CME ARTICLE REVIEW ARTICLE Precision and accuracy of clinical quantification of myocardial blood flow by dynamic PET: A technical perspective Jonathan B. Moody, PhD,a Benjamin C. Lee, PhD,a James R. Corbett, MD,b,c Edward P. Ficaro, PhD,a,b and Venkatesh L. Murthy, MD, PhDb,c a

INVIA Medical Imaging Solutions, Ann Arbor, MI Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, MI c Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor, MI b

Received Dec 18, 2014; accepted Feb 11, 2015 doi:10.1007/s12350-015-0100-0

A number of exciting advances in PET/CT technology and improvements in methodology have recently converged to enhance the feasibility of routine clinical quantification of myocardial blood flow and flow reserve. Recent promising clinical results are pointing toward an important role for myocardial blood flow in the care of patients. Absolute blood flow quantification can be a powerful clinical tool, but its utility will depend on maintaining precision and accuracy in the face of numerous potential sources of methodological errors. Here we review recent data and highlight the impact of PET instrumentation, image reconstruction, and quantification methods, and we emphasize 82Rb cardiac PET which currently has the widest clinical application. It will be apparent that more data are needed, particularly in relation to newer PET technologies, as well as clinical standardization of PET protocols and methods. We provide recommendations for the methodological factors considered here. At present, myocardial flow reserve appears to be remarkably robust to various methodological errors; however, with greater attention to and more detailed understanding of these sources of error, the clinical benefits of stress-only blood flow measurement may eventually be more fully realized. Key Words: Myocardial blood flow Æ Myocardial flow reserve Æ Cardiac PET/CT Æ Rubidium-82 Abbreviations MPI Myocardial Perfusion Imaging MBF Myocardial Blood Flow MFR Myocardial Flow Reserve TOF Time of Flight RPC Repeatability Coefficient FBP Filtered Back Projection 3DRP 3D Reprojection (i.e., 3D FBP)

Reprint requests: Venkatesh L. Murthy, MD, PhD, Division of Nuclear Medicine, Department of Radiology, University of Michigan, 1338 Cardiovascular Center, 1500 E. Medical Center Dr, SPC 5873, Ann Arbor, MI 48109-5873; [email protected] 1071-3581/$34.00 Copyright  2015 American Society of Nuclear Cardiology.


Ordered Subsets Expectation Maximization Point Spread Function Left Ventricle Right Ventricle

INTRODUCTION Recent advances in positron emission tomography (PET) technology have rapidly expanded the clinical application of cardiac PET myocardial perfusion imaging (MPI). Among these, vastly improved count rate performance, data handling, and computing power of current-generation PET systems have enabled routine quantification of absolute myocardial blood flow (MBF)

Moody et al Clinical PET MBF: technical perspective

and myocardial flow reserve (MFR). Prognostic data from several recent studies1-5 have suggested an important clinical role for MBF quantification and have motivated increased interest in this area. While recent technological advances have greatly enhanced the ease with which clinical MBF measurements can be made,6 the data acquisition and processing requirements for such measurements continue to be more demanding than conventional MPI. However, new PET technology that enables the acquisition of dynamic cardiac data, and commercial software that enables convenient quantification of absolute MBF are not enough. In order for absolute MBF and MFR to achieve their full clinical potential and meaningfully influence the care of patients, clinical MBF quantification must be better understood and standardized so that consistent and accurate results can be routinely realized across all cardiac PET centers. This review will highlight the impact of recent technological improvements on MBF quantification, as well as draw attention to those technical considerations that remain important for clinical applications. Improved Technology for Dynamic Cardiac PET PET scanner design necessarily incorporates many trade-offs to perform the delicate task of detecting trace quantities of radiopharmaceuticals in the living body.7 Detection efficiency, spatial resolution, count rate performance and cost are among the most important factors that must be balanced. It has been recognized for many years that the requirements and optimal design for dynamic PET are somewhat different from those of static imaging.8-10 Until recently, the dominant commercial scanner design favored detection efficiency and spatial resolution for static whole-body imaging over count rate performance. MBF quantification with such scanners was typically limited to academic research centers, often with the capability to produce blood flow tracers such as 13Nammonia which entail somewhat lower count rate demands for dynamic PET than 15O-water and 82Rb. The scintillator crystal commonly utilized by these scanners, bismuth germanate (BGO), has excellent detection efficiency but slow timing which limits count rate performance.7,8,10 More recent scintillators, including lutetium oxyorthosilicate (LSO), lutetium yttrium orthosilicate (LYSO), and germanium orthosilicate (GSO), which all have faster timing and higher light output than BGO, have enabled improved count rate performance while generally maintaining good detection efficiency.7 Count rate performance is quantified by the peak noise equivalent count rate (NECR) which is defined as the peak achievable true coincidence count rate after

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accounting for the noise penalty incurred in correcting for random and scattered coincidences.11 Since dynamic PET involves simultaneous tracer injection and image acquisition, the scanner must cope with very high initial activities, particularly when short-lived radionuclides such as 15O and 82Rb are used. High initial count rates can lead to PET detector saturation, extreme dead-time losses, and loss of image resolution and contrast.12,13 Therefore, poor count rate performance can severely limit the accuracy of tracer quantification during the initial first-pass of tracer through the left ventricle. In this review, we focus on dynamic PET with 82Rb, which is currently the most widely applicable radiotracer for clinical MBF quantification. Although the development of new scanner technology has been driven by many factors, the recent emergence of new PET scintillator technology and commercial 3D scanner designs14-16 as well as practical implementations of time-of-flight (TOF)17,18 and listmode acquisition19 have neatly converged to meet the needs of routine dynamic cardiac PET. For example, the published count rate performance of sixteen 3D PET scanners from 2000 to the present is shown in Figure 1, illustrating the dramatic increase in count rate performance which has coincided with the gradual rise of clinical MBF quantification. Recently announced PET/ CT scanners from Philips (Vereos TF) and GE (Discovery IQ) will likely maintain the same upward trend in count rate performance.

Figure 1. Growth in 3D count rate performance for commercial PET scanners since 2000 (peak NECR-1R from published performance evaluations using the NEMA NU-2 2001 or 2007 standard14-16,18,53,136-141; some scanners are capable of both 2D and 3D acquisition, and some are 3D only).

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Moody et al Clinical PET MBF: technical perspective

Precision and Accuracy of Myocardial Blood Flow We consider two general types of error in the context of MBF quantification: deterministic (or systematic) error which is quantified by MBF bias, and statistical (or random) error which is quantified by MBF variance. The true MBF must be known to be able to quantify bias. However, in practice, we usually only have access to a ‘‘gold standard’’ estimate of the true MBF, which is commonly taken to be MBF measured by 15 O-water PET or microspheres. We will also use the inversely related terms accuracy and precision for bias and variance. A related measure of variability to be discussed below is the commonly reported repeatability coefficient (RPC)20 which is defined as 1.96 times the standard deviation of the difference between two shortterm test-retest measurements. Unlike the intra-class correlation coefficient, RPC is reported in absolute units. The interpretation of RPC in the context of MBF quantification is that under the prescribed experimental conditions, 95% of short-term test-retest MBF measurements will be within RPC mL/min/g of each other: a larger RPC implies a higher variability (lower repeatability).

METHODOLOGICAL SOURCES OF MBF ERROR The precision and accuracy of MBF depends on the precision and accuracy of the underlying PET images,

and may be affected by many potential methodological errors arising from PET instrumentation, image reconstruction, and methods used to extract regional information from the PET images.21 Six major methodological factors are listed in Table 1 and are reviewed in detail in the following sections. Tracer Infusion and Temporal Sampling Dynamic cardiac PET image acquisition begins at the same time as tracer infusion, and the data are characterized by rapidly changing activity distributions during the initial passage of tracer through the chambers of the heart, followed by slowly varying distribution after tracer uptake in the myocardium.22 Accurate estimation of MBF depends on accurate image sampling of the initial rapid phase, as well as obtaining adequate count statistics in the later slow phase. In general, the MBF variance will increase with increasing tracer infusion time;22 however, shorter infusion times require correspondingly higher temporal sampling rates to avoid introducing bias into the MBF estimates. Many of the tracer injection protocols in use today were optimized on prior low-count-rate PET systems.23 On such PET systems, slow tracer infusion (e.g., 30 seconds or longer) was common to avoid detector saturation and dead-time losses.24 Current-generation PET scanners that have enhanced count-rate performance may be capable of higher temporal sampling rates, and thus, shorter infusion times. However, it is necessary to

Table 1. Major methodological factors that affect MBF quantification and are considered in detail in the text, and specific recommendations to address these factors in order to move the utilization of 82 Rb MBF quantification forward in the clinic

Methodological factor Tracer infusion and temporal sampling Scatter correction Prompt gamma correction Image reconstruction and post-filtering Patient motion Tracer kinetic modeling

Recommendations Standardization needed that takes into consideration the 82Rb generator design, the count rate capabilities of the PET scanner, and the requirements of a clinically feasible computational load Further data needed to optimize correction for dynamic cardiac PET, and assess impact on MBF precision and accuracy Further data needed for the validation of existing methods, and to assess the impact on MBF precision and accuracy Further data needed to optimize and standardize newer technology and methods specifically for dynamic cardiac PET, and to assess the impact on MBF precision and accuracy Further development needed for clinically viable correction methods Standardization needed for image pre-processing, partial volume correction, and model-based methods; further data and standardization needed for RenkinCrone extraction relation with respect to PET corrections and image reconstruction; impact on MBF precision and accuracy needs to be fully characterized

Moody et al Clinical PET MBF: technical perspective

balance the acquisition of more images with the requirement of a clinically feasible computational load. An optimization methodology based on simulations was proposed by Kolthammer et al.25 Preliminary results to develop optimized temporal sampling for 82Rb dynamic PET were recently reported by Lee et al,26 who found by analysis of patient data that a simple two-phase framing of dynamic PET images with temporal sampling for the fast initial phase as a function of the injection duration was adequate. MBF variance can also be increased by variability in the activity profile of the infusion27 which is primarily an issue with generator-produced 82Rb. The most commonly used 82Rb generator (CardioGen-82, Bracco Diagnostics Inc.) delivers 82Rb at a constant flow rate of 50 mL/min. As the 82Sr parent isotope decays over the course of the generator’s lifetime, less 82 Rb is available for elution. For a given dose, this leads to greater elution volumes at the end of the generator’s life, and thus the infusion time can increase by as much as three times.6 A novel 82Rb generator has recently been developed with the specific goal of providing constant activity rate infusions, improving the consistency of the infusion profile over the life of the generator.27 The potential benefits of this new generator are a reduced likelihood for PET scanner saturation during high count initial frames, and more consistent MBF variance over the lifetime of the generator,27 although no studies demonstrating these benefits have yet been reported. The higher sensitivity of 3D PET acquisition compared to 2D acquisition allows lower radiotracer doses to be used, but is accompanied by an increase in random and scatter events.28 Since 82Rb is short-lived (76 seconds half-life), it requires careful optimization of dose to balance potential saturation and dead-time losses in the early dynamic time frames (blood-pool phase) with adequate myocardial counts at later time frames (myocardial phase)23 (Figure 2). Tout et al29 compared two fixed 82Rb doses in consecutive patients using a current-generation high-count-rate capable 3D scanner (Siemens Biograph mCT): 1480 MBq (40 mCi) in 217 patients, and 1110 MBq (30 mCi) in 159 patients. The higher dose resulted in detector saturation during the blood-pool phase in 15% of cases (33/217), which was reduced to 1% (2/159) in the lower dose group.29 Patient BMI and 82Rb generator age were found to contribute only weakly to the incidence of saturation, and slow transit or pooling of 82 Rb in the axillary vessels also appeared to play a role.29 Detector saturation caused variable count losses at the peak of the arterial LV input function (Figure 2), and likely contributed to MBF bias, although this was not directly evaluated.29

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Figure 2. Input function from first-pass dynamic data showing (A) a patient with saturation identified during stress (frames 2-6) and (B) a patient with saturation identified during stress (frames 3-9) and rest (frames 3-9). (Reproduced from Tout et al29 with kind permission from Lippincott Williams & Wilkins.).

Scatter Correction Even with current high-count-rate scanners and appropriate radiotracer doses, the accuracy of the initial blood-pool phase may be further affected by errors in scatter correction. Cheng et al30 showed that for the most widely used scatter estimation method (single scatter simulation31), random fractions higher than 50% and/or low count frames can produce substantially biased and unstable scatter estimates, often leading to overcorrection of scatter. These conditions often occur during the early time frames of dynamic cardiac acquisitions. For example, in Figure 3, the random and scatter fractions are shown (mean ± SD, dotted lines are minimum, maximum) for rest and regadenoson stress 82 Rb dynamic PET scans (injected dose: 12 MBq/kg) acquired in 25 normal volunteers on a current-generation scanner (Siemens Biograph mCT). The mean random fraction was as high as 80% in the initial 50 seconds, and did not drop to 50% until after 82Rb had cleared the blood pool around 150 seconds. During the same period,

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Moody et al Clinical PET MBF: technical perspective

its effect on MBF accuracy have not been evaluated. The single scatter simulation method is also susceptible to errors due to misalignment of emission and transmission scans.32 When misalignment occurs, the scatter estimate may be scaled too high causing overcorrection and quantitative errors throughout the image.33 Further studies are needed to investigate the potential impact of scatter correction errors on MBF accuracy and whether a modified scatter correction methodology may be warranted.30 Prompt Gamma Correction For some positron-emitting isotopes, a fraction of the decay events that emit a positron also emit a single gamma, called a prompt gamma, not associated with the subsequent positron annihilation.34 For 82Rb, 13% of nuclear decays are accompanied by a 776 MeV prompt gamma emission.35 Since prompt gammas are temporally correlated with the annihilation photons, but spatially uncorrelated, they can produce multiple (3 photon) and random coincidence events, as well as increased dead-time, and are a problem primarily for 3D acquisition.34 The effect of prompt gamma contamination on the diagnostic accuracy of 3D 82Rb MPI was recently reported by Esteves et al,36 who found improved specificity and normalcy rate after prompt gamma correction. When prompt gamma effects were neglected, the mean circumferential profiles from 19 low

Figure 3. Prompt coincidence rates (A), random fractions (B), and scatter fractions (C) vs dynamic Frame Time from rest and regadenoson stress dynamic cardiac 82Rb PET scans in 25 normal volunteers acquired on a Siemens Biograph mCT PET/ CT scanner. The average peak prompts rate at Frame Time 40 s corresponds to an average peak singles rate of 50 ± 8 Mcps. (mean ± SD, dotted lines indicate minimum and maximum).

the average scatter fraction was 15-20% higher than that of the myocardial phase, with 50% greater standard deviations. In this particular case, the scanner vendor simply limits the per-frame scatter fraction to a maximum of 75% to avoid gross overestimation of scatter; however, the accuracy of this arbitrary limit and

Figure 4. Mean count densities (A) and standard deviations (B) from the mid-left ventricular circumferential profile of the normal file generated with 19 low likelihood patients. (Reproduced from Figure 4 of Esteves et al.36 with kind permission from Springer Science and Business Media.).

Moody et al Clinical PET MBF: technical perspective

likelihood patients exhibited focally reduced count density and significantly greater variability, particularly in the septal wall, which was due to overcorrection of scatter36 (Figure 4). A recent multicenter trial compared phantom scans and visual interpretation of 3D 82Rb MPI from seven different PET/CT scanners, of which three had prompt gamma correction.37 Consistent results were obtained for visually scored summed stress, rest, and difference scores across all scanners, although prompt gamma correction was not directly evaluated.37 The correction method used by Esteves et al36 was reported in a conference abstract by the vendor (Siemens),38 but until now a clinical validation has not appeared in the literature. Prompt gamma correction is also available on recent GE PET scanners (600 series) but that correction method has not yet been reported in the literature.37 Since the correction is not yet standard, it will be important for 3D cardiac PET studies to explicitly state whether or not prompt gamma correction was applied to the data. At present, there are no published data on the effect of 82Rb prompt gamma correction on the precision and accuracy of MBF. Image Reconstruction FBP vs OSEM reconstruction. Filtered back projection (FBP) is a linear reconstruction method that produces images with well-understood noise properties39-41 When appropriate PET corrections are applied, FBP images can be quantitatively accurate for regionsof-interest large enough to be minimally affected by partial volume effects (dimension greater than two times the PET scanner resolution, or 8-10 mm for currentgeneration PET scanners).42 However, iterative (nonlinear) image reconstruction using ordered subsets expectation maximization (OSEM) is usually preferred for cardiac PET to reduce artifacts arising from extracardiac activity and to improve the noise properties of low count dynamic frames.23 When 2D OSEM reconstruction has been compared to FBP (either for 2D PET data or for 3D PET data after 2D rebinning), comparable MBF values have generally been obtained if appropriate numbers of iterations and subsets were used43-45 However, 3D OSEM reconstruction of 3D PET data, while potentially providing better resolution and noise properties than 2D OSEM, can produce biased regional activity concentration estimates in low count PET data42,46-50 Further, this low count bias depends on activity distribution, OSEM iterations and subsets, and noise equivalent counts in the data.42,49 In dynamic cardiac PET, the low count bias can produce effects that are similar to partial volume effects within a cold region adjacent to a hotter region.42,48 However, the effect is distinct from the partial volume effect and may

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confound conventional partial volume corrections based on an assumed myocardial wall thickness.51 The low count bias has most often been observed with 3D OSEM reconstruction of dynamic brain PET data.42,49 Presotto et al50 have assessed this effect in phantom studies that mimicked the conditions of dynamic cardiac 13N-ammonia PET: peak prompt coincidence rates of 2-3.5 Mcps during the early blood-pool phase with 50% random fraction and 300 kcps during the later tissue uptake phase with 17% random fraction (note these conditions are somewhat different than 82Rb (Figure 3) where higher peak prompt rates of 3-5 Mcps and random fractions are greater than 50% were observed during the first 2-3 minutes). They found a positive bias in the myocardium during the early bloodpool phase which depends on iterations and frame length, and for which the OSEM bias was 40-85% higher than the FBP bias.50 Although they did not fully analyze the LV blood-pool bias, they observed that the LV blood pool was correctly quantified during the blood-pool phase, but during the later myocardial phase when the LV blood-pool activity was expected to be very low (true LV blood-pool:myocardium activity ratio of 1:20), there was a 300% bias within the LV blood pool50 which may reflect partial volume effects, OSEM low count bias, or contributions from both effects. For example, if the activity concentration in the myocardium was 50 kBq/mL during the myocardial phase, then the observed LV blood-pool concentration would be 7.5 kBq/mL, instead of the true activity of 2.5 kBq/mL. Benefits and limitations of resolution recovery modeling. Resolution recovery modeling (also called point spread function (PSF) modeling) incorporates a model of the processes that degrade spatial resolution into the iterative reconstruction in an attempt to improve image resolution.52 PSF modeling has recently become commercially available for routine clinical PET from the major PET vendors.15,16,53 The primary benefit of PSF modeling for MBF quantification is to reduce partial volume effects which can improve quantitative accuracy of regional activity estimates.52 There is some evidence that PSF modeling may also help mitigate the low-count bias mentioned in the previous section.48 However, PSF modeling is also known to slow the convergence of iterative reconstruction,52 introduce edge artifacts,54 and alter the noise properties of images.55 In general, the voxel variance is reduced and the inter-voxel correlations are increased which leads to images that visually appear smoother. In this context, it is important to distinguish between spatial noise variance (the lumpiness and noise texture in a single image), and ensemble noise variance (the statistical randomness of an ensemble of repeated images of the same subject).55 While PSF modeling

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reduces spatial noise variance, ensemble noise variance can be reduced, remain the same, or actually increase depending on inter-voxel correlations and region-ofinterest size.52,55 Clearly, this has important consequences for MBF precision and further study is required to more fully understand potential tradeoffs between precision and accuracy. Moreover, the applicability of PSF modeling to 3D 82 Rb PET data may be limited for two additional reasons: conventional PSF models that have been developed for common 18F- and 11C-based radiotracers may not account for the substantial positron range of 82 Rb; and the potential overcorrection for scatter and artificially elevated contrast arising from uncompensated prompt gamma emissions have a poorly defined relation to conventional PSF models and may lead to unexpected quantitative consequences. Further studies are needed to better delineate the scope of these potential limitations. Benefits and limitations of TOF. Time-offlight (TOF) PET acquisition provides additional information that spatially localizes positron annihilation events along the line-of-response of coincident photon pairs.56,57 This additional information depends on the timing resolution of the PET scanner, i.e., the capability to resolve differences in arrival time of pairs of coincident photons. Despite the spatial nature of the TOF information, the benefit of TOF for the best currently achievable timing resolution in commercial PET scanners (*500 ps) is not improved spatial resolution, but primarily improved signal-to-noise ratio (SNR),57,58 improved precision and accuracy of regional activity quantification,59 and improved ability to accurately recover regional activity in low count PET data.60,61 Additionally, TOF reconstruction accelerates the convergence of iterative reconstruction which can partially compensate the slower convergence when PSF modeling is also performed.58,62 Further, it has been shown that TOF reconstruction helps mitigate systematic errors or inconsistencies in the PET corrections for attenuation (for example, CT misalignment due to patient motion or CT truncation artifact), scatter, and detector normalization.63 The magnitude and extent of these benefits are directly proportional to the coincidence timing resolution of the TOF PET scanner. For 82Rb dynamic PET, an important consideration for TOF scanners is the potential degradation of timing resolution with increasing count rates. For example, a previous generation TOF PET scanner (Philips Gemini TF-16) experienced a 50% increase in timing resolution (from 600 to 900 ps) when the singles rate was increased to 50 Mcps.18 This singles rate is comparable to that of the blood-pool phase (Figure 3), and the increase in timing resolution will degrade the benefit of TOF acquisition during these

Moody et al Clinical PET MBF: technical perspective

early frames. By contrast, current-generation TOF PET scanners have improved capability to maintain shorter timing resolution at high count rates. For example, the timing resolution at a singles rate of 50 Mcps was reported to be *650 ps for the Philips Ingenuity TF 128 PET/CT16 and *540 ps for the Siemens Biograph mCT.14 For the new digital detector in the recently announced Philips Vereos TF, preliminary data indicated a timing resolution of 400 ps, remaining constant up to very high singles rates typical of 82Rb dynamic PET.64 The influence of TOF and PSF modeling on MBF quantification has been investigated in three recent studies. Presotto et al65 reported a study of 22 cardiac patients assessed with 13N-ammonia dynamic PET on a GE Discovery 690. Global MBF at rest and stress derived from 3D-OSEM reconstructions (3 or 5 iterations, 18 subsets, 4.3 mm 3D Hanning post-filter) was not significantly different than analytical reconstruction (3D reprojection, 3DRP, an FBP reconstruction method for 3D data).65 However, TOF?PSF reconstruction (5 iterations, 18 subsets, 2 mm post-filter) resulted in 5-9% higher MBF, and 13-18% lower fractional blood volume and tracer washout parameters compared to 3DRP.65 Similarly, Armstrong et al66 evaluated MBF quantification in 37 patients using 82Rb dynamic PET on a Siemens Biograph mCT. Global MBF computed from TOF?PSF reconstructions (2 iterations, 21 subsets, 6.5 mm post-filter) was 14% higher at rest and 10% higher at stress than from 3D-OSEM (3 iterations, 8 subsets, 6.5 mm post-filter). TOF?PSF also yielded arterial input functions that were an average of 20-60% lower during the first two dynamic frames, and *20% lower during the final 3 minutes of the dynamic scan.66 The results of both studies65,66 probably reflect the TOF benefits of enhanced SNR, accelerated convergence, improved low count recovery, and reduced partial volume effects and possibly reduced low count bias through the use of PSF modeling. Tomiyama et al67 compared MBF quantification between 3D-RAMLA (a special case of 3D-OSEM, 2 iterations, 33 subsets) and TOF-OSEM (3 iterations, 33 subsets) reconstruction using 13N-ammonia on a Philips Gemini TF-16 PET/CT scanner. In both normal volunteers (N = 7) and CAD patients (N = 13), TOF-OSEM produced increased rest MBF by 13-14%, while stress MBF was unchanged for normal volunteers and increased by 6-7% in CAD patients. Consequently, MFR was decreased by 11-12% in normal volunteers, and decreased by 7% in CAD patients. They used the same MBF quantification software and kinetic model as Presotto et al;65 the larger TOF increase in rest MBF observed here may be due to differences in their

Moody et al Clinical PET MBF: technical perspective

reference reconstruction method (3D-RAMLA) or PET scanner capabilities. Interestingly, they noted increased tracer uptake in the lateral wall after TOF-OSEM which may reflect the reduced sensitivity of TOF to misalignment of PET and CT.63 A potential limitation of TOF?PSF reconstruction is the 50% greater reconstruction time compared to 3DOSEM, which may impact its routine clinical utility.66 Another important issue in 82Rb studies which deserves further investigation concerns the empirical RenkinCrone model,68 which is required to map the tracer uptake parameter K1 to MBF as discussed below (Tracer Kinetic Modeling). The MBF software package employed by Armstrong et al66 (Siemens syngo.PET MBF69) incorporated the Lortie Renkin-Crone model,68 which was based on 3D data without prompt gamma correction and FBP reconstruction, and may have contributed to MBF bias in varying degrees to both 3D-OSEM and TOF?PSF cases. In all three studies, a reference standard for MBF was not available and bias was not assessed.65-67 TOF and/or PSF modeling clearly have the potential to improve the accuracy of regional activity quantification;50 however, more studies are required to better delineate the consequences for MBF accuracy and precision and clinical standardization. Image Filtering Image reconstruction with OSEM generally requires post-reconstruction filtering to control image noise. Moderate post-filtering after OSEM has previously been shown to cause minimal changes in rest and stress MBF compared to FBP reconstruction,43,44 however, aggressive smoothing produced MBF reductions of 15-20%.43 MFR was shown to be unchanged regardless of smoothing.43 When factor analysis (described in detail below) is used to determine the LV input function, the effects of post-filtering can be very different. A recent study found MBF to be increased at all levels of post-filtering by as much as 56-71%, and the variance of K1 similarly increased, while changes in MFR were minimal.70 Because of the sensitivity of factor analysis to postfiltering, care should be exercised to maintain consistent filtering when comparing MBF between different studies. Patient Motion Presotto et al50 studied the effects of motion and reconstruction methods under conditions that mimicked dynamic PET using a custom-built heart phantom with mechanical cardiac and respiratory motion.71 With motion the bias of myocardial activity estimates was

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higher than without motion due to increased partial volume effects; however, image reconstruction with TOF?PSF modeling offered the best activity quantification accuracy and precision in both cases. Moreover, linear relationships were observed between the ratio of measured to true myocardial activity and the LV:myocardium activity ratio,71 which is consistent with the assumptions of the Hutchins partial volume correction method often used for 82Rb MBF quantification.72 Recent ongoing work on methods to correct cardiac and respiratory motion has focused on comprehensive approaches that address motion in both CT and PET components.73-75 Although there are no clinical solutions at present for correction of motion during dynamic PET, there has been some recent progress in motion estimation from list-mode dynamic data.76 In addition, novel approaches of ensuring the alignment of emission and transmission images using time-of-flight information have been reported77,78 which may potentially improve MBF precision and accuracy and clinical reliability. Tracer Kinetic Modeling Image preprocessing. Prior to kinetic modeling, conventional pre-processing of dynamic cardiac PET images consists of reorienting the left ventricle to short-axis orientation, identification of the boundaries of the left ventricular myocardium, and mapping samples of regional myocardial activity concentration to a 2D polar map representation,79 yielding a dynamic sequence of polar maps from which regional tissue time-activity curves can be extracted.24 Both the processes of image reorientation and polar map sampling involve interpolation which introduces smoothing to the data, and should be considered in addition to post-reconstruction image filtering. An image-based arterial input function80 is typically estimated by placing regions-of-interest within the LV blood pool on each dynamic image. Although this simple pre-processing procedure has been in use for more than 30 years, it still remains the most commonly used approach perhaps because of its simplicity and robustness. More recent software implementations have largely automated the procedure, reducing the variability incurred by manual processing and making it suitable for routine clinical MBF quantification.81 Partial volume effects. Since the arterial input function and tissue time-activity curves are subject to partial volume effects, some form of partial volume correction is generally recommended to recover accurate activity concentration estimates.82 A basic method requires an estimate of the scanner’s point spread function and the myocardial wall thickness, which, in

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practice, is often assumed to be 10 mm.51,83 Partial volume effects arise not only from limited spatial resolution, but also through cardiac and respiratory motion, which can be addressed by the Hutchins geometric correction method.72 This method has been widely used for 82Rb MBF quantification.81,84 The method assumes that the left ventricular arterial input function is accurately known; although it can be very effective at reducing partial volume bias of the tissue time-activity curves, the common practice of centering the myocardial region-of-interest on the midwall85 may lead to biased kinetic parameter estimates.72,84 More recently, improved factor analysis methods have been applied to 82Rb dynamic cardiac PET86-89 to automatically extract more accurate input functions and tissue time-activity curves with ostensibly less partial volume bias. An inherent limitation of these methods is that the number of factors, representing physiological regions with distinct temporal behavior in the dynamic sequence, must be pre-specified.86,87 Typically, three factors are defined (blood pool in the right (RV) and left (LV) ventricles, and myocardial tissue) but this assumes a uniform factor for the entire myocardium and does not allow for regional variations that might occur with perfusion defects. To work around this limitation, the tissue factor is usually discarded and the measured tissue time-activity curves are used instead,87 although this can lead to inconsistent data. Alternatively, a ‘‘hybrid’’ method similar to the conventional approach entails using the factors corresponding to RV and LV blood pools to determine time-activity curves for the RV and LV by localizing conventional regions-of-interest in the dynamic images.90 Another promising approach based on this ‘‘hybrid’’ method using independent component analysis instead of factor analysis has recently been proposed and validated.91 Model-based methods. The most commonly used kinetic model for 82Rb MBF quantification is the one-tissue (1T) compartmental model68,92-94 which depends on two parameters: a tracer uptake parameter (K1), and a tracer washout parameter (k2). This model is a simplification of two-tissue (2T) compartment models previously developed for 82Rb95-98 and represents a compromise between higher precision but increased bias of K1 estimates.92 For the 1T compartmental model, K1 can be mathematically related to MBF by the generalized Renkin-Crone relation which corrects for bias and decreased tracer extraction at hyperemic flows.6,99 In general, the variance of MBF will depend on the variance of K1. K1 variance ultimately depends on the variance in the underlying images, which in turn depends on the image reconstruction method, PET corrections, counting statistics and losses in the sinogram data, and injected activity. Intuitively, the

Moody et al Clinical PET MBF: technical perspective

statistical uncertainty in the underlying images increases as the tracer decays during the dynamic PET sequence.100 The variance of K1 will correctly reflect the statistical uncertainty in the underlying images when good estimates of regional image variance are used as weights in the model parameter estimation problem.21 Although the noise properties of PET images have been well-studied40,41,101-104 detailed analysis of image variance is not clinically available and approximations are typically used100,105 which can limit the accuracy of K1 variance estimates. A simple approximation advocated by Lammertsma100 is to use weights estimated as the total (whole scanner) true coincidence events per frame (non-decay-corrected) divided by frame duration. An alternative model-based method that does not require parameter estimation is the retention model99 which simply computes the ratio of the equilibrium myocardial tracer concentration to the area under the LV input function curve in the first 2 minutes. The model also requires measured or assumed partial volume correction factors and an empirically determined Renkin-Crone relation.99 The retention model has compared favorably to compartmental models in animal studies99,106 and has also been used in large clinical studies.107,108 Renkin-Crone extraction model. In practice, the relationship between K1 and MBF for a given tracer can be empirically determined by fitting the generalized Renkin-Crone equation to independent measurements of K1 and MBF in a set of volunteers.93,99 This empirical relationship can then be used to map K1 to MBF in subsequent PET exams.24,68,93,94 One approach to utilizing the empirical Renkin-Crone relation is to substitute K1 in the 1T model with the Renkin-Crone relation and directly estimate MBF.99,109 In this case, MBF variance can also be obtained from the parameter estimation procedure. However, a disadvantage of substituting the Renkin-Crone relation for K1 is that the 1T kinetic model, which is linear in K1, 110,111 becomes nonlinear in MBF after substitution. This can lead to difficulties and failed fits for noisy data, such as occurs when fitting parametric polar maps of MBF. Alternatively, a K1 estimate may be directly mapped to MBF with a simple lookup table computed using the RenkinCrone relation. However, both approaches neglect the statistical uncertainty of the empirical Renkin-Crone relation itself. Intuitively, the nonlinear tracer extraction at hyperemic MBF leads to reduced sensitivity to distinguish small changes in MBF and noise amplification in the MBF estimate. A difficulty in quantitatively assessing this intuition is that the mathematical form of the Renkin-Crone equation prevents accurate computation of MBF variance by standard error propagation. A novel

Moody et al Clinical PET MBF: technical perspective

Figure 5. The fraction of global MBF coefficient of variation   CVðK1 Þ (CV) due to the Renkin-Crone relation 1  CVðMBFÞ as a function of CV(K1) for 82Rb68 (A), and 13N-ammonia142 (B). The shaded region (top) indicates a typical range of CV(K1) observed for clinical 82Rb data (4-13%). MBF, myocardial blood flow (mL/min/g).

analytical formula has recently been proposed112 that allows direct determination of MBF variance, accounting for the statistical uncertainty in the Renkin-Crone relation. Using this formula, the variance contribution of the Renkin-Crone relation to the overall MBF variance can be quantitatively assessed (Figure 5). For 82Rb at rest (MBF = 1.0 mL/min/g), the Renkin-Crone relation contributes between 50% and 70% of the coefficient of variation in global MBF, while at stress (MBF = 3.0) it contributes 40-50%, depending on the variance of K1 (Figure 5). Currently, the most widely used Renkin-Crone relation for 82Rb MBF quantification is the Lortie model.68 As noted above, this model is based on 3D PET data (CTI/Siemens ECAT ART PET scanner) without prompt gamma correction reconstructed by FBP after 2D Fourier rebinning.68 The appropriateness of using

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this model with 3D PET data with prompt gamma correction reconstructed by 3D TOF and/or PSF modeling has not yet been established and represents a potentially major limitation to the accuracy of MBF quantification. Software implementations. A number of software packages for MBF quantification have appeared, as detailed in a recent review,113 including five commercial packages with current regulatory approval for clinical use (FDA and/or CE): cfrQuant (Positron114), Corridor4DM (INVIA115), ImagenQ (CVIT116), QPET (Cedars-Sinai117), and syngo.PET MBF (Siemens118). In addition, numerous research packages are also available.113 These software packages have been compared to one another in recent studies65,85,119-122 DeKemp et al85 studied three commercial software packages with automatic processing and the same conventional preprocessing and kinetic model. They processed the same 90 dynamic rest/stress PET studies, achieving an average MBF repeatability coefficient (RPC) of 0.26 mL/ min/g (rest and stress together) and MFR RPC of 0.29. The MBF RPC was similar to the statistical error in 82Rb dynamic data as indicated by test-retest results at rest.123,124 They concluded that good clinical reproducibility was obtained when software packages used similar preprocessing methods and the same kinetic model.85 Tahari et al120 examined four different software methods among three software packages, and found that MBF was significantly different between the methods. While MFR was not statistically different between methods, the pairwise agreement between methods using the binary normal MFR threshold of 2.0 was in the range 76-90%.120 Murthy et al90 compared 5 different Renkin-Crone extraction models and three different preprocessing methods in a group of 2,783 consecutive patients, and found that the relationship between cardiac mortality and stress MBF was variable depending on the input function method and extraction model, whereas the relationship between MFR and risk was highly consistent (Figure 6). In the study of Nesterov et al,122 the agreement of 82 Rb MBF and MFR was evaluated across ten different software packages using a common clinical dataset consisting of 48 patients with suspected or known CAD. The predefined criteria for agreement were (i) pairwise interclass correlation coefficient greater than or equal to 0.75, and (ii) pairwise MBF and MFR differences within 20% of the observed median values across all evaluated software packages. The latter criterion was chosen to be similar to test-retest repeatability results for 82Rb rest MBF reported by Efseaff et al124 who compared different processing approaches and obtained a bestcase test-retest repeatability coefficient (RPC) of 0.20

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Moody et al Clinical PET MBF: technical perspective

pffiffiffi would be increased to 38% ( 2  27%). Note that the majority of recent studies that have evaluated the clinical utility of MBF or MFR1-5,107,125 have each done so using a single software package, without the additional variability incurred by the choice of software and kinetic modeling methods. Summary and Recommendations The six major methodological factors considered in detail above that can impact MBF precision and accuracy are summarized in Table 1, along with specific recommendations to move the routine utilization of 82Rb MBF quantification forward in the clinic. For most of these factors, further data are needed to define their relative importance for MBF quantification. It is likely that weight-based 82Rb doses will need to be PET scanner-specific due to the wide variability in scanner performance, and some older scanners may be limited in their ability to quantify MBF and MFR with acceptable precision (Figure 1). In general, standardization of PET acquisition, image reconstruction, and processing protocols must take into consideration the count rate capabilities of the PET scanner and be specifically optimized for dynamic cardiac imaging.

Figure 6. Decreasing stress myocardial blood flow (MBF) (A) and myocardial flow reserve (MFR) (B) are both associated with markedly increased annual rates of cardiac death. However, the relationship for MFR is relatively consistent regardless of kinetic model used for rubidium-82, compared to substantial variability in the corresponding relationship between stress MBF and cardiac death. In all cases, the curves were generated by sampling the blood-pool input function from regions-of-interest spanning the mitral valve. Figure derived from data reported in Murthy et al90.

(24% of mean rest MBF) which was comparable to that obtained by Manabe et al123 (0.19, 24%) (Table 2). These RPC values represent the 95% confidence limits within which short-term repeated rest MBF measurements would be observed (a larger RPC implies a higher variability). Since Efseaff et al124 and Manabe et al123 each used one software package to quantify MBF, these RPC values represent the variance of the MBF data apart from the choice of software. If the choice of software and kinetic model contributes an additional variance component of the same magnitude as the MBF data variance, then the overall rest MBF RPC, after accounting for both MBF pffiffidata ffi variance and choice of software, would be 34% ( 2  24%). Similarly, using the stress MBF RPC reported by Manabe et al,123 the overall stress MBF RPC (including variance due to data and software)

CURRENT PROSPECTS FOR CLINICAL UTILITY OF MBF AND MFR In Table 2, the test-retest repeatability of MBF is shown as measured by the repeatability coefficient (RPC) from the literature over the period 1999-2012. The RPC values have remained somewhat consistent across different radiotracers although the variability in these RPC estimates is evident in their relatively wide 95% confidence intervals. However, all of these studies (except Efseaff et al124) were acquired in 2D mode on older BGO-based PET scanners and utilized FBP reconstruction. Therefore, these RPC values may not represent the MBF repeatability (precision) that is achievable on current-generation 3D PET scanners with advanced reconstruction methods. In this context, it is worth mentioning that a commercial BGO-based dedicated cardiac PET scanner is currently available (Attrius, Positron Corporation) with line-source attenuation correction and the capability to measure MFR114 using the retention model discussed above (‘‘Model-based methods’’ section). Although it has a lower cost compared to general purpose PET/CT scanners, a performance evaluation of the Attrius has not yet appeared in the literature. The methodology for clinical quantification of MBF and MFR has evidently improved with the use of newer

Moody et al Clinical PET MBF: technical perspective

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Table 2. Test-retest repeatability coefficients for myocardial blood flow from the literature

Rest Reference


Kaufmann et al.132 Wyss et al.133 Siegrist et al.134 Schindler et al.135 Manabe et al.123 Efseaff et al.124 Efseaff et al.124

21 11 20 20 15 36 36



O-water O-water 15 O-water 13 N-ammonia 82 Rb 82 Rb 82 Rb

0.17 0.26 – 0.26 0.19 0.28 0.20

15 15

95% CI [0.11, [0.13, – [0.17, [0.11, [0.21, [0.15,

0.23] 0.39] 0.35] 0.27] 0.35] 0.25]

Stress %


18 21 – 39 24 33 24

0.94 1.34 0.41* 0.28* 0.92 – –

95% CI [0.63, [0.67, [0.27, [0.19, [0.55, – –

1.25] 2.01] 0.55] 0.37] 1.29]

% 25 27 28 32 27 – –

Repeatability coefficients (RPC) for short-term test-retest MBF that have been reported in the literature for different flow tracers with 95% confidence intervals (mL/min/g) (N, number of patients; %, RPC as a percentage of mean MBF)  Efseaff et al compared several processing methods; the RPC values shown in the table were obtained using the best-case method OSEM-6-SOC with the left ventricular input function (0.28) and left atrial input function (0.20) Stress methods: (*) cold pressor testing; all other studies used adenosine

PET technology. For example in 1998, Muzik et al126 studied the diagnostic utility of 13N-ammonia MBF and MFR for detection of CAD, and concluded that ‘‘it does not appear to be intrinsically more accurate than semiquantitative static PET analysis…’’ and ‘‘given the relatively complex analysis techniques required, routine clinical use of this technique does not currently appear warranted.’’ By contrast, a more recent study by Fiechter et al127 found that 13N-ammonia MFR significantly improved the diagnostic sensitivity, accuracy, and negative predictive value for detecting coronary artery disease compared to conventional MPI without impairing the specificity. Similarly, MFR quantified by 82 Rb PET has been recently reported to improve the detection of multivessel disease128 and provide high negative predictive value for excluding high-risk coronary artery disease on angiography.129 The currently available data have shown that MFR is largely unaffected by the various methodological sources of error discussed above,66,85,120,121 suggesting that MFR may be more reproducible than MBF across the broad range of PET scanners and variations in methodology found in current clinical practice. However, the clinical studies that have demonstrated the prognostic utility of MFR have primarily been singlecenter studies.4,5,90 Stress MBF on its own has several practical advantages compared to MFR, including a simplified imaging protocol and lower radiation dose to the patient since a resting study is not needed.113 In recent studies, stress MBF was shown to be a promising tool for evaluating coronary artery disease,130,131 but more data are needed as well as a high degree of methodological standardization if absolute MBF is to achieve the level of precision, accuracy, and robustness required to be a clinically viable tool.

CONCLUSIONS An important challenge illustrated by the steady improvements in PET scanner count rate performance (Figure 1) is the ever widening range of scanner capabilities. Along with the highly capable currentgeneration PET/CT systems (e.g., the GE Discovery 690, Philips Ingenuity TF 128, and Siemens Biograph mCT), the prevalence of previous-generation 2D and 3D PET scanners—primarily from the secondary market of used scanners—with inherent limitations for dynamic PET, will likely contribute to increased clinical variability in absolute MBF and increased difficulty in standardizing clinical MBF thresholds. Meeting this challenge will require greater attention to and better understanding of the effects of newer PET technologies on MBF precision and accuracy. Much can be learned from current collaborative efforts in the oncological PET community to standardize SUV quantification of FDG PET (, and it is expected that similar standardization initiatives will benefit clinical application of MBF quantification. Although MFR is less sensitive to methodological errors, more multicenter studies are needed to validate its clinical utility. As PET instrumentation and methodology continue to improve, the contribution and importance of these various factors will also change. With continued improvements in spatial resolution and PSF modeling, partial volume effects may become less important. With continued improvements in coincidence timing resolution, eventually simple TOF back projection may be all that is required for accurate image reconstruction.61 The clinical utility of MBF and MFR quantification depends on both a detailed understanding of these methodological factors, as well as a standardization process that

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can accommodate technological changes and future innovations while maintaining quantitative precision and accuracy in the clinic. Disclosure J.B. Moody and B.C. Lee are employees of INVIA Medical Imaging Solutions, E.P. Ficaro and J.R. Corbett are stockholders of INVIA Medical Imaging Solutions, and V.L. Murthy has received research support from INVIA Medical Imaging Solutions, which produces a software package for myocardial blood flow estimation. V.L. Murthy has minor stock holdings in General Electric, Mallinckrodt, and Cardinal Health.

References 1. Tio RA, Dabeshlim A, Siebelink H-MJ, Sutter J de, Hillege HL, Zeebregts CJ, et al. Comparison between the prognostic value of left ventricular function and myocardial perfusion reserve in patients with ischemic heart disease. J Nucl Med 2009;50:214-9. doi:10.2967/jnumed.108.054395. 2. Herzog BA, Husmann L, Valenta I, Gaemperli O, Siegrist PT, Tay FM, et al. Long-term prognostic value of 13N-ammonia myocardial perfusion positron emission tomography: Added value of coronary flow reserve. J Am Coll Cardiol 2009;54:1506. doi:10.1016/j.jacc.2009.02.069. 3. Ziadi MC, deKemp RA, Williams KA, Guo A, Chow BJW, Renaud JM, et al. Impaired myocardial flow reserve on rubidium82 positron emission tomography imaging predicts adverse outcomes in patients assessed for myocardial ischemia. J Am Coll Cardiol 2011;58:740-8. doi:10.1016/j.jacc.2011.01.065. 4. Murthy VL, Naya M, Foster CR, Hainer J, Gaber M, Di Carli G, et al. Improved cardiac risk assessment with noninvasive measures of coronary flow reserve. Circulation 2011;124:2215-24. doi:10.1161/CIRCULATIONAHA.111.050427. 5. Murthy VL, Naya M, Foster CR, Gaber M, Hainer J, Klein J, et al. Association between coronary vascular dysfunction and cardiac mortality in patients with and without diabetes mellitus. Circulation 2012;126:1858-68. doi:10.1161/CIRCULATIONAHA.112.120402. 6. Klein R, Beanlands R, deKemp R. Quantification of myocardial blood flow and flow reserve: Technical aspects. J Nucl Cardiol 2010;17:555-70. 7. Lewellen TK. The challenge of detector designs for PET. Am J Roentgenol 2010;195:301-9. doi:10.2214/AJR.10.4741. 8. Budinger TF, Derenzo SE, Huesman RH, Cahoon JL. Medical criteria for the design of a dynamic positron tomograph for heart studies. IEEE Trans Nucl Sci 1982;29:488. 9. Mullani NA, Gaeta J, Yerian K, Wong W-H, Hartz RK, Philippe EA, et al. Dynamic imaging with high resolution time-of-flight PET camera—TOFPET I. IEEE Trans Nucl Sci 1984;31:609-13. doi:10.1109/TNS.1984.4333329. 10. Ter-Pogossian MM, Ficke DC, Beecher DE, Hoffman GR, Bergmann SR. The super PET 3000-E: A PET scanner designed for high count rate cardiac applications. J Comput Assist Tomogr 1994;18:661-9. 11. Strother SC, Casey ME, Hoffman EJ. Measuring PET scanner sensitivity: Relating countrates to image signal-to-noise ratios using noise equivalents counts. IEEE Trans Nucl Sci 1990;37: 783-8. doi:10.1109/23.106715.

Moody et al Clinical PET MBF: technical perspective

12. Germano G, Hoffman EJ. Investigation of count rate and deadtime characteristics of a high resolution PET system. J Comput Assist Tomogr 1988;12:836-46. 13. Germano G, Hoffman EJ. A study of data loss and mispositioning due to pileup in 2-D detectors in PET. IEEE Trans Nucl Sci 1990;37:671-5. doi:10.1109/23.106696. 14. Jakoby BW, Bercier Y, Conti M, Casey ME, Bendriem B, Townsend DW. Physical and clinical performance of the mCT time-of-flight PET/CT scanner. Phys Med Biol 2011;56:2375. doi:10.1088/0031-9155/56/8/004. 15. Bettinardi V, Presotto L, Rapisarda E, Picchio M, Gianolli L, Gilardi MC. Physical performance of the new hybrid PET/CT discovery-690. Med Phys 2011;38:5394-411. doi:10.1118/ 1.3635220. 16. Kolthammer JA, Su K-H, Grover A, Narayanan M, Jordan DW, Muzic RF. Performance evaluation of the Ingenuity TF PET/CT scanner with a focus on high count-rate conditions. Phys Med Biol 2014;59:3843. doi:10.1088/0031-9155/59/14/3843. 17. Conti M, Bendriem B, Casey M, Chen M, Kehren F, Michel C, et al. First experimental results of time-of-flight reconstruction on an LSO PET scanner. Phys Med Biol 2005;50:4507. doi: 10.1088/0031-9155/50/19/006. 18. Surti S, Kuhn A, Werner ME, Perkins AE, Kolthammer J, Karp JS. Performance of Philips Gemini TF PET/CT scanner with special consideration for its time-of-flight imaging capabilities. J Nucl Med 2007;48:471-80. 19. Seo Y, Teo B-K, Hadi M, Schreck C, Bacharach SL, Hasegawa BH. Quantitative accuracy of PET/CT for image-based kinetic analysis. Med Phys 2008;35:3086-9. doi:10.1118/1.2937439. 20. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1:307-10. 21. Carson RE. Tracer kinetic modeling in PET. In: Bailey DL, Townsend DW, Valk PE, Maisey MN, editors. Positron emission tomography. London: Springer; 2005. p. 127-59. 22. Raylman RR, Caraher JM, Hutchins GD. Sampling requirements for dynamic cardiac PET studies using image-derived input functions. J Nucl Med 1993;34:440-7. 23. Di Carli MF, Dorbala S, Meserve J, El Fakhri G, Sitek A, Moore SC. Clinical myocardial perfusion PET/CT. J Nucl Med 2007;48: 783-93. 24. Schelbert HR. Positron emission tomography of the heart: Methodology, findings in the normal and the diseased heart, and clinical applications. In: Phelps ME, editor. PET: Molecular imaging and its biological applications. 1st ed. New York: Springer; 2004. p. 389-508. 25. Kolthammer JA, Muzic RF. Optimized dynamic framing for PET-based myocardial blood flow estimation. Phys Med Biol 2013;58:5783. doi:10.1088/0031-9155/58/16/5783. 26. Lee B, Moody J, Murthy V, Corbett J, Ficaro E. Effects of temporal sampling on PET myocardial blood flow estimates. Soc Nucl Med Annu Meet Abstr 2014;55:1775. 27. Klein R, Adler A, Beanlands RS, deKemp RA. Precision-controlled elution of a 82Sr/82Rb generator for cardiac perfusion imaging with positron emission tomography. Phys Med Biol 2007;52:659-73. doi:10.1088/0031-9155/52/3/009. 28. deKemp RA, Yoshinaga K, Beanlands RSB. Will 3-dimensional PET-CT enable the routine quantification of myocardial blood flow? J Nucl Cardiol 2007;14:380-97. 29. Tout D, Tonge C, Muthu S, Arumugam P. Assessment of a protocol for routine simultaneous myocardial blood flow measurement and standard myocardial perfusion imaging with rubidium-82 on a high count rate positron emission tomography system. Nucl Med Commun 2012;33:1202-11. doi:10.1097/MNM.0b013e3283567554.

Moody et al Clinical PET MBF: technical perspective

30. Cheng J-C, Blinder S, Rahmim A, Sossi V. A Scatter calibration technique for dynamic brain imaging in high resolution PET. IEEE Trans Nucl Sci 2010;57:225-33. doi:10.1109/TNS.2009.2031643. 31. Watson CC. Extension of single scatter simulation to scatter correction of time-of-flight PET. IEEE Trans Nucl Sci 2007;54:1679-86. doi:10.1109/TNS.2007.901227. 32. Walker MD, Sossi V. Commentary: An eye on PET quantification. Mol Imaging Biol 2014;17:1-3. doi:10.1007/s11307-014-0791-7. 33. Rajaram M, Tahari AK, Lee AH, Lodge MA, Tsui B, Nekolla S, et al. Cardiac PET/CT misregistration causes significant changes in estimated myocardial blood flow. J Nucl Med 2013;54:50-4. doi:10.2967/jnumed.112.108183. 34. Martin CC, Christian BT, Satter MR, Nickerson LDH, Nickles RJ. Quantitative PET with positron emitters that emit prompt gamma rays. IEEE Trans Med Imaging 1995;14:681-7. doi: 10.1109/42.476109. 35. Cherry SR, Sorenson J, Phelps M. Physics in nuclear medicine, vol. 3. Philadelphia: Saunders; 2003. 36. Esteves FP, Nye JA, Khan A, Folks RD, Halkar RK, Garcia EV, et al. Prompt-gamma compensation in Rb-82 myocardial perfusion 3D PET/CT. J Nucl Cardiol 2010;17:247-53. 37. Renaud JM, Mylonas I, McArdle B, Dowsley T, Yip K, Turcotte E, et al. Clinical interpretation standards and quality assurance for the multicenter PET/CT Trial: 82Rb as an alternative radiopharmaceutical for myocardial imaging. J Nucl Med 2014. doi: 10.2967/jnumed.112.117515. 38. Watson C, Hayden C, Casey M, Hamill J, Bendriem B. Prompt gamma correction for improved quantification in 82Rb PET. Soc Nucl Med Annu Meet Abstr 2008;49:64P. 39. Alpert NM, Chesler D, Correia J, Ackerman RH, Chang JY, Finklestein S, et al. Estimation of the local statistical noise in emission computed tomography. IEEE Trans Med Imaging 1982;1:142-6. doi:10.1109/TMI.1982.4307561. 40. Huesman RH. A new fast algorithm for the evaluation of regions of interest and statistical uncertainty in computed tomography. Phys Med Biol 1984;29:543-52. 41. Carson RE, Yan Y, Daube-Witherspoon ME, Freedman N, Bacharach SL, Herscovitch P. An approximation formula for the variance of PET region-of-interest values. IEEE Trans Med Imaging 1993;12:240-50. 42. Van Velden FHP, Kloet RW, van Berckel BNM, Lammertsma AA, Boellaard R. Accuracy of 3-dimensional reconstruction algorithms for the high-resolution research tomograph. J Nucl Med 2009;50:72-80. doi:10.2967/jnumed.108.052985. 43. Chen GP, Branch KR, Alessio AM, Pham P, Tabibiazar R, Kinahan P, et al. Effect of reconstruction algorithms on myocardial blood flow measurement with 13N-ammonia PET. J Nucl Med 2007;48:1259-65. doi:10.2967/jnumed.106.038232. 44. Søndergaard HM, Madsen MM, Boisen K, Bøttcher M, Schmitz O, Nielsen TT, et al. Evaluation of iterative reconstruction (OSEM) versus filtered back-projection for the assessment of myocardial glucose uptake and myocardial perfusion using dynamic PET. Eur J Nucl Med Mol Imaging 2007;34:320-9. doi: 10.1007/s00259-006-0198-z. 45. Kobayashi M, Mori T, Kiyono Y, Tsujikawa T, Maruyama R, Higaki Y, et al. Appropriate parameters of the ordered-subset expectation maximization algorithm on measurement of myocardial blood flow and oxygen consumption with 11C-acetate PET. Nucl Med Commun 2012;33:130-8. doi:10.1097/MNM.0b013e328 34e7f5c. 46. Boellaard R, van Lingen A, Lammertsma AA. Experimental and clinical evaluation of iterative reconstruction (OSEM) in dynamic PET: Quantitative characteristics and effects on kinetic modeling. J Nucl Med 2001;42:808-17.

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47. Reilhac A, Tomeı¨ S, Buvat I, Michel C, Keheren F, Costes N. Simulation-based evaluation of OSEM iterative reconstruction methods in dynamic brain PET studies. NeuroImage 2008;39:359-68. doi:10.1016/j.neuroimage.2007.07.038. 48. Walker MD, Asselin M-C, Julyan PJ, Feldmann M, Talbot PS, Jones T, et al. Bias in iterative reconstruction of low-statistics PET data: Benefits of a resolution model. Phys Med Biol 2011;56:931. doi:10.1088/0031-9155/56/4/004. 49. Jian Y, Carson RE. Effect of subsets on bias and variance in lowcount iterative PET reconstruction. In: 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 2013. p. 1-4. doi:10.1109/NSSMIC.2013.6829220. 50. Presotto L, Gianolli L, Gilardi MC, Bettinardi V. Evaluation of image reconstruction algorithms encompassing time-of-flight and point spread function modelling for quantitative cardiac PET: Phantom studies. J Nucl Cardiol 2014. doi:10.1007/s12350014-0023-1. 51. Henze E, Huang S-C, Ratib O, Hoffman E, Phelps ME, Schelbert HR. Measurements of regional tissue and blood-pool radiotracer concentrations from serial tomographic images of the heart. J Nucl Med 1983;24:987-96. 52. Rahmim A, Qi J, Sossi V. Resolution modeling in PET imaging: Theory, practice, benefits, and pitfalls. Med Phys 2013;40: 064301. doi:10.1118/1.4800806. 53. Jakoby BW, Bercier Y, Watson CC, Bendriem B, Townsend DW. Performance characteristics of a New LSO PET/CT scanner with extended axial field-of-view and PSF reconstruction. IEEE Trans Nucl Sci 2009;56:633-9. doi:10.1109/TNS.2009.2015 764. 54. Tong S, Alessio AM, Thielemans K, Stearns C, Ross S, Kinahan PE. Properties and mitigation of edge artifacts in PSF-based PET reconstruction. IEEE Trans Nucl Sci 2011;58:2264-75. doi: 10.1109/TNS.2011.2164579. 55. Tong S, Alessio AM, Kinahan PE. Noise and signal properties in PSF-based fully 3D PET image reconstruction: An experimental evaluation. Phys Med Biol 2010;55:1453. doi:10.1088/0031-915 5/55/5/013. 56. Snyder DL, Thomas LJ, Ter-Pogossian MM. A mathematical model for positron-emission tomography systems having timeof-flight measurements. IEEE Trans Nucl Sci 1981;28:3575-83. doi:10.1109/TNS.1981.4332168. 57. Budinger TF. Time-of-flight positron emission tomography: Status relative to conventional PET. J Nucl Med 1983;24:73-8. 58. Karp JS, Surti S, Daube-Witherspoon ME, Muehllehner G. Benefit of time-of-flight in PET: Experimental and clinical results. J Nucl Med Off Publ Soc Nucl Med 2008;49:462-70. doi: 10.2967/jnumed.107.044834. 59. Daube-Witherspoon ME, Surti S, Perkins AE, Karp JS. Determination of accuracy and precision of lesion uptake measurements in human subjects with time-of-flight PET. J Nucl Med 2014;55:602-7. doi:10.2967/jnumed.113.127035. 60. Mettivier G, Tabacchini V, Conti M, Russo P. Signal-to-noise gain at variable randoms ratio in TOF PET. IEEE Trans Nucl Sci 2012;59:1948-57. doi:10.1109/TNS.2012.2198833. 61. Westerwoudt V, Conti M, Eriksson L. Advantages of improved time resolution for TOF PET at very low statistics. IEEE Trans Nucl Sci 2014;61:126-33. doi:10.1109/TNS.2013.2287175. 62. Surti S, Karp JS, Popescu LM, Daube-Witherspoon ME, Werner M. Investigation of time-of-flight benefit for fully 3-D PET. IEEE Trans Med Imaging 2006;25:529-38. doi:10.1109/TMI. 2006.871419. 63. Conti M. Why is TOF PET reconstruction a more robust method in the presence of inconsistent data? Phys Med Biol 2011;56:155. doi:10.1088/0031-9155/56/1/010.

Journal of Nuclear Cardiology

64. Degenhardt C, Rodrigues P, Trindade A, Zwaans B, Mulhens O, Dorscheid R, et al. Performance evaluation of a prototype Positron Emission Tomography scanner using Digital Photon Counters (DPC). In: 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 2012:2820-4. doi: 10.1109/NSSMIC.2012.6551643. 65. Presotto L, Busnardo E, Bettinardi V, Landoni C, Todeschini P, Rimoldi O, et al. Evaluation of time of flight (TOF) and point spread function (PSF) reconstructions in the quantification of myocardial blood flow with 13N ammonia and PET: Comparison among reconstructions (reprojection, OSEM), software (PMOD and CARIMAS) and operators. In: 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 2012:3979-82. doi:10.1109/NSSMIC.2012.6551912. 66. Armstrong IS, Tonge CM, Arumugam P. Impact of point spread function modeling and time-of-flight on myocardial blood flow and myocardial flow reserve measurements for rubidium-82 cardiac PET. J Nucl Cardiol 2014;21:467-74. doi:10.1007/ s12350-014-9858-8. 67. Tomiyama T, Ishihara K, Suda M, Kanaya K, Sakurai M, Takahashi N, et al. Impact of time-of-flight on qualitative and quantitative analyses of myocardial perfusion PET studies using 13N-ammonia. J Nucl Cardiol 2014. doi:10.1007/s12350014-0037-8. 68. Lortie M, Beanlands RSB, Yoshinaga K, Klein R, Dasilva JN, DeKemp RA. Quantification of myocardial blood flow with 82Rb dynamic PET imaging. Eur J Nucl Med Mol Imaging 2007;34:1765-74. 69. Pan X-B, Declerck J, Burckhardt DD. Cardiac positron emission tomography: Overview of myocardial perfusion, myocardial blood flow and myocardial flow reserve imaging. Siemens Medical Solutions; 2011:1-24. 70. Lee B, Moody J, Sitek A, Murthy V, Di Carli M, Corbett J, et al. Effects of filtering on Rb-82 myocardial blood flow estimates. In: Society of Nuclear Medicine Annual Meeting Abstracts. 2013;54:1659. 71. Presotto L, Bettinardi V, Petta P, Gilardi MC. A compact dynamic phantom to assess the effect of motion in cardiac PET and SPECT studies. In: 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 2012:2638-42. doi: 10.1109/NSSMIC.2012.6551601. 72. Hutchins GD, Caraher JM, Raylman RR. A region of interest strategy for minimizing resolution distortions in quantitative myocardial PET studies. J Nucl Med 1992;33:1243-50. 73. Scha¨fers KP, Stegger L. Combined imaging of molecular function and morphology with PET/CT and SPECT/CT: Image fusion and motion correction. Basic Res Cardiol 2008;103:191-9. doi: 10.1007/s00395-008-0717-0. 74. Mohy-ud-Din H, Karakatsanis NA, Goddard JS, Baba J, Wills W, Tahari AK, et al. Generalized dynamic PET inter-frame and intraframe motion correction: Phantom and human validation studies. In: 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 2012:3067-78. doi:10.1109/NSSMIC. 2012.6551701. 75. Pourmoghaddas A, Klein R, deKemp RA, Wells RG. Respiratory phase alignment improves blood-flow quantification in Rb82 PET myocardial perfusion imaging. Med Phys 2013;40:022503. doi:10.1118/1.4788669. 76. Schleyer PJ, Thielemans K, Marsden PK. Extracting a respiratory signal from raw dynamic PET data that contain tracer kinetics. Phys Med Biol 2014;59:4345. doi:10.1088/0031-9155/59/ 15/4345. 77. Defrise M, Rezaei A, Nuyts J. Time-of-flight PET data determine the attenuation sinogram up to a constant. Phys Med Biol 2012;57:885. doi:10.1088/0031-9155/57/4/885.

Moody et al Clinical PET MBF: technical perspective

78. Rezaei A, Nuyts J. Simultaneous reconstruction of the activity image and registration of the CT image in TOF-PET. In: 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). 2013. 79. Garcia EV, Van Train K, Maddahi J, Prigent F, Friedman J, Areeda J, et al. Quantification of rotational thallium-201 myocardial tomography. J Nucl Med 1985;26:17-26. 80. Weinberg IN, Huang SC, Hoffman EJ, Araujo L, Nienaber C, McKay MG, et al. Validation of PET-acquired input functions for cardiac studies. J Nucl Med 1988;29:241-7. 81. Klein R, Renaud JM, Ziadi MC, Thorn SL, Adler A, Beanlands RS, et al. Intra- and inter-operator repeatability of myocardial blood flow and myocardial flow reserve measurements using rubidium-82 PET and a highly automated analysis program. J Nucl Cardiol 2010;17:600-16. doi:10.1007/s12350-010-9225-3. 82. Hoffman EJ, Huang SC, Phelps ME. Quantitation in positron emission computed tomography: 1. Effect of object size. J Comput Assist Tomogr 1979;3:299-308. doi:10.1097/00004728197906000-00001 83. Nuyts J, Maes A, Vrolix M, Schiepers C, Schelbert H, Kuhle W, et al. Three-dimensional correction for spillover and recovery of myocardial PET images. J Nucl Med 1996;37:767-74. 84. Coxson PG, Brennan KM, Huesman RH, Lim S, Budinger TF. Variability and reproducibility of rubidium-82 kinetic parameters in the myocardium of the anesthetized canine. J Nucl Med 1995;36:287-96. 85. deKemp RA, Declerck J, Klein R, Pan X-B, Nakazato R, Tonge C, et al. Multisoftware reproducibility study of stress and rest myocardial blood flow assessed with 3D dynamic PET/CT and a 1-tissue-compartment model of 82Rb kinetics. J Nucl Med 2013;54:571-7. 86. Sitek A, Gullberg GT, Huesman RH. Correction for ambiguous solutions in factor analysis using a penalized least squares objective. IEEE Trans Med Imaging 2002;21:216-25. 87. El Fakhri G, Sitek A, Guerin B, Kijewski MF, Di Carli MF, Moore SC. Quantitative dynamic cardiac 82Rb PET using generalized factor and compartment analyses. J Nucl Med 2005;46:1264-71. 88. El Fakhri G, Kardan A, Sitek A, Dorbala S, Abi-Hatem N, Lahoud Y, et al. Reproducibility and accuracy of quantitative myocardial blood flow assessment with 82Rb PET: Comparison with 13N-ammonia PET. J Nucl Med 2009;50:1062-71. doi: 10.2967/jnumed.104.007831. 89. Klein R, Beanlands RS, Wassenaar RW, Thorn SL, Lamoureux M, DaSilva JN, et al. Kinetic model-based factor analysis of dynamic sequences for 82-rubidium cardiac positron emission tomography. Med Phys 2010;37:3995-4010. 90. Murthy VL, Lee BC, Sitek A, Naya M, Moody J, Polavarapu V, et al. Comparison and prognostic validation of multiple methods of quantification of myocardial blood flow with 82Rb PET. J Nucl Med 2014;55:1952-8. doi:10.2967/jnumed.114.145342. 91. Germino M, Ropchan J, Mulnix T, Najafzadeh S, Ackah E, Feringa H, et al. Generation of parametric images from timeof-flight 82Rb and 15O-water cardiac PET with ICA-derived input functions. Soc Nucl Med Annu Meet Abstr 2014; 55:46. 92. Coxson PG, Huesman RH, Borland L. Consequences of using a simplified kinetic model for dynamic PET data. J Nucl Med 1997;38:660-7. 93. Van den Hoff J, Burchert W, Borner A-R, Fricke H, Kuhnel G, Meyer GJ, et al. [1-11C]Acetate as a quantitative perfusion tracer in myocardial PET. J Nucl Med 2001;42:1174-82. 94. Prior JO, Allenbach G, Valenta I, Kosinski M, Burger C, Verdun FR, et al. Quantification of myocardial blood flow with 82Rb

Moody et al Clinical PET MBF: technical perspective









103. 104.








positron emission tomography: Clinical validation with 15Owater. Eur J Nucl Med Mol Imaging 2012;39:1037-47. Goldstein RA, Mullani NA, Marani SK, Fisher DJ, Gould KL, O’Brien HA. Myocardial perfusion with rubidium-82. II. Effects of metabolic and pharmacologic interventions. J Nucl Med 1983;24:907-15. Mullani NA, Goldstein RA, Gould KL, Marani SK, Fisher DJ, O’Brien HA, et al. Myocardial perfusion with rubidium-82. I. Measurement of extraction fraction and flow with external detectors. J Nucl Med 1983;24:898-906. Herrero P, Markham J, Shelton ME, Weinheimer CJ, Bergmann SR. Noninvasive quantification of regional myocardial perfusion with rubidium-82 and positron emission tomography. Exploration of a mathematical model. Circulation 1990;82:1377-86. Herrero P, Markham J, Shelton M, Bergmann S. Implementation and evaluation of a two-compartment model for quantification of myocardial perfusion with rubidium-82 and positron emission tomography. Circ Res 1992;70:496-507. Yoshida K, Mullani N, Gould KL. Coronary flow and flow reserve by PET simplified for clinical applications using rubidium82 or nitrogen-13-ammonia. J Nucl Med 1996;37:1701-12. Lammertsma AA. Quantification of cerebral blood flow. In: Gru¨nder G, editor. Molecular Imaging in the Clinical Neurosciences. Neuromethods. New York: Humana Press; 2012. p. 99109. doi:10.1007/7657_2012_43. Alpert NM, Barker WC, Gelman A, Weise S, Senda M, Correia JA. The precision of positron emission tomography: Theory and measurement. J Cereb Blood Flow Metab 1991;11:A26-30. doi: 10.1038/jcbfm.1991.33. Fessler JA. Mean and variance of implicitly defined biased estimators (such as penalized maximum likelihood): Applications to tomography. IEEE Trans Image Process 1996;5:493-506. Qi J. A unified noise analysis for iterative image estimation. Phys Med Biol 2003;48:3505-19. doi:10.1088/0031-9155/48/21/004. Qi J, Huesman H. Theoretical study of penalized-likelihood image reconstruction for region of interest quantification. IEEE Trans Med Imaging 2006;25:640-8. doi:10.1109/TMI.2006.873223. Yaqub M, Boellaard R, Kropholler MA, Lammertsma AA. Optimization algorithms and weighting factors for analysis of dynamic PET studies. Phys Med Biol 2006;51:4217. doi: 10.1088/0031-9155/51/17/007. Lautama¨ki R, George R, Kitagawa K, Higuchi T, Merrill J, Voicu C, et al. Rubidium-82 PET-CT for quantitative assessment of myocardial blood flow: Validation in a canine model of coronary artery stenosis. Eur J Nucl Med Mol Imaging 2009;36:576-86. Johnson NP, Gould KL. Physiological basis for angina and STsegment change: PET-verified thresholds of quantitative stress myocardial perfusion and coronary flow reserve. JACC Cardiovasc Imaging 2011;4:990-8. doi:10.1016/j.jcmg.2011.06.015. Johnson NP, Gould KL. Integrating noninvasive absolute flow, coronary flow reserve, and ischemic thresholds into a comprehensive map of physiological severity. JACC Cardiovasc Imaging 2012;5:430-40. doi:10.1016/j.jcmg.2011.12.014. PMOD Technologies. User’s Guide: PMOD Cardiac Modeling (PCARD). Accessed May 23, 2013. Koeppe RA, Holden JE, Ip WR. Performance comparison of parameter estimation techniques for the quantitation of local cerebral blood flow by dynamic positron computed tomography. J Cereb Blood Flow Metab 1985;5:224-34. doi:10.1038/jcbfm. 1985.29. Kadrmas DJ, Oktay MB. Generalized separable parameter space techniques for fitting 1K-5K serial compartment models. Med Phys 2013;40:072502. doi:10.1118/1.4810937.

Journal of Nuclear Cardiology

112. Moody JB, Lee BC, Ficaro EP. Error estimation for dynamic PET myocardial blood flow. Soc Nucl Med Annu Meet Abstr 2012;53:323. 113. Saraste A, Kajander S, Han C, Nesterov SV, Knuuti J. PET: Is myocardial flow quantification a clinical reality? J Nucl Cardiol 2012;19:1044-59. 114. Positron Corporation. Integrating the Cardiac PET Supply Chain. 2014. Accessed December 1, 2014. 115. INVIA Medical Imaging Solutions. Corridor4DM CFR. 2014. Accessed December 1, 2014. 116. Cardiovascular Imaging Technologies. ImagenQ: Absolute PET Quantification of cardiac PET. 2014. products/imagenq/. Accessed December 1, 2014. 117. Cedars-Sinai. Quantitative PET (QPET). 2014. Artificial-Intelligence-in-Medicine-AIM/Projects/QuantitativePET-QPET.aspx. Accessed December 1, 2014. 118. Siemens. syngo.PET MBF (Cardiology Applications). syngo.via. 2014. pet-ct/syngo-via/technical-details. Accessed December 1, 2014. 119. Slomka PJ, Alexanderson E, Ja´come R, Jime´nez M, Romero E, Meave A, et al. Comparison of clinical tools for measurements of regional stress and rest myocardial blood flow assessed with 13Nammonia PET/CT. J Nucl Med 2012;53:171-81. doi: 10.2967/jnumed.111.095398. 120. Tahari AK, Lee A, Rajaram M, Fukushima K, Lodge MA, Lee BC, et al. Absolute myocardial flow quantification with 82Rb PET/CT: Comparison of different software packages and methods. Eur J Nucl Med Mol Imaging 2014;41:126-35. 121. Sunderland JJ, Pan X-B, Declerck J, Menda Y. Dependency of cardiac rubidium-82 imaging quantitative measures on age, gender, vascular territory, and software in a cardiovascular normal population. J Nucl Cardiol 2015;22:72–84. doi:10.1007/s12350-014-9920-6. 122. Nesterov SV, Deshayes E, Sciagra` R, Settimo L, Declerck JM, Pan X-B, et al. Quantification of myocardial blood flow in absolute terms using 82Rb PET imaging: Results of RUBY-10 study. JACC Cardiovasc Imaging 2014. doi:10.1016/j.jcmg. 2014.08.003. 123. Manabe O, Yoshinaga K, Katoh C, Naya M, Kemp RA, Tamaki N. Repeatability of rest and hyperemic myocardial blood flow measurements with 82Rb dynamic PET. J Nucl Med 2009;50:68-71. 124. Efseaff M, Klein R, Ziadi MC, Beanlands RS, deKemp RA. Short-term repeatability of resting myocardial blood flow measurements using rubidium-82 PET imaging. J Nucl Cardiol 2012;19:997-1006. 125. Fukushima K, Javadi MS, Higuchi T, Lautama¨ki R, Merrill J, Nekolla SG, et al. Prediction of short-term cardiovascular events using quantification of global myocardial flow reserve in patients referred for clinical 82Rb PET perfusion imaging. J Nucl Med 2011;52:726-32. doi:10.2967/jnumed.110.081828. 126. Muzik O, Duvernoy C, Beanlands R, Sawada S, Dayanikli F, Wolfe E, et al. Assessment of diagnostic performance of quantitative flow measurements in normal subjects and patients with angiographically documented coronary artery disease by means of nitrogen-13 ammonia and positron emission tomography. J Am Coll Cardiol 1998;31:534-40. 127. Fiechter M, Ghadri JR, Gebhard C, Fuchs TA, Pazhenkottil AP, Nkoulou RN, et al. Diagnostic value of 13N-ammonia myocardial perfusion PET: Added value of myocardial flow reserve. J Nucl Med 2012;53:1230-4. doi:10.2967/jnumed.111.101840. 128. Ziadi MC, deKemp RA, Williams K, Guo A, Renaud JM, Chow BJW, et al. Does quantification of myocardial flow reserve using

Journal of Nuclear Cardiology







rubidium-82 positron emission tomography facilitate detection of multivessel coronary artery disease? J Nucl Cardiol 2012;19:67080. doi:10.1007/s12350-011-9506-5. Naya M, Murthy VL, Taqueti VR, Foster CR, Klein J, Garber M, et al. Preserved coronary flow reserve effectively excludes highrisk coronary artery disease on angiography. J Nucl Med 2014;55:248-55. doi:10.2967/jnumed.113.121442. Hajjiri MM, Leavitt MB, Zheng H, Spooner AE, Fischman AJ, Gewirtz H. Comparison of positron emission tomography measurement of adenosine-stimulated absolute myocardial blood flow versus relative myocardial tracer content for physiological assessment of coronary artery stenosis severity and location. J Am Coll Cardiol Imaging 2009;2:751-8. doi:10.1016/j.jcmg. 2009.04.004. Kajander S, Joutsiniemi E, Saraste M, Pietila¨ M, Ukkonen H, Saraste A, et al. Cardiac positron emission tomography/computed tomography imaging accurately detects anatomically and functionally significant coronary artery disease. Circulation 2010; 122:603-13. doi:10.1161/CIRCULATIONAHA.109.915009. Kaufmann PA, Gnecchi-Ruscone T, Yap JT, Rimoldi O, Camici PG. Assessment of the reproducibility of baseline and hyperemic myocardial blood flow measurements with 15O-labeled water and PET. J Nucl Med 1999;40:1848-56. Wyss CA, Koepfli P, Mikolajczyk K, Burger C, von Schulthess GK, Kaufmann PA. Bicycle exercise stress in PET for assessment of coronary flow reserve: Repeatability and comparison with adenosine stress. J Nucl Med 2003;44:146-54. Siegrist PT, Gaemperli O, Koepfli P, Schepis T, Namdar M, Valenta I, et al. Repeatability of cold pressor test-induced flow increase assessed with H15 2 O and PET. J Nucl Med 2006;47: 1420-6.

Moody et al Clinical PET MBF: technical perspective

135. Schindler TH, Zhang X-L, Prior JO, Cadenas J, Dahlbom M, Sayre J, et al. Assessment of intra- and interobserver reproducibility of rest and cold pressor test-stimulated myocardial blood flow with 13N-ammonia and PET. Eur J Nucl Med Mol Imaging 2007;34:1178-88. 136. Eriksson L, Wienhard K, Eriksson M, Casey ME, Knoess C, Bruckbauer T, et al. NEMA evaluation of the first and second generation of the Ecat Exact and Ecat Exact HR family of scanners. In: 2001 IEEE Nuclear Science Symposium Conference Record. vol 3, 2001:1223-26. doi:10.1109/NSSMIC.2001. 1008556. 137. Herzog H, Tellmann L, Hocke C, Pietrzyk U, Casey ME, Kuwert T. NEMA NU2-2001 guided performance evaluation of four Siemens ECAT PET scanners. IEEE Trans Nucl Sci. 2004;51: 2662-9. doi:10.1109/TNS.2004.835778. 138. Surti S, Karp JS. Imaging characteristics of a 3-dimensional GSO whole-body PET camera. J Nucl Med 2004;45:1040-9. 139. Bettinardi V, Danna M, Savi A, Lecchi M, Castiglioni I, Gilardi M, et al. Performance evaluation of the new whole-body PET/CT scanner: Discovery ST. Eur J Nucl Med Mol Imaging 2004;31:867-81. doi:10.1007/s00259-003-1444-2. 140. Kemp BJ, Kim C, Williams JJ, Ganin A, Lowe VJ. NEMA NU 22001 performance measurements of an LYSO-based PET/CT system in 2D and 3D acquisition modes. J Nucl Med 2006;47:1960-7. 141. Tera¨s M, Tolvanen T, Johansson J, Williams J, Knuuti J. Performance of the new generation of whole-body PET/CT scanners: Discovery STE and discovery VCT. Eur J Nucl Med Mol Imaging 2007;34:1683-92. doi:10.1007/s00259-007-0493-3. 142. Schelbert HR, Phelps ME, Huang SC, MacDonald NS, Hansen H, Selin C, et al. N-13 ammonia as an indicator of myocardial blood flow. Circulation 1981;63:1259-72.

Precision and accuracy of clinical quantification of myocardial blood flow by dynamic PET: A technical perspective.

A number of exciting advances in PET/CT technology and improvements in methodology have recently converged to enhance the feasibility of routine clini...
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