Technical considerations for quantification of 18F-FDG uptake in carotid atherosclerosis

Journal of Nuclear Cardiology, Nov 2017

Sina Tavakoli MD, PhD

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Technical considerations for quantification of 18F-FDG uptake in carotid atherosclerosis

Received Aug Technical considerations for quantification of 18 F-FDG uptake in carotid atherosclerosis Sina Tavakoli 0 0 Reprint requests: Sina Tavakoli , MD, PhD , Departments of Radiology and Medicine (Vascular Medicine Institute), University of Pittsburgh, UPMC Presbyterian Hospital , 200 Lothrop Street, Suite E200, Pittsburgh, PA 15213 , USA 1 Departments of Radiology and Medicine (Vascular Medicine Institute), University of Pittsburgh , Pittsburgh, PA , USA - In this issue of the Journal of Nuclear Cardiology, Johnsrud et al.1 have explored the correlation between different quantification methods of 18F-fluorodeoxyglucose (18F-FDG) uptake and plaque inflammation in endarterectomy specimens of patients with severe carotid stenosis. It has been over a decade since the initial reports of 18F-FDG accumulation in unstable carotid plaques and its correlation with indices of plaque vulnerability.2 This has elicited great enthusiasm to investigate the role of 18F-FDG PET in risk stratification of patients with carotid artery disease. However, the clinical utilization of 18F-FDG PET in the management of atherosclerosis has been hampered by multiple factors, including striking technical variabilities in image acquisition and quantification protocols between various studies as well as metabolic and biological complexity of 18F-FDG uptake in the vessel wall. Despite the recent declines in the incidence and ageadjusted mortality rate,3 stroke has remained a major global health issue. In the United States, approximately 800,000 people suffer from a new (* 75%) or recurrent (* 25%) stroke every year; and about 130,000 people die from it, which puts stroke as the 5th leading cause of death.3 Carotid atherosclerosis is a common and a potentially preventable cause of ischemic stroke, accounting for * 15% of cases.3,4 Currently, atherosclerosis is suspected as a possible etiology of stroke if the patient has significant disease ([ 50% luminal stenosis) in a clinically relevant artery, in accordance with the Trial of Org10172 in Acute Stroke Treatment (TOAST) classification system.5 However, strokes caused by thromboembolic complications of unstable non-stenotic or mildly stenotic (\ 50%) plaques are not accounted for in this classification;5 and such cases may instead be classified as cryptogenic.6 This may result in an underestimation of the true contribution of carotid atherosclerosis as the etiology of ischemic stroke. It is now well established that a large number of acute coronary syndromes originate from acute complications (e.g., rupture or ulceration) of vulnerable, but mildly stenotic, plaques, which triggers the activation of thrombotic cascade and luminal occlusion. 7 It is plausible to assume similar pathophysiology may contribute to plaque vulnerability in patients with mild carotid stenosis, which by current management guidelines will not be candidates for invasive interventions.8 Traditionally, the decision to proceed with carotid intervention to prevent new or recurrent stroke has been primarily based on the patients’ symptoms and the extent of luminal stenosis, as detected by catheter angiography or non-invasive imaging. Large randomized clinical trials dating back to 1980s have shown that carotid interventions (endarterectomy and more recently stenting) reduce the risk of stroke compared to medical therapy in symptomatic or asymptomatic patients with severe stenosis ([ 70%) and in symptomatic patients with moderate stenosis (50%-70%).8–10 However, this approach has several shortcomings, for example: 1. Asymptomatic moderate and severe carotid artery atherosclerosis is highly prevalent, particularly in elderly men.11 The prevalence of moderate and severe stenosis in men [ 80 years reaches to *7.5% and *3.1%, respectively. Comparatively, in women older than 80 years, the prevalence of moderate and severe stenosis is *5% and *0.9%.11 Fortunately, about 80% of patients with high-grade stenosis remain stroke-free during long follow-up periods (*10-15 years). Therefore, recommending invasive interventions, which carry a considerable risk of complications (e.g., peri-procedural stroke, myocardial infarction, and cranial nerve injury) based on the extent of stenosis may not be ideal. 2. There is a 1.6% annual risk of stroke in patients with asymptomatic mild-to-moderate stenosis. While, some of these cases are likely caused by vulnerable plaques, these patients are not usually considered as candidates for carotid interventions.12 3. Advances in risk factor modifications and medical therapy, e.g., intensive statin therapy, since the original trials have significantly reduced the risk of stroke in patients who are treated non-invasively. Thus, more information is required to establish whether the severity of stenosis can still be used as a reliable criterion to identify patients who benefit from endarterectomy versus medical therapy.10 Development and validation of non-invasive imaging techniques that can identify high-risk carotid atherosclerotic lesions have been a subject of extensive research over the past decade. Characteristics of vulnerable plaques that may be imaged non-invasively include intra-plaque hemorrhage,13,14 thin fibrous cap,15,16 large necrotic core,14,16 and high inflammatory burden.17 Such detailed characterization of carotid plaques through non-invasive approaches may ultimately improve the risk stratification of patients and allow for more accurate selection of patients who benefit from invasive interventions. HIGH-RESOLUTION CAROTID MRI MRI and MRA of carotid arteries have been commonly utilized clinically to determine the severity of luminal compromise. Recent improvements in the spatial resolution and development of new sequences have allowed for detailed structural characterization of carotid plaques, and identification of features that are associated with plaque vulnerability.18 While MRI has a low sensitivity for detection of molecular processes that contribute to plaque vulnerability, it has a great potential for structural characterization of carotid plaques with a number of avenues yet to be explored. For example, dense fibrous tissue, loose fibrous matrix, and lipid/ necrotic core can be accurately and reproducibly differentiated using T1, T2, proton density, and time-of-flight images.19 High-resolution MRI is also capable to differentiate between the intact and ruptured fibrous cap with * 90% agreement with histological analysis of endarterectomy specimens.15 The T1-shorterning effect caused by intra-plaque hemorrhage may be detected by various sequences, e.g., magnetization-prepared rapid gradient echo (MP-RAGE), time-of-flight, and fast spin echo.20,21 Additionally, dynamic contrast-enhanced MRI provides information on carotid plaque neovascularization.22 18F-FDG PET Complementing the structural information obtained by MRI, PET can track ongoing metabolic, molecular and cellular processes that contribute to the pathogenesis of plaque vulnerability. So far, a large number of PET tracers have been tested in pre-clinical investigations, which target various aspects of atherosclerosis, such as vessel wall inflammation, plaque hypoxia, protease activity, and extra-cellular matrix remodeling.7 However, the most commonly used PET tracer in the clinical setting is 18F-FDG, reflecting its widespread availability as a Food and Drug Administration (FDA)-approved radiopharmaceutical with excellent safety profile. Multiple studies have shown the association of 18F-FDG uptake and the inflammatory burden of carotid plaques and its capacity to retrospectively identify culprit lesions after strokes or transient ischemic attacks.23 18F-FDG PET has also been promising in prospective risk stratification of patients with carotid artery disease and in prediction of the future risk of cardiovascular events.23. However, the routine application of 18F-FDG PET in the clinical practice and risk stratification of patients with carotid atherosclerosis has been challenged by several limitations, which will be briefly discussed here. LIMITED BIOLOGICAL SPECIFICITY 18F-FDG accumulation in sites of inflammation, including atherosclerosis, has been commonly attributed to the high glycolytic activity of inflammatory cells, particularly activated macrophages.7,23 However, glucose uptake represents a nearly ubiquitous metabolic process; hence, the uptake by other vascular and perivascular cells limits the specificity of 18F-FDG PET for imaging of plaque inflammation.7,23–26 This is particularly problematic in imaging of coronary arteries, in which the high background uptake of 18F-FDG by the myocardium obscures the visualization of coronary plaques and complicates the quantification of uptake.27 Among different constituents of plaques, macrophages, particularly upon activation into proinflammatory states, have been considered as the main contributors to 18F-FDG uptake.28 However, recent data suggest that 18F-FDG uptake may not adequately characterize the metabolic divergence of macrophages upon activation into different pro-inflammatory (M1) or antiinflammatory (M2) polarization states.24–26,29 Striking technical variability is another limitation of 18F-FDG PET of atherosclerosis.30,31 This is of particular concern as the small size of plaques and their close proximity to blood pool make them prone to partialvolume effects, which influence the accuracy of 18FFDG uptake quantification.31 Therefore, any meta-analysis or comparison between the results of different studies need to be performed with extreme caution to account for these technical variabilities.32 PATIENT PREPARATION, IMAGE ACQUISITION, AND RECONSTRUCTION The fasting period (usually from 4 to 12 hours) and the pre-scan blood glucose level affect 18F-FDG uptake.32 High levels of glucose reduce 18F-FDG uptake in cultured cells and vessel wall, presumably through competition for glucose transporters, while it increases the blood pool activity.30,32,33 It is recommended that a blood glucose level \ 130 mg/dl is optimal for 18F-FDG PET of vessel wall.30 But, glucose levels of up to 200 mg/dl have been used in multiple investigations.32 The injected dose of 18F-FDG is another potential confounding factor, which varies between studies from 185 to 925 MBq.30 Low doses of 18F-FDG have been shown not to influence the standardized uptake value (SUV) and target-to-background ratio (TBR).33 Therefore, doses of 3-4 MBq/kg have been advocated for imaging of atherosclerosis to reduce the patients’ radiation exposure, particularly if repetitive scans are being considered.30 The wait time post-injection varies from 30 to 210 minutes in different studies. Delayed scans (* 2 hours) seem to improve the target-to-background contrast and reduce the influence of partial-volume effect from blood pool, allowing for better visualization and quantification of 18F-FDG uptake in atherosclerotic plaques.30–32 Vascular PET is highly susceptible to partial-volume effect due to the small size of the vessel wall, which is usually below or at the resolution of PET. Thus, quantitative 18F-FDG imaging is strongly influenced by the plaque volume and morphology as well as multiple scan-related factors, e.g., voxel size, slice thickness, reconstruction algorithms and attenuation protocols, reviewed elsewhere.30,31 Plaque uptake seems to be generally underestimated by multiple folds due to the partial-volume effect.30 UPTAKE QUANTIFICATION Standardized uptake values (SUVs) and target-tobackground ratios (TBR) are the two most commonly reported parameters for quantification of 18F-FDG uptake in atherosclerosis. SUV is a semi-quantitative measure of uptake and attempts to account for variations in the injected dose of radiotracers and the patient’s size through correction for body weight, lean body weight, or body surface area. Mean SUV (SUVmean), maximum SUV (SUVmax), or the average SUVmax over multiple slices (either throughout or over the most diseased segments of plaques) (mean of SUVmax) have often been reported in different studies.31,32. Normalization of vessel wall SUV to that of a reference tissue (most commonly SUVmean of blood pool) has been widely used to remove the influence of compounding factors, such as the clearance rate and blood glucose level, on the estimated 18F-FDG uptake. The normalization may be performed through either subtraction of the blood pool SUV from vessel wall SUV (i.e., corrected SUV), or more commonly by calculating the ratio of plaque-to-blood pool SUV (i.e., TBR). Both corrected SUV and TBR remove the effect of blood pool spill-in the vessel wall; thus, may more accurately represent the 18F-FDG uptake and plaque inflammation,32,34 though they will be influenced by factors which alter the blood pool activity, e.g., renal failure and increased 18F-FDG uptake by circulating cells.30 Importantly, TBR is more reproducible under different scan settings32 and has been shown to have a higher association with plaque inflammation and macrophage burden compared to SUV.32 However, the potential of the various quantification techniques in prediction of long-term risk of carotid atherosclerosis progression and stroke is not yet available. Johnsrud et al.1 recruited 44 patients with carotid artery atherosclerosis associated with[70% stenosis for 18F-FDG PET/CT. Of these patients, 30 underwent endarterectomy and histological assessment of inflammation to evaluate the correlation between different 18FFDG uptake quantification methods and histological indices of inflammation. 18F-FDG uptake has been quantified using various parameters, including mean SUVmax, maximal SUVmax throughout the plaques or through the most diseased segment, blood background-corrected SUV (cSUV), and TBR. The authors have found strong correlations between the various 18F-FDG uptake quantification parameters (correlation coefficients of 0.570.99, P \ 0.001). Inter-observer variability analysis, performed through assessment of the correlation between two independent nuclear medicine physicians, showed a higher agreement for uncorrected SUVmax (correlation coefficients of 0.96-0.98) compared to both cSUV and TBR (correlation coefficients of 0.63-0.68 for TBR and 0.90-0.93 for cSUV). The study reports moderate correlations between the different 18F-FDG uptake parameters and the extent of inflammation in endarterectomy specimens (correlation coefficients of 0.44-0.59, P \ 0.02). Correlations between 18F-FDG uptake and histology were overall very similar using the different parameters, although they were slightly stronger when mean SUVmax was used compared to the other parameters. A strength of this study is the concurrent comparison between the different quantification methods and histology in patients who have undergone endarterectomy in a relatively short interval from 18F-FDG PET. A more detailed histological approach, including immunological profiling of inflammatory cells, and determining the correlation between different quantitative measures of 18F-FDG uptake and other indices of plaque vulnerability (e.g., thin fibrous cap, large necrotic core, intraplaque hemorrhage) would have brought a more indepth insight into this topic. Together, this study demonstrates a higher interobserver agreement for SUVmax and a slightly higher association between SUVmax and plaque inflammation. However, it should be noted that SUV is highly prone to variations in tracer dose as well as patients’ (e.g., body weight) and scans parameters, which adversely influence the reproducibility of quantification compared to TBR.30 Therefore, the use of TBR has been recommended for quantification of 18F-FDG uptake in atherosclerosis by the Cardiovascular Committee of the European Association of Nuclear Medicine.30 CONCLUSION 18F-FDG uptake has been shown to be associated with carotid plaque inflammatory burden and risk of cardiovascular events in a number of investigations.23 However, the application of 18F-FDG in clinical practice has been hampered by several limitations, including the limited biological specificity of 18F-FDG towards inflammatory cell,24–26 myocardial and skeletal muscle uptake interfering with visualization and quantification of vascular uptake, and lack of standardized protocols leading to large variabilities in patients’ preparation, image acquisition/ reconstruction and quantification. Over the recent years, there has been a growing interest in optimization of image acquisition and quantification methods and standardization of vascular 18F-FDG PET.30–32 This approach may ultimately lead to development of more reliable and reproducible protocols, which allow for widespread clinical utility and validation in large cohorts of patients to determine the value of 18F-FDG PET in predicting the risk of stroke and disease outcome. Additionally, other novel PET tracers have been developed, which may overcome the limited biological specificity of 18F-FDG. While most such approaches are still in the pre-clinical stage, a few have already provided promising results in early clinical studies, e.g., 18F-NaF, 68Ga- and 64Cu-DOTATATE, 11Ccholine, and 11C-acetate;30 and their clinical utility is beginning to be explored. 1. Johnsrud K , Skagen K , Seierstad T , Skjelland M , Russell D , Revheim ME . 18F-FDG PETCT for the quantification of inflammation in large carotid artery plaques . J Nucl Cardiol . 2017 . https://doi.org/10.1007/s12350-017-1121-7. 2. Rudd JH , Warburton EA , Fryer TD , Jones HA , Clark JC , Antoun N , Johnstrom P , Davenport AP , Kirkpatrick PJ , Arch BN , Pickard JD , Weissberg PL . Imaging atherosclerotic plaque inflammation with [18f]-fluorodeoxyglucose positron emission tomography . Circulation . 2002 ; 105 : 2708 - 11 . 3. Benjamin EJ , Blaha MJ , Chiuve SE , Cushman M , Das SR , Deo R , de Ferranti SD , Floyd J , Fornage M , Gillespie C , Isasi CR , Jimenez MC , Jordan LC , Judd SE , Lackland D , Lichtman JH , Lisabeth L , Liu S , Longenecker CT , Mackey RH , Matsushita K , Mozaffarian D , Mussolino ME , Nasir K , Neumar RW , Palaniappan L , Pandey DK , Thiagarajan RR , Reeves MJ , Ritchey M , Rodriguez CJ , Roth GA , Rosamond WD , Sasson C , Towfighi A , Tsao CW , Turner MB , Virani SS , Voeks JH , Willey JZ , Wilkins JT , Wu JH , Alger HM , Wong SS , Muntner P . Heart disease and stroke statistics-2017 update: A report from the american heart association . Circulation . 2017 ; 135 : e146 - 603 . 4. Kolominsky-Rabas PL , Weber M , Gefeller O , Neundoerfer B , Heuschmann PU . Epidemiology of ischemic stroke subtypes according to toast criteria: Incidence, recurrence, and long-term survival in ischemic stroke subtypes: A population-based study . Stroke . 2001 ; 32 : 2735 - 40 . 5. Adams HP Jr, Biller J. Classification of subtypes of ischemic stroke: History of the trial of org 10172 in acute stroke treatment classification . Stroke . 2015 ; 46 : e114 - 7 . 6. Hart RG , Diener HC , Coutts SB , Easton JD , Granger CB , O'Donnell MJ , Sacco RL , Connolly SJ . Embolic strokes of undetermined source: The case for a new clinical construct . Lancet Neurol . 2014 ; 13 : 429 - 38 . 7. Tavakoli S , Vashist A , Sadeghi MM . Molecular imaging of plaque vulnerability . J Nucl Cardiol . 2014 ; 21 : 1112 - 28 . 8. Bayer-Karpinska A , Schindler A , Saam T. Detection of vulnerable plaque in patients with cryptogenic stroke . Neuroimaging Clin N Am . 2016 ; 26 : 97 - 110 . 9. Mayberg MR , Wilson SE , Yatsu F , Weiss DG , Messina L , Hershey LA , Colling C , Eskridge J , Deykin D , Winn HR . Carotid endarterectomy and prevention of cerebral ischemia in symptomatic carotid stenosis. Veterans affairs cooperative studies program 309 trialist group . JAMA . 1991 ; 266 : 3289 - 94 . 10. Bonati LH , Nederkoorn PJ. Clinical perspective of carotid plaque imaging . Neuroimaging Clin N Am . 2016 ; 26 : 175 - 82 . 11. de Weerd M , Greving JP , Hedblad B , Lorenz MW , Mathiesen EB , O'Leary DH , Rosvall M , Sitzer M , Buskens E , Bots ML . Prevalence of asymptomatic carotid artery stenosis in the general population: An individual participant data meta-analysis . Stroke . 2010 ; 41 : 1294 - 7 . 12. Inzitari D , Eliasziw M , Gates P , Sharpe BL , Chan RK , Meldrum HE , Barnett HJ . The causes and risk of stroke in patients with asymptomatic internal-carotid-artery stenosis . North american symptomatic carotid endarterectomy trial collaborators . N Engl J Med . 2000 ; 342 : 1693 - 700 . 13. Cappendijk VC , Cleutjens KB , Heeneman S , Schurink GW , Welten RJ , Kessels AG , van Suylen RJ , Daemen MJ , van Engelshoven JM , Kooi ME . In vivo detection of hemorrhage in human atherosclerotic plaques with magnetic resonance imaging . J Magn Reson imaging . 2004 ; 20 : 105 - 10 . 14. Yuan C , Mitsumori LM , Ferguson MS , Polissar NL , Echelard D , Ortiz G , Small R , Davies JW , Kerwin WS , Hatsukami TS . In vivo accuracy of multispectral magnetic resonance imaging for identifying lipid-rich necrotic cores and intraplaque hemorrhage in advanced human carotid plaques . Circulation . 2001 ; 104 : 2051 - 6 . 15. Hatsukami TS , Ross R , Polissar NL , Yuan C . Visualization of fibrous cap thickness and rupture in human atherosclerotic carotid plaque in vivo with high-resolution magnetic resonance imaging . Circulation . 2000 ; 102 : 959 - 64 . 16. Cai J , Hatsukami TS , Ferguson MS , Kerwin WS , Saam T , Chu B , Takaya N , Polissar NL , Yuan C. In vivo quantitative measurement of intact fibrous cap and lipid-rich necrotic core size in atherosclerotic carotid plaque: Comparison of high-resolution, contrast-enhanced magnetic resonance imaging and histology . Circulation . 2005 ; 112 : 3437 - 44 . 17. Marnane M , Merwick A , Sheehan OC , Hannon N , Foran P , Grant T , Dolan E , Moroney J , Murphy S , O'Rourke K , O'Malley K , O'Donohoe M , McDonnell C , Noone I , Barry M , Crowe M , Kavanagh E , O'Connell M , Kelly PJ . Carotid plaque inflammation on 18f-fluorodeoxyglucose positron emission tomography predicts early stroke recurrence . Ann Neurol . 2012 ; 71 : 709 - 18 . 18. Vesey AT , Dweck MR , Fayad ZA . Utility of combining pet and mr imaging of carotid plaque . Neuroimaging Clin N Am . 2016 ; 26 : 55 - 68 . 19. Saam T , Ferguson MS , Yarnykh VL , Takaya N , Xu D , Polissar NL , Hatsukami TS , Yuan C . Quantitative evaluation of carotid plaque composition by in vivo MRI . Arterioscler Thrombosis Vasc Biol . 2005 ; 25 : 234 - 9 . 20. Moody AR , Murphy RE , Morgan PS , Martel AL , Delay GS , Allder S , MacSweeney ST , Tennant WG , Gladman J , Lowe J , Hunt BJ . Characterization of complicated carotid plaque with magnetic resonance direct thrombus imaging in patients with cerebral ischemia . Circulation . 2003 ; 107 : 3047 - 52 . 21. Ota H , Yarnykh VL , Ferguson MS , Underhill HR , Demarco JK , Zhu DC , Oikawa M , Dong L , Zhao X , Collar A , Hatsukami TS , Yuan C . Carotid intraplaque hemorrhage imaging at 3.0-t mr imaging: Comparison of the diagnostic performance of three t1- weighted sequences . Radiology . 2010 ; 254 : 551 - 63 . 22. Kerwin W , Hooker A , Spilker M , Vicini P , Ferguson M , Hatsukami T , Yuan C . Quantitative magnetic resonance imaging analysis of neovasculature volume in carotid atherosclerotic plaque . Circulation . 2003 ; 107 : 851 - 6 . 23. Sadeghi MM . (18)F-FDG PET and vascular inflammation: Time to refine the paradigm? J Nucl Cardiol . 2015 ; 22 : 319 - 24 . 24. Tavakoli S , Short JD , Downs K , Nguyen HN , Lai Y , Zhang W , Jerabek P , Goins B , Sadeghi MM , Asmis R. Differential regulation of macrophage glucose metabolism by macrophage colony-stimulating factor and granulocyte-macrophage colony-stimulating factor: Implications for 18F FDG PET imaging of vessel wall inflammation . Radiology . 2017 ; 283 : 87 - 97 . 25. Tavakoli S , Zamora D , Ullevig S , Asmis R . Bioenergetic profiles diverge during macrophage polarization: Implications for the interpretation of 18F-FDG PET imaging of atherosclerosis . J Nucl Med . 2013 ; 54 : 1661 - 7 . 26. Tavakoli S , Downs K , Short JD , Nguyen HN , Lai Y , Jerabek PA , Goins B , Toczek J , Sadeghi MM , Asmis R . Characterization of macrophage polarization states using combined measurement of 2-deoxyglucose and glutamine accumulation: Implications for imaging of atherosclerosis . Arterioscler Thromb Vasc Biol . 2017 ; 37 : 1840 - 1848 . https://doi.org/10.1161/ATVBAHA.117.308848. 27. Wells RG , Ruddy TD . The dream of imaging coronary artery inflammation with FDG PET/CT imaging . J Nucl Cardiol . 2016 . https://doi.org/10.1007/s12350-016-0510-7. 28. Tawakol A , Singh P , Mojena M , Pimentel-Santillana M , Emami H , MacNabb M , Rudd JH , Narula J , Enriquez JA , Traves PG , Fernandez-Velasco M , Bartrons R , Martin-Sanz P , Fayad ZA , Tejedor A , Bosca L. HIF -1alpha and PFKFB3 mediate a tight relationship between proinflammatory activation and anaerobic metabolism in atherosclerotic macrophages . Arterioscler Thromb Vasc Biol . 2015 ; 35 : 1463 - 71 . 29. Folco EJ , Sheikine Y , Rocha VZ , Christen T , Shvartz E , Sukhova GK , Di Carli MF , Libby P . Hypoxia but not inflammation augments glucose uptake in human macrophages: Implications for imaging atherosclerosis with 18fluorine-labeled 2-deoxy-D-glucose positron emission tomography . J Am Coll Cardiol . 2011 ; 58 : 603 - 14 . 30. Bucerius J , Hyafil F , Verberne HJ , Slart RH , Lindner O , Sciagra R , Agostini D , Ubleis C , Gimelli A , Hacker M. Position paper of the cardiovascular committee of the European Association of Nuclear Medicine (EANM) on pet imaging of atherosclerosis . Eur J Nucl Med Mol Imaging . 2016 ; 43 : 780 - 92 . 31. Huet P , Burg S , Le Guludec D , Hyafil F , Buvat I . Variability and uncertainty of 18F-FDG PET imaging protocols for assessing inflammation in atherosclerosis: Suggestions for improvement . J Nucl Med . 2015 ; 56 : 552 - 9 . 32. Gholami S , Salavati A , Houshmand S , Werner TJ , Alavi A . Assessment of atherosclerosis in large vessel walls: A comprehensive review of FDG-PET/CT image acquisition protocols and methods for uptake quantification . J Nucl Cardiol . 2015 ; 22 : 468 - 79 . 33. Bucerius J , Mani V , Moncrieff C , Machac J , Fuster V , Farkouh ME , Tawakol A , Rudd JH , Fayad ZA . Optimizing 18F-FDG PET/ CT imaging of vessel wall inflammation: The impact of 18F-FDG circulation time, injected dose, uptake parameters, and fasting blood glucose levels . Eur J Nucl Med Mol Imaging . 2014 ; 41 : 369 - 83 . 34. Lensen KDF , van Sijl AM , Voskuyl AE , van der Laken CJ , Heymans MW , Comans EFI , Nurmohamed MT , Smulders YM , Boellaard R. Variability in quantitative analysis of atherosclerotic plaque inflammation using 18F-FDG PET/CT . PLoS ONE . 2017 ; 12 : e0181847 .


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Sina Tavakoli MD, PhD. Technical considerations for quantification of 18F-FDG uptake in carotid atherosclerosis, Journal of Nuclear Cardiology, 2017, 1-5, DOI: 10.1007/s12350-017-1060-3