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1

Acton, Paul D., Hongming Zhuang, and Abass Alavi. "Quantification in PET." Radiologic Clinics of North America 42, no. 6 (2004): 1055–62. http://dx.doi.org/10.1016/j.rcl.2004.08.010.

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2

Lammertsma, Adriaan A. "Quantification of PET Studies." Journal of Nuclear Cardiology 26, no. 6 (2019): 2045–47. http://dx.doi.org/10.1007/s12350-018-01583-x.

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3

Bussink, J. "Quantification of tumour hypoxia." Nuklearmedizin 49, S 01 (2010): S37—S40. http://dx.doi.org/10.1055/s-0038-1626532.

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SummaryTumor cell hypoxia is considered one of the important causes for radiation resistance. The introduction of IMRT (intensity modulated radiotherapy) allows specific boosting of tumor subvolumes that may harbour these radioresistant tumour cells. PET imaging of these subvolumes can be incorporated into treatment planning.However, at this moment microenvironmental changes visualized and quantified by means of PET-imaging need to be validated by highresolution microscopic techniques. This will allow interpretation of imaging techniques with intermediate resolution (such as PET/CT) in relatio
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4

Hofheinz, F., G. Schramm, L. Oehme, et al. "Evaluation of PET quantification accuracy in vivo." Nuklearmedizin 53, no. 03 (2014): 67–77. http://dx.doi.org/10.3413/nukmed-0588-13-05.

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SummaryQuantitative positron emission tomography (PET) requires accurate scanner calibration, which is commonly performed using phantoms. It is not clear to what extent this procedure ensures quantitatively correct results in vivo, since certain conditions differ between phantom and patient scans. Aim: We, therefore, have evaluated the actual quantification accuracy in vivo of PET under clinical routine conditions. Patients, methods: We determined the activity concentration in the bladder in patients undergoing routine [18F]FDG whole body investigations with three different PET scanners (Sieme
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5

Ferrando, Ornella, Franca Foppiano, Tindaro Scolaro, Chiara Gaeta, and Andrea Ciarmiello. "PET/CT images quantification for diagnostics and radiotherapy applications." Journal of Diagnostic Imaging in Therapy 2, no. 1 (2015): 18–29. http://dx.doi.org/10.17229/jdit.2015-0216-013.

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6

Rogasch, Julian M. M., Frank Hofheinz, Lutz van Heek, Conrad-Amadeus Voltin, Ronald Boellaard, and Carsten Kobe. "Influences on PET Quantification and Interpretation." Diagnostics 12, no. 2 (2022): 451. http://dx.doi.org/10.3390/diagnostics12020451.

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Various factors have been identified that influence quantitative accuracy and image interpretation in positron emission tomography (PET). Through the continuous introduction of new PET technology—both imaging hardware and reconstruction software—into clinical care, we now find ourselves in a transition period in which traditional and new technologies coexist. The effects on the clinical value of PET imaging and its interpretation in routine clinical practice require careful reevaluation. In this review, we provide a comprehensive summary of important factors influencing quantification and inte
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7

Van Camp, Nadja, Yaël Balbastre, Anne-Sophie Herard, et al. "Assessment of simplified methods for quantification of [18F]-DPA-714 using 3D whole-brain TSPO immunohistochemistry in a non-human primate." Journal of Cerebral Blood Flow & Metabolism 40, no. 5 (2019): 1103–16. http://dx.doi.org/10.1177/0271678x19859034.

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The 18 kDa translocator protein (TSPO) is the main molecular target to image neuroinflammation by positron emission tomography (PET). However, TSPO-PET quantification is complex and none of the kinetic modelling approaches has been validated using a voxel-by-voxel comparison of TSPO-PET data with the actual TSPO levels of expression. Here, we present a single case study of binary classification of in vivo PET data to evaluate the statistical performance of different TSPO-PET quantification methods. To that end, we induced a localized and adjustable increase of TSPO levels in a non-human primat
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Lawal, Ismaheel O., Gbenga O. Popoola, Johncy Mahapane, et al. "[68Ga]Ga-Pentixafor for PET Imaging of Vascular Expression of CXCR-4 as a Marker of Arterial Inflammation in HIV-Infected Patients: A Comparison with 18F[FDG] PET Imaging." Biomolecules 10, no. 12 (2020): 1629. http://dx.doi.org/10.3390/biom10121629.

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People living with human immunodeficiency virus (PLHIV) have excess risk of atherosclerotic cardiovascular disease (ASCVD). Arterial inflammation is the hallmark of atherogenesis and its complications. In this study we aimed to perform a head-to-head comparison of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG PET/CT) and Gallium-68 pentixafor positron emission tomography/computed tomography [68Ga]Ga-pentixafor PET/CT for quantification of arterial inflammation in PLHIV. We prospectively recruited human immunodeficiency virus (HIV)-infected patients t
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9

Frost, J. J. "Receptor localization and quantification with PET." Radiology 169, no. 1 (1988): 273–74. http://dx.doi.org/10.1148/radiology.169.1.3262227.

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10

Walker, Matthew D., and Vesna Sossi. "Commentary: An Eye on PET Quantification." Molecular Imaging and Biology 17, no. 1 (2014): 1–3. http://dx.doi.org/10.1007/s11307-014-0791-7.

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11

Walker, Matthew D., Jonathan I. Gear, Allison J. Craig, and Daniel R. McGowan. "Effects of Respiratory Motion on Y-90 PET Dosimetry for SIRT." Diagnostics 12, no. 1 (2022): 194. http://dx.doi.org/10.3390/diagnostics12010194.

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Respiratory motion degrades the quantification accuracy of PET imaging by blurring the radioactivity distribution. In the case of post-SIRT PET-CT verification imaging, respiratory motion can lead to inaccuracies in dosimetric measures. Using an anthropomorphic phantom filled with 90Y at a range of clinically relevant activities, together with a respiratory motion platform performing realistic motions (10–15 mm amplitude), we assessed the impact of respiratory motion on PET-derived post-SIRT dosimetry. Two PET scanners at two sites were included in the assessment. The phantom experiments showe
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12

Shah, Jay, Yiming Che, Javad Sohankar, et al. "Enhancing Amyloid PET Quantification: MRI-Guided Super-Resolution Using Latent Diffusion Models." Life 14, no. 12 (2024): 1580. https://doi.org/10.3390/life14121580.

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Amyloid PET imaging plays a crucial role in the diagnosis and research of Alzheimer’s disease (AD), allowing non-invasive detection of amyloid-β plaques in the brain. However, the low spatial resolution of PET scans limits the accurate quantification of amyloid deposition due to partial volume effects (PVE). In this study, we propose a novel approach to addressing PVE using a latent diffusion model for resolution recovery (LDM-RR) of PET imaging. We leverage a synthetic data generation pipeline to create high-resolution PET digital phantoms for model training. The proposed LDM-RR model incorpo
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13

Veronese, Mattia, Gaia Rizzo, Alessandra Bertoldo, and Federico E. Turkheimer. "Spectral Analysis of Dynamic PET Studies: A Review of 20 Years of Method Developments and Applications." Computational and Mathematical Methods in Medicine 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/7187541.

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In Positron Emission Tomography (PET), spectral analysis (SA) allows the quantification of dynamic data by relating the radioactivity measured by the scanner in time to the underlying physiological processes of the system under investigation. Among the different approaches for the quantification of PET data, SA is based on the linear solution of the Laplace transform inversion whereas the measured arterial and tissue time-activity curves of a radiotracer are used to calculate the input response function of the tissue. In the recent years SA has been used with a large number of PET tracers in b
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14

Pinto, Samara Oliveira, Paulo R. R. V. Caribe, Lucas Narciso, and Ana Maria Marques da Silva. "Optimization of reconstruction parameters in [18F]FDG PET brain images aiming scan time reduction." Revista Brasileira de Física Médica 15 (July 13, 2021): 611. http://dx.doi.org/10.29384/rbfm.2021.v15.19849001611.

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Iterative image reconstruction methods are widely used in PET due to their better image quality when compared to analytical methods. However, inaccurate quantification occurs in low activity concentration regions, which leads to biased quantification of PET images. The diagnosis of some neurodegenerative diseases, such as Alzheimer’s disease, is based on identifying such low-uptake regions. Furthermore, PET imaging in these populations should be as short as possible to limit head movements and improve patient comfort. This work aims to identify optimized reconstruction parameters of [18F]FDG P
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15

Becker, Guillaume, Sylvestre Dammicco, Mohamed Ali Bahri, and Eric Salmon. "The Rise of Synaptic Density PET Imaging." Molecules 25, no. 10 (2020): 2303. http://dx.doi.org/10.3390/molecules25102303.

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Many neurological disorders are related to synaptic loss or pathologies. Before the boom of positrons emission tomography (PET) imaging of synapses, synaptic quantification could only be achieved in vitro on brain samples after autopsy or surgical resections. Until the mid-2010s, electron microscopy and immunohistochemical labelling of synaptic proteins were the gold-standard methods for such analyses. Over the last decade, several PET radiotracers for the synaptic vesicle 2A protein have been developed to achieve in vivo synapses visualization and quantification. Different strategies were use
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16

Nowak, B., H. J. Kaiser, S. Block, et al. "An approach for comparative quantification of myocardial blood flow (0-15-H2O-PET), perfusion (Tc-99m-tetrofosmin-SPECT), and metabolism (F 18-FDG-PET)." Nuklearmedizin 40, no. 05 (2001): 164–71. http://dx.doi.org/10.1055/s-0038-1623882.

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Summary Aim: In the present study a new approach has been developed for comparative quantification of absolute myocardial blood flow (MBF), myocardial perfusion, and myocardial metabolism in short-axis slices. Methods: 42 patients with severe CAD, referred for myocardial viability diagnostics, were studied consecutively with 0-15-H2O PET (H2O-PET) (twice), Tc-99m-Tetrofosmin 5PECT (TT-SPECT) and F-18-FDG PET (FDG-PET). All dato sets were reconstructed using attenuation correction and reoriented into short axis slices. Each heart was divided into three representative slices (base, rnidventricul
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17

Mansor, S., R. Boellaard, F. E. Froklage, et al. "Quantification of Dynamic 11C-Phenytoin PET Studies." Journal of Nuclear Medicine 56, no. 9 (2015): 1372–77. http://dx.doi.org/10.2967/jnumed.115.158055.

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18

Zaidi, Habib, and Nicolas Karakatsanis. "Towards enhanced PET quantification in clinical oncology." British Journal of Radiology 91, no. 1081 (2018): 20170508. http://dx.doi.org/10.1259/bjr.20170508.

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19

Barker, W. C., L. P. Szajek, S. L. Green, and R. E. Carson. "Improved quantification for Tc-94m PET imaging." IEEE Transactions on Nuclear Science 48, no. 3 (2001): 739–42. http://dx.doi.org/10.1109/23.940156.

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20

Zaidi, Habib, and Abass Alavi. "Trends in PET quantification: opportunities and challenges." Clinical and Translational Imaging 2, no. 3 (2014): 183–85. http://dx.doi.org/10.1007/s40336-014-0065-z.

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21

Bettinardi, V., I. Castiglioni, E. De Bernardi, and M. C. Gilardi. "PET quantification: strategies for partial volume correction." Clinical and Translational Imaging 2, no. 3 (2014): 199–218. http://dx.doi.org/10.1007/s40336-014-0066-y.

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22

de Jong, Hugo W. A. M., and Mark Lubberink. "Issues in quantification of cardiac PET studies." European Journal of Nuclear Medicine and Molecular Imaging 34, no. 3 (2006): 316–19. http://dx.doi.org/10.1007/s00259-006-0283-3.

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23

Mawlawi, Osama, S. Kappadath, Tinsu Pan, Eric Rohren, and Homer Macapinlac. "Factors Affecting Quantification in PET/CT Imaging." Current Medical Imaging Reviews 4, no. 1 (2008): 34–45. http://dx.doi.org/10.2174/157340508783502778.

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24

Antunovic, Lidija, Marcello Rodari, Pietro Rossi, and Arturo Chiti. "Standardization and Quantification in PET/CT Imaging." PET Clinics 9, no. 3 (2014): 259–66. http://dx.doi.org/10.1016/j.cpet.2014.03.002.

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25

Veldman, Emma R., Andrea Varrone, Katarina Varnäs, et al. "Serotonin 1B receptor density mapping of the human brainstem using positron emission tomography and autoradiography." Journal of Cerebral Blood Flow & Metabolism 42, no. 4 (2021): 630–41. http://dx.doi.org/10.1177/0271678x211049185.

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The serotonin 1B (5-HT1B) receptor has lately received considerable interest in relation to psychiatric and neurological diseases, partly due to findings based on quantification using Positron Emission Tomography (PET). Although the brainstem is an important structure in this regard, PET radioligand binding quantification in brainstem areas often shows poor reliability. This study aims to improve PET quantification of 5-HT1B receptor binding in the brainstem. Volumes of interest (VOIs) were selected based on a 3D [3H]AZ10419369 Autoradiography brainstem model, which visualized 5-HT1B receptor
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26

Veldman, Emma R., Andrea Varrone, Katarina Varnäs, et al. "Serotonin 1B receptor density mapping of the human brainstem using positron emission tomography and autoradiography." Journal of Cerebral Blood Flow & Metabolism 42, no. 4 (2021): 630–41. http://dx.doi.org/10.1177/0271678x211049185.

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The serotonin 1B (5-HT1B) receptor has lately received considerable interest in relation to psychiatric and neurological diseases, partly due to findings based on quantification using Positron Emission Tomography (PET). Although the brainstem is an important structure in this regard, PET radioligand binding quantification in brainstem areas often shows poor reliability. This study aims to improve PET quantification of 5-HT1B receptor binding in the brainstem. Volumes of interest (VOIs) were selected based on a 3D [3H]AZ10419369 Autoradiography brainstem model, which visualized 5-HT1B receptor
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27

Elmenhorst, David, Luciano Minuzzi, Antonio Aliaga, et al. "In vivo and in vitro Validation of Reference Tissue Models for the mGluR5 Ligand [11C]ABP688." Journal of Cerebral Blood Flow & Metabolism 30, no. 8 (2010): 1538–49. http://dx.doi.org/10.1038/jcbfm.2010.65.

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The primary objective of this study was to verify the suitability of reference tissue-based quantification methods of the metabotropic glutamate receptor type 5 (mGluR5) with [11C]ABP688. This study presents in vivo (Positron Emission Tomography (PET)) and in vitro (autoradiography) measurements of mGluR5 densities in the same rats and evaluates both noninvasive and blood-dependent pharmacokinetic models for the quantification of [11C]ABP688 binding. Eleven rats underwent [11C]ABP688 PET scans. In five animals, baseline scans were compared with blockade experiments with the antagonist 1,2-meth
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28

Cnossen, Trijntje T., Watske Smit, Constantijn J. A. M. Konings, Jeroen P. Kooman, Karel M. Leunissen, and Raymond T. Krediet. "Quantification of Free Water Transport during the Peritoneal Equilibration Test." Peritoneal Dialysis International: Journal of the International Society for Peritoneal Dialysis 29, no. 5 (2009): 523–27. http://dx.doi.org/10.1177/089686080902900509.

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Objective Free water transport (FWT) can be calculated after a dwell of 1 hour with a 3.86% glucose solution using sodium kinetics (mini-PET, as developed by LaMilia et al.). This requires measurement of the intraperitoneal volume after drainage of the abdomen. Since valuable information of a 4-hour peritoneal equilibration test (PET) may be lost, the aim of the present study was to investigate whether temporary drainage of the peritoneal cavity after 1 hour and re-instillation thereafter would influence the results of the 4-hour PET. Methods and Patients Two PETs were performed in 10 stable p
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29

Kuttner, Samuel, Martin Lyngby Lassen, Silje Kjærnes Øen, Rune Sundset, Thomas Beyer, and Live Eikenes. "Quantitative PET/MR imaging of lung cancer in the presence of artifacts in the MR-based attenuation correction maps." Acta Radiologica 61, no. 1 (2019): 11–20. http://dx.doi.org/10.1177/0284185119848118.

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Background Positron emission tomography (PET)/magnetic resonance (MR) imaging may become increasingly important for assessing tumor therapy response. A prerequisite for quantitative PET/MR imaging is reliable and repeatable MR-based attenuation correction (AC). Purpose To investigate the frequency and test–retest reproducibility of artifacts in MR-AC maps in a lung cancer patient cohort and to study the impact of artifact corrections on PET-based tumor quantification. Material and Methods Twenty-five lung cancer patients underwent single-day, test–retest, 18F-fluorodeoxyglucose (FDG) PET/MR im
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30

Torrado-Carvajal, A., L. García-Cañamaque, and N. Malpica. "Teaching a hands-on session on simultaneous Positron Emission Tomography and Magnetic Resonance image acquisition and quantification in a clinical setting." Journal of Instrumentation 19, no. 04 (2024): C04028. http://dx.doi.org/10.1088/1748-0221/19/04/c04028.

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Abstract Positron Emission Tomography/Magnetic Resonance (PET/MR) imaging, is a novel imaging modality that combines the capabilities of two powerful imaging techniques in a single acquisition. This unique integration allows for simultaneous acquisition of both metabolic and structural data within a single imaging session. In this sense, PET/MR offers a comprehensive and innovative approach to medical imaging, but accompanied by its intrinsic physics and engineering complexity, involving intricate synchronization of high-performance detectors, electromagnetic shielding, and sophisticated corre
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31

Weyts, Kathleen, Elske Quak, Idlir Licaj, et al. "Deep Learning Denoising Improves and Homogenizes Patient [18F]FDG PET Image Quality in Digital PET/CT." Diagnostics 13, no. 9 (2023): 1626. http://dx.doi.org/10.3390/diagnostics13091626.

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Given the constant pressure to increase patient throughput while respecting radiation protection, global body PET image quality (IQ) is not satisfactory in all patients. We first studied the association between IQ and other variables, in particular body habitus, on a digital PET/CT. Second, to improve and homogenize IQ, we evaluated a deep learning PET denoising solution (Subtle PETTM) using convolutional neural networks. We analysed retrospectively in 113 patients visual IQ (by a 5-point Likert score in two readers) and semi-quantitative IQ (by the coefficient of variation in the liver, CVliv
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32

Alves, Isadora L., Antoon TM Willemsen, Rudi A. Dierckx, Ana Maria M. da Silva, and Michel Koole. "Dual time-point imaging for post-dose binding potential estimation applied to a [11C]raclopride PET dose occupancy study." Journal of Cerebral Blood Flow & Metabolism 37, no. 3 (2016): 866–76. http://dx.doi.org/10.1177/0271678x16644463.

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Receptor occupancy studies performed with PET often require time-consuming dynamic imaging for baseline and post-dose scans. Shorter protocol approximations based on standard uptake value ratios have been proposed. However, such methods depend on the time-point chosen for the quantification and often lead to overestimation and bias. The aim of this study was to develop a shorter protocol for the quantification of post-dose scans using a dual time-point approximation, which employs kinetic parameters from the baseline scan. Dual time-point was evaluated for a [11C]raclopride PET dose occupancy
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33

Wimberley, Catriona, Sonia Lavisse, Vincent Brulon, et al. "Impact of Endothelial 18-kDa Translocator Protein on the Quantification of 18F-DPA-714." Journal of Nuclear Medecine 59, no. 2 (2017): 307–14. https://doi.org/10.2967/jnumed.117.195396.

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Abstract <sup>18</sup>F-DPA-714 is a second-generation tracer for PET imaging of the 18-kDa translocator protein (TSPO), a marker of neuroinflammation. Analysis and interpretation of TSPO PET are challenging, especially because of the basal expression of TSPO. The aim of this study was to evaluate a compartmental model that accounts for the effect of endothelial TSPO binding on the quantification of <sup>18</sup>F-DPA-714 PET scans from a cohort of healthy subjects. <strong>Methods:</strong> Fifteen healthy subjects (9 high-affinity binders and 6 mixed-affinity binders) underwent <sup>18</sup>
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34

Mohymen, Ahmed Abdel, Hamed Ibrahim Farag, Sameh M. Reda, Ahmed Soltan Monem, and Said A. Ali. "Investigating the Impact of Voxel Size and Postfiltering on Quantitative Analysis of Positron Emission Tomography/Computed Tomography: A Phantom Study." Journal of Medical Physics 49, no. 4 (2024): 597–607. https://doi.org/10.4103/jmp.jmp_123_24.

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Aim: This study aims to investigate the influence of voxel size and postfiltering on the quantification of standardized uptake value (SUV) in positron emission tomography/computed tomography (PET/CT) images. Materials and Methods: National Electrical Manufacturers Association phantom with the spheres of different sizes were utilized to simulate the lesions. The phantom was scanned using a PET/CT scanner, and the acquired images were reconstructed using two different matrix sizes, (192 × 192) and (256 × 256), and a wide range of postfiltering values. Results: The findings demonstrated that post
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35

Zanderigo, Francesca, Ramin V. Parsey, and R. Todd Ogden. "Model-Free Quantification of Dynamic PET Data Using Nonparametric Deconvolution." Journal of Cerebral Blood Flow & Metabolism 35, no. 8 (2015): 1368–79. http://dx.doi.org/10.1038/jcbfm.2015.65.

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Dynamic positron emission tomography (PET) data are usually quantified using compartment models (CMs) or derived graphical approaches. Often, however, CMs either do not properly describe the tracer kinetics, or are not identifiable, leading to nonphysiologic estimates of the tracer binding. The PET data are modeled as the convolution of the metabolite-corrected input function and the tracer impulse response function (IRF) in the tissue. Using nonparametric deconvolution methods, it is possible to obtain model-free estimates of the IRF, from which functionals related to tracer volume of distrib
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36

Chen, Delphine L., Joseph Cheriyan, Edwin R. Chilvers, et al. "Quantification of Lung PET Images: Challenges and Opportunities." Journal of Nuclear Medicine 58, no. 2 (2017): 201–7. http://dx.doi.org/10.2967/jnumed.116.184796.

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37

Seo, Youngho. "Quantification of SPECT and PET for Drug Development." Current Radiopharmaceuticalse 1, no. 1 (2008): 17–21. http://dx.doi.org/10.2174/1874471010801010017.

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38

Kucharczak, Florentin, Kevin Loquin, Irène Buvat, Olivier Strauss, and Denis Mariano-Goulart. "Interval-based reconstruction for uncertainty quantification in PET." Physics in Medicine & Biology 63, no. 3 (2018): 035014. http://dx.doi.org/10.1088/1361-6560/aa9ea6.

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39

Bowen, Spencer L., Andrea Ferrero, and Ramsey D. Badawi. "Quantification with a dedicated breast PET/CT scanner." Medical Physics 39, no. 5 (2012): 2694–707. http://dx.doi.org/10.1118/1.3703593.

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40

Namías, Mauro, and Robert Jeraj. "Improved PET quantification and harmonization by adaptive denoising." Biomedical Physics & Engineering Express 6, no. 1 (2020): 015023. http://dx.doi.org/10.1088/2057-1976/ab6996.

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41

Buvat, I. "Quantification in oncologic FDG-PET: A scientific overview." Médecine Nucléaire 35, no. 5 (2011): 320–21. http://dx.doi.org/10.1016/j.mednuc.2011.02.015.

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42

Myers, R., R. B. Banati, T. Jones, and V. J. Cunningham. "Quantification of [11C](R)-PK11195 in Clinical PET." NeuroImage 7, no. 4 (1998): A37. http://dx.doi.org/10.1016/s1053-8119(18)31906-2.

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López-González, Francisco J., Jesús Silva-Rodríguez, José Paredes-Pacheco, et al. "Intensity normalization methods in brain FDG-PET quantification." NeuroImage 222 (November 2020): 117229. http://dx.doi.org/10.1016/j.neuroimage.2020.117229.

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44

Saraste, Antti, Sami Kajander, Chunlei Han, Sergey V. Nesterov, and Juhani Knuuti. "PET: Is myocardial flow quantification a clinical reality?" Journal of Nuclear Cardiology 19, no. 5 (2012): 1044–59. http://dx.doi.org/10.1007/s12350-012-9588-8.

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45

Soret, M. "PET high-resolution imaging and impact on quantification." Physica Medica 28 (June 2012): S4. http://dx.doi.org/10.1016/j.ejmp.2012.08.018.

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46

Adel, D., J. Delrieu, A. S. Brun, et al. "ROI impact on SUVR quantification in amyloid PET." Physica Medica 29 (June 2013): e28. http://dx.doi.org/10.1016/j.ejmp.2013.08.088.

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47

Slomka, Piotr, Daniel S. Berman, Erick Alexanderson, and Guido Germano. "The role of PET quantification in cardiovascular imaging." Clinical and Translational Imaging 2, no. 4 (2014): 343–58. http://dx.doi.org/10.1007/s40336-014-0070-2.

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48

Zhou, Yongxia, and Bing Bai. "AV-1451 PET-TAU IMAGING QUANTIFICATION AND CORRELATIONS." Alzheimer's & Dementia 13, no. 7 (2017): P151. http://dx.doi.org/10.1016/j.jalz.2017.06.2584.

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49

Lopresti, Brian J., William E. Klunk, Chester A. Mathis, et al. "Simplified quantification of PIB amyloid imaging PET studies." Journal of Cerebral Blood Flow & Metabolism 25, no. 1_suppl (2005): S589. http://dx.doi.org/10.1038/sj.jcbfm.9591524.0589.

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Kim, Jin Su. "Combination Radioimmunotherapy Approaches and Quantification of Immuno-PET." Nuclear Medicine and Molecular Imaging 50, no. 2 (2016): 104–11. http://dx.doi.org/10.1007/s13139-015-0392-7.

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