scanners”. In: International journal of imaging systems
    and technology 23.3 (2013), pp. 205–214.
    [152] M. Rafecas et al. “Effect of noise in the probability matrix used for statistical
    reconstruction of PET data”. In: IEEE Transactions on Nuclear Science
    51.4 (2004), pp. 149–156.
    184
    [153] J. Qi et al. “High-resolution 3D Bayesian image reconstruction using the
    microPET small-animal scanner”. In: Physics in Medicine and Biology 43
    (1998), pp. 1001–1013.
    [154] C. Kao et al. “Image reconstruction of a dual-head small-animal PET system
    by usingMCcomputed system response matrix”. In: 9th International
    Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and
    Nuclear Medicine. 2007, pp. 398–401.
    [155] Adam M Alessio, Paul E Kinahan, and Thomas K Lewellen. “Modeling
    and incorporation of system response functions in 3-D whole body PET”.
    In: IEEE Transactions on Medical Imaging 25.7 (2006), pp. 828–837.
    [156] Robert L Siddon. “Fast calculation of the exact radiological path for a
    three-dimensional CT array”. In: Medical physics 12.2 (1985), pp. 252–255.
    [157] Huaxia Zhao and Andrew J Reader. “Fast ray-tracing technique to calculate
    line integral paths in voxel arrays”. In: 2003 IEEE Nuclear Science Symposium.
    Conference Record (IEEE Cat. No. 03CH37515). Vol. 4. IEEE. 2003,
    pp. 2808–2812.
    [158] Jeffrey A Fessler and Alfred O Hero. “Penalized maximum-likelihood
    image reconstruction using space-alternating generalized EM algorithms”.
    In: IEEE Transactions on Image Processing 4.10 (1994), pp. 1417–
    1429. DOI: 10.1109/83.334981.
    [159] Marcel Beister, Daniel Kolditz, and Willi A Kalender. “Iterative reconstruction
    methods in X-ray CT”. In: Physica Medica 28.2 (2012), pp. 94–
    108. DOI: 10.1016/j.ejmp.2012.01.003.
    [160] L A Shepp and Y Vardi. “Maximum likelihood reconstruction for emission
    tomography”. In: IEEE Transactions on Medical Imaging 1.2 (1982),
    pp. 113–122. DOI: 10.1109/TMI.1982.4307558.
    [161] Richard Leahy and Xiaolan Yan. “Incorporation of anatomical MR data
    for improved functional imaging with PET”. In: Information Processing in
    Medical Imaging. Springer. 1991, pp. 105–120. DOI: 10.1007/978-1-
    4615-3724-3_8.
    [162] K J La Croix et al. “Investigation of the use of X-ray CT images for attenuation
    correction in SPECT”. In: IEEE Transactions on Nuclear Science 41.6
    (1994), pp. 2793–2799. DOI: 10.1109/23.340642.
    [163] Michael A King, Benjamin M Tsui, and Tinsu S Pan. “Attenuation compensation
    for cardiac single-photon emission computed tomographic
    imaging”. In: Journal of Nuclear Cardiology 2.1 (1995), pp. 18–29. DOI: 10.
    1007/BF03041821.
    185
    [164] H M Hudson and R S Larkin. “Accelerated image reconstruction using
    ordered subsets of projection data”. In: IEEE Transactions on Medical Imaging
    13.4 (1994), pp. 601–609. DOI: 10.1109/42.363108.
    [165] Peter J Green. “Bayesian reconstructions from emission tomography data
    using a modified EM algorithm”. In: IEEE Transactions on Medical Imaging
    9.1 (1990), pp. 84–93. DOI: 10.1109/42.52985.
    [166] WD Newhauser and R Zhang. “The physics of proton therapy”. In: Phys
    Med Biol 60.8 (Apr. 2015).
    [167] Scholz. “State of the Art and Future Prospects of Ion Beam Therapy:
    Physical and Radiobiological Aspects”. In: IEEE Transactions on Radiation
    and Plasma Medical Sciences 4.2 (Mar. 2020).
    [168] H. Paganetti. “Range uncertainties in proton therapy and the role of
    Monte Carlo simulations”. In: Phys. Med. Biol. 57 (2012), pp. 99–117.
    [169] McGowan SE, Burnet NG, and Lomax AJ. “Treatment planning optimisation
    in proton therapy”. In: Br J Radiol 86 (2013), pp. 2012–2088.
    [170] H D Maccabee, U Madhvanath, and M R Raju. “Tissue activation studies
    with alpha-particle beams”. In: Phys. Med. Biol. 14 (1969), p. 213.
    [171] G.W. Bennett et al. “Beam localization via 15O activation in protonradiation
    therapy”. In: Nucl. Instrum. Methods 125 (1975), pp. 333–8.
    [172] Enghardt W et al. “The spatial distribution of positron-emitting nuclei
    generated by relativistic light ion beams in organic matter”. In: Phys. Med.
    Biol. 37 (1992).
    [173] K. Parodi and W. Enghardt. “Potential application of PET in quality assurance
    of proton therapy”. In: Phys. Med. Biol. 45 (2000), pp. 151–170.
    [174] K. Parodi et al. “PET/CT imaging for treatment verification after proton
    therapy: A study with plastic phantoms and metallic implants”. In: Med
    Phys. 34.2 (2007).
    [175] A. Miyatake et al. “Measurement and verification of positron emitter nuclei
    generated at each treatment site by target nuclear fragment reactions
    in proton therapy”. In: Med. Phys (10, volume = 37, number = 8, month =
    8).
    [176] Y. Shao et al. “In-beam PET imaging for on-line adaptive proton therapy:
    an initial phantom study”. In: Phys. Med. Biol. 59 (2014).
    [177] M. A. Piliero et al. “First results of the INSIDE in-beam PET scanner
    for the on-line monitoring of particle therapy treatments”. In: JINST 11
    (2016), p. C12011.
    186
    [178] S. Binet et al. “Construction and First Tests of an in-beam PET Demonstrator
    Dedicated to the Ballistic Control of Hadrontherapy Treatments
    With 65 MeV Protons”. In: IEEE Transactions on Radiation and Plasma Medical
    Sciences 2.1 (Jan. 2018).
    [179] H. Tashima et al. “Development of a multi-use human-scale single-ring
    OpenPET system”. In: IEEE Transactions on Radiation and Plasma Medical
    Sciences (Nov. 2020).
    [180] P. Crespo et al. “Suppression of Random coincidences during in-beam
    PET measurements at ion beam radiotherapy facilities”. In: IEEE Trans
    Nucl Sci 52 (2005), p. 980.
    [181] H. J. T. Buitenhuis et al. “Beam-on imaging of short-lived positron emitters
    during proton therapy”. In: Phys Med Biol 62 (2017), p. 4654.
    [182] J. J. R. Frausto da Silva and R. Williams. The Biological Chemistry of the
    Elements. 1991. ISBN: 0198508484.
    [183] X. Zhu and G. El Fakhri. “Proton Therapy Verification with PET Imaging”.
    In: Theranostics 3.10 (2013).
    [184] A Knopf et al. “Systematic analysis of biological and physical limitations
    of proton beam range verification with offline PET/CT scans”. In: Phys.
    Med. Biol. 54 (2009), pp. 4477–4495.
    [185] P Dendooven et al. “Short-lived positron emitters in beam-on PET imaging
    during proton therapy”. In: Phys. Med. Biol. 60 (2015), pp. 8923–8947.
    [186] P Dendooven et al. “Corrigendum: Short-lived positron emitters in
    beam-on PET imaging during proton therapy”. In: Phys. Med. Biol. 64
    (2019), p. 129501.
    [187] Harion et al. “STiC - a mixed mode silicon photomultiplier readout ASIC
    for time-of-flight applications”. In: JINST 9 (2014), p. C02003.
    [188] W. Enghardt et al. “Charged hadron tumour therapy monitoring by
    means of PET”. In: Nuclear Instruments and Methods in Physics Research
    A 525 (2004), pp. 284–288.
    [189] National Institute of Standards and Technology (NIST). Stopping-Power
    and Range Tables for Protons. Accessed: current year. URL: https : / /
    physics.nist.gov/cgi-bin/Star/ap_table.pl.
    [190] Ozoemelam et al. “Feasibility of quasi-prompt PET-based range verification
    in proton therapy”. In: Phys. Med. Biol. 65 (2020), p. 245013.
    [191] M. T. Studenski and Y. Xiao. “Proton therapy dosimetry using positron
    emission tomography”. In: World Journal of Radiology 2.4 (Apr. 2010),
    pp. 135–142.
    187
    [192] F. Horst et al. “Measurement of PET isotope production cross sections for
    protons and carbon ions on carbon and oxygen targets for applications
    in particle therapy range verification”. In: Physics in Medicine and Biology
    64.16 (2019), p. 205012.
    [193] A. Bongrand et al. “Use of short-lived positron emitters for in-beam and
    real-time β+ range monitoring in proton therapy”. In: Physica Medica 69
    (2020), pp. 248–255.
    [194] R. L. Siddon. “Fast calculation of the exact radiological path for a 3-
    dimensional CT array”. In: Medical Physics 12 (1985), p. 229.
    [195] H. Wang et al. “Hypoxic Radioresistance: Can ROS Be the Key to Overcome
    It?” In: Cancers 11 (112 2019).
    [196] Hsiao-Ming Lu. “A potential method for in vivo range verification in
    proton therapy treatment”. In: Physics in Medicine & Biology 53.5 (2008),
    p. 1413.
    [197] David A Watts et al. “A proton range telescope for quality assurance
    in hadrontherapy”. In: 2009 IEEE Nuclear Science Symposium Conference
    Record (NSS/MIC). IEEE. 2009, pp. 4163–4166.
    [198] K Parodi. “Potential application of PET in quality assurance of proton
    therapy”. In: 45.11 (Oct. 2000), pp. 151–156. DOI: 10.1088/0031-9155/
    45/11/403. URL: https://doi.org/10.1088/0031-9155/45/
    11/403.
    [199] Chul-Hee Min et al. “Prompt gamma measurements for locating the dose
    falloff region in the proton therapy”. In: Applied physics letters 89.18 (2006),
    p. 183517.
    [200] Harald Paganetti. “Range uncertainties in proton therapy and the role
    of Monte Carlo simulations”. In: 57.11 (May 2012), pp. 99–117. DOI: 10.
    1088/0031-9155/57/11/r99. URL: https://doi.org/10.1088/
    0031-9155/57/11/r99.
    [201] Katia Parodi et al. “Patient Study of In Vivo Verification of Beam Delivery
    and Range, Using Positron Emission Tomography and Computed
    Tomography Imaging After Proton Therapy”. In: International Journal of
    Radiation Oncology Biology Physics 68.3 (2007), pp. 920–934. ISSN: 0360-
    3016. DOI: https://doi.org/10.1016/j.ijrobp.2007.01.063.
    URL: https://www.sciencedirect.com/science/article/
    pii/S036030160700377X.
    [202] A.M.J. Paans and J.M. Schippers. “Proton therapy in combination with
    PET as monitor: a feasibility study”. In: IEEE Transactions on Nuclear Science
    40.4 (1993), pp. 1041–1044. DOI: 10.1109/23.256709.
    188
    [203] Juan José Vaquero and Paul Kinahan. “Positron Emission Tomography:
    Current Challenges and Opportunities for Technological Advances in
    Clinical and Preclinical Imaging Systems”. In: Annual Review of Biomedical
    Engineering 17.1 (2015). PMID: 26643024, pp. 385–414. DOI: 10.1146/
    annurev- bioeng- 071114- 040723. eprint: https://doi.org/
    10.1146/annurev-bioeng-071114-040723. URL: https://doi.
    org/10.1146/annurev-bioeng-071114-040723.
    [204] Adam Alessio, Ken Sauer, and Paul Kinahan. “Analytical reconstruction
    of deconvolved Fourier rebinned PET sinograms”. In: Physics in medicine
    & biology 51.1 (2005), p. 77.
    [205] Shan Tong, Adam M Alessio, and Paul E Kinahan. “Image reconstruction
    for PET/CT scanners: past achievements and future challenges”. In:
    Imaging in medicine 2.5 (Oct. 2010), pp. 529–545. ISSN: 1755–5191. DOI:
    10.2217/iim.10.49.
    [206] A. Iriarte et al. “System models for PET statistical iterative reconstruction:
    A review”. In: Computerized Medical Imaging and Graphics
    48 (2016), pp. 30–48. ISSN: 0895–6111. DOI: https : / / doi . org /
    10 . 1016 / j . compmedimag . 2015 . 12 . 003. URL: https :
    / / www . sciencedirect . com / science / article / pii /
    S0895611115001901.
    [207] H Paganetti and G El Fakhri. “Monitoring proton therapy with PET”. In:
    The British journal of radiology 88.1051 (2015), pp. 2015–2173.
    [208] W. Enghardt et al. “Dose quantification from in-beam positron emission
    tomography”. In: Radiotherapy and Oncology 73 (2004). Carbon-Ion Theraphy,
    S96–S98. ISSN: 0167–8140. DOI: https://doi.org/10.1016/
    S0167-8140(04)80024-0. URL: https://www.sciencedirect.
    com/science/article/pii/S0167814004800240.
    [209] Fine Fiedler et al. “On the effectiveness of ion range determination from
    in-beam PET data”. In: Physics in Medicine & Biology 55.7 (2010), p. 1989.
    [210] E Fourkal, J Fan, and I Veltchev. “Absolute dose reconstruction in proton
    therapy using PET imaging modality: feasibility study”. In: 54.11 (May
    2009), pp. 217–228. DOI: 10.1088/0031- 9155/54/11/n02. URL:
    https://doi.org/10.1088/0031-9155/54/11/n02.
    [211] Katia Parodi and Thomas Bortfeld. “A filtering approach based on Gaussian–
    powerlaw convolutions for local PET verification of proton radiotherapy”.
    In: 51.8 (Mar. 2006), pp. 1991–2009. DOI: 10 . 1088 / 0031 -
    9155/51/8/003. URL: https://doi.org/10.1088/0031-9155/
    51/8/003.
    189
    [212] F Attanasi et al. “Extension and validation of an analytical model for invivo
    PET verification of proton therapy: a phantom and clinical study”.
    In: 56.16 (July 2011), pp. 5079–5098. DOI: 10.1088/0031-9155/56/
    16/001. URL: https://doi.org/10.1088/0031-9155/56/16/
    001.
    [213] Steffen Remmele et al. “A deconvolution approach for PET-based dose
    reconstruction in proton radiotherapy”. In: 56.23 (Nov. 2011), pp. 7601–
    7619. DOI: 10.1088/0031-9155/56/23/017. URL: https://doi.
    org/10.1088/0031-9155/56/23/017.
    [214] Takamitsu Masuda et al. “MLEM algorithm for dose estimation using
    PET in proton therapy”. In: 64.17 (Sept. 2019), pp. 175–200. DOI: 10 .
    1088/1361- 6560/ab3276. URL: https://doi.org/10.1088/
    1361-6560/ab3276.
    [215] Chuang Liu et al. “Range and dose verification in proton therapy using
    proton-induced positron emitters and recurrent neural networks
    (RNNs)”. In: 64.17 (Sept. 2019). DOI: 10.1088/1361-6560/ab3564.
    URL: https://doi.org/10.1088/1361-6560/ab3564.
    [216] Zongsheng Hu et al. “A machine learning framework with anatomical
    prior for online dose verification using positron emitters and PET in proton
    therapy”. In: 65.18 (Sept. 2020), p. 185003. DOI: 10.1088/1361-
    6560/ab9707. URL: https://doi.org/10.1088/1361- 6560/
    ab9707.
    [217] Xiaoke Zhang et al. “Dose calculation in proton therapy using a discovery
    cross-domain generative adversarial network (DiscoGAN)”. In: Medical
    Physics 48.5 (2021), pp. 2646–2660.
    [218] Ida Häggström et al. “DeepPET: A deep encoder–decoder network for
    directly solving the PET image reconstruction inverse problem”. In:
    Medical Image Analysis 54 (2019), pp. 253–262. ISSN: 1361-8415. DOI:
    https : / / doi . org / 10 . 1016 / j . media . 2019 . 03 . 013. URL:
    https://www.sciencedirect.com/science/article/pii/
    S1361841518305838.
    [219] Olaf Ronneberger and Thomas Fischer Philippand Brox. “U-Net: Convolutional
    Networks for Biomedical Image Segmentation”. In: Medical Image
    Computing and Computer-Assisted Intervention – MICCAI 2015. Ed. by
    Nassir Navab et al. Springer International Publishing, 2015, pp. 234–241.
    [220] Zhong Su et al. “Evaluations of a flat-panel based compact daily quality
    assurance device for proton pencil beam scanning (PBS) system”. In:
    Physica Medica 80 (2020), pp. 243–250.
    190
    [221] Ikechi Ozoemelam et al. “Feasibility of quasi-prompt PET-based range
    verification in proton therapy”. In: Physics in Medicine & Biology 65.24
    (2020), p. 245013.
    [222] U Oelfke, G K Y Lam, and M S Atkins. “Proton dose monitoring with
    PET: quantitative studies in Lucite.” In: 41.1 (Jan. 1996), pp. 177–196. DOI:
    10.1088/0031-9155/41/1/013. URL: https://doi.org/10.
    1088/0031-9155/41/1/013.
    [223] Phillip Isola et al. “Image-to-Image Translation with Conditional Adversarial
    Networks”. In: 2017 IEEE Conference on Computer Vision and Pattern
    Recognition (CVPR) (2017), pp. 5967–5976.
    [224] S Jan et al. “GATE V6: a major enhancement of the GATE simulation platform
    enabling modelling of CT and radiotherapy”. In: 56.4 (Jan. 2011),
    pp. 881–901. DOI: 10.1088/0031- 9155/56/4/001. URL: https:
    //doi.org/10.1088/0031-9155/56/4/001.
    [225] L Grevillot et al. “A Monte Carlo pencil beam scanning model for proton
    treatment plan simulation using GATE/GEANT4”. In: 56.16 (July 2011),
    pp. 5203–5219. DOI: 10.1088/0031-9155/56/16/008. URL: https:
    //doi.org/10.1088/0031-9155/56/16/008.
    [226] Wilfried Schneider, Thomas Bortfeld, and Wolfgang Schlegel. “Correlation
    between CT numbers and tissue parameters needed for Monte Carlo
    simulations of clinical dose distributions”. In: 45.2 (Jan. 2000), pp. 459–
    478. DOI: 10.1088/0031-9155/45/2/314. URL: https://doi.
    org/10.1088/0031-9155/45/2/314.
    [227] Carla Winterhalter et al. “Evaluation of GATE-RTion (GATE/Geant4)
    Monte Carlo simulation settings for proton pencil beam scanning quality
    assurance”. In: Medical Physics 47.11 (2020), pp. 5817–5828.
    [228] Thomas Bortfeld and Wolfgang Schlegel. “An analytical approximation
    of depth-dose distributions for therapeutic proton beams”. In: Physics in
    Medicine & Biology 41.8 (1996), p. 1331.
    [229] David Jette andWeimin Chen. “Creating a spread-out Bragg peak in proton
    beams”. In: Physics in Medicine & Biology 56.11 (2011), N131.
    [230] Asako Kanezaki et al. “Deep learning for multimodal data fusion”. In:
    Multimodal Scene Understanding. Elsevier, 2019, pp. 20–22.
    [231] Subhadip Mukherjee et al. “End-to-end reconstruction meets data-driven
    regularization for inverse problems”. In: Advances in Neural Information
    Processing Systems. Ed. by M. Ranzato et al. Vol. 34. Curran Associates,
    Inc., 2021, pp. 21413–21425. URL: https://proceedings.neurips.
    cc/paper/2021/file/b2df0a0d4116c55f81fd5aa1ef876510-
    Paper.pdf.
    191
    [232] Espagna S. and Paganetti H. “The impact of uncertainties in the CT conversion
    algorithm when predicting proton beam ranges in patients from
    dose and PET-activity distributions”. In: Physics in Medicine & Biology 55
    (2010), pp. 7557–7571.DOI: 10.1088/0031-9155/55/24/011.

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