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

Transcription

1 Precision and accuracy of clinical quantification of myocardial blood flow by dynamic PET: a technical perspective Jonathan B. Moody, PhD (1), Benjamin C. Lee, PhD (1), James R. Corbett, MD (2,3), Edward P. Ficaro, PhD (1, 2), Venkatesh L. Murthy, MD, PhD (2, 3) Affiliations: (1) INVIA Medical Imaging Solutions, Ann Arbor, MI (2) Division of Nuclear Medicine, Department of Radiology, University of Michigan, Ann Arbor, MI (3) Division of Cardiovascular Medicine, Department of internal Medicine, University of Michigan, Ann Arbor, MI Correspondence: Venkatesh L. Murthy, MD, PhD 1338 Cardiovascular Center 1500 E. Medical Center Dr, SPC 5873 Ann Arbor, MI Tel: Fax: Word Count: 9212 Number of Tables: 2 Number of Figures: 6

2 Abstract 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 82 Rb 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; myocardial flow reserve; cardiac PET/CT; 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) OSEM Ordered Subsets Expectation Maximization PSF Point Spread Function LV Left Ventricle RV Right Ventricle 2

3 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) and myocardial flow reserve (MFR). Prognostic data from several recent studies[1 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 3

4 such scanners was typically limited to academic research centers, often with the capability to produce blood flow tracers such as 13 N-ammonia which entail somewhat lower count rate demands for dynamic PET than 15 O-water and 82 Rb. 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 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 15 O and 82 Rb 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 82 Rb, 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 designs[14 16], as well as practical implementations of time-of-flight (TOF)[17, 18] and list-mode acquisition[19] 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 are 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. 4

5 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 short-term 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 5

6 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 sec 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 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 82 Rb 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 infusion[27] which is primarily an issue with generator-produced 82 Rb. The most commonly used 82 Rb generator (CardioGen-82, Bracco Diagnostics Inc.), delivers 82 Rb at a constant flow rate of 50 ml/min. As the 82 Sr 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 82 Rb 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 82 Rb is short-lived (76 sec half-life), it requires careful optimization of dose to balance potential saturation and dead-time losses in the early dynamic time frames (bloodpool phase) with adequate myocardial counts at later time frames (myocardial phase)[23] (Figure 2). Tout et al.[29] compared two fixed 82 Rb doses in consecutive patients using a current-generation high- 6

7 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 bloodpool phase in 15% of cases (33/217), which was reduced to 1% (2/159) in the lower dose group[29]. Patient BMI and 82 Rb generate 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]. 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 al.[30] showed that for the most widely used scatter estimation method (single scatter simulation[31]) 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 sec, and did not drop to 50% until after 82 Rb had cleared the blood pool around 150 sec. During the same period, 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 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 7

8 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 82 Rb, 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 82 Rb 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 likelihood patients exhibited focally reduced count density and significantly greater variability, particularly in the septal wall, which was due to overcorrection of scatter[36] (Figure 4). A recent multicenter trial compared phantom scans and visual interpretation of 3D 82 Rb 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 al.[36] was reported in a conference abstract by the vendor (Siemens)[38][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 82 Rb prompt gamma correction on the precision and accuracy of MBF. 8

9 Image reconstruction FBP vs. OSEM reconstruction. Filtered back projection (FBP) is a linear reconstruction method that produces images with well-understood noise properties[39 41]. When appropriate PET corrections are applied, FBP images can be quantitatively accurate for regions of 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 current-generation 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 extra-cardiac 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 used[43 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 data[42, 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 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 al.[50] have assessed this effect in phantom studies that mimicked the conditions of dynamic cardiac 13 N-ammonia PET: peak prompt coincidence rates of Mcps during the early bloodpool 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 82 Rb (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 blood pool phase which depended 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 9

10 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 pool[50] 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) 58. While PSF modeling reduces spatial noise variance, ensemble noise variance can be reduced, remain the same, or actually increase depending on inter-voxel correlations and region-of-interest 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 18 F and 11 C based radiotracers may not account for the substantial positron range of 82 Rb; and the 10

11 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-of-flight (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 psec) 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 82 Rb 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 psec to 900 psec) when the singles rate was increased to 50 Mcps[18]. This singles rate is comparable to that of the bloodpool phase (Figure 3) and the increase in timing resolution will degrade the benefit of TOF acquisition during these 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 psec for the Philips Ingenuity TF 128 PET/CT[16] and 11

12 ~540 psec 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 psec, remaining constant up to very high singles rates typical of 82 Rb dynamic PET[64]. The influence of TOF and PSF modeling on MBF quantification has been investigated in three recent studies. Presotto et al.[65] reported a study of 22 cardiac patients assessed with 13 N- 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, 2mm 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 al.[66] evaluated MBF quantification in 37 patients using 82 Rb 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 studies[65, 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 al.[67] 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 13 N-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 reference 12

13 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 3D-OSEM, which may impact its routine clinical utility[66]. Another important issue in 82 Rb studies which deserves further investigation concerns the empirical Renkin-Crone model[68], which is required to map the tracer uptake parameter K 1 to MBF as discussed below (Kinetic modeling). The MBF software package employed by Armstrong et al.[66] (Siemens syngo.pet MBF[69]) 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, 66]. 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 K 1 similarly increased, while changes in MFR were minimal[70]. Because of the sensitivity of factor analysis to post-filtering, care should be exercised to maintain consistent filtering when comparing MBF between different studies. 13

14 Patient motion Presotto et al.[50] 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 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 82 Rb 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 reported[77, 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]. The processes of image reorientation and polar map sampling both involve interpolation which introduces smoothing to the data, and should be considered in addition to post-reconstruction image filtering. An image-based arterial input function[80] is typically estimated by placing regions-of-interest within the LV bloodpool on each dynamic image. Although this simple pre-processing procedure has been in use for more than 30 years, it still 14

15 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 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 82 Rb MBF quantification[81, 84]. The method assumes the left ventricular arterial input function is accurately known, and although it can be very effective at reducing partial volume bias, the common practice of centering the myocardial region-of-interest on the midwall[85] may lead to biased kinetic parameter estimates[72, 84]. More recently, improved factor analysis methods have been applied to 82 Rb dynamic cardiac PET[86 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-activitycurves for the RV and LV by localizing conventional regions-of-interest in the dynamic 15

16 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 82 Rb MBF quantification is the one-tissue (1T) compartmental model[68, 92 94], which depends on two parameters: a tracer uptake parameter (K 1 ), and a tracer washout parameter (k 2 ). This model is a simplification of two-tissue (2T) compartment models previously developed for 82 Rb[95 98], and represents a compromise between higher precision but increased bias of K 1 estimates[92]. For the 1T compartmental model, K 1 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 K 1. K 1 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 statistical uncertainty in the underlying images increases as the tracer decays during the dynamic PET sequence[100]. The variance of K 1 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-studied[40, 41, ], detailed analysis of image variance is not clinically available and approximations are typically used[100, 105] which can limit the accuracy of K 1 variance estimates. A simple approximation advocated by Lammertsma[100] 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 model[99] 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 16

17 compartmental models in animal studies[99, 106] and has also been used in large clinical studies[107, 108]. Renkin-Crone extraction model. In practice, the relationship between K 1 and MBF for a given tracer can be empirically determined by fitting the generalized Renkin-Crone equation to independent measurements of K 1 and MBF in a set of volunteers[93, 99]. This empirical relationship can then be used to map K 1 to MBF in subsequent PET exams[24, 68, 93, 94]. One approach to utilizing the empirical Renkin-Crone relation is to substitute K 1 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 this approach is that the 1T kinetic model, which is linear in K 1 [110, 111], then becomes nonlinear in MBF, which can lead to difficulties and failed fits for noisy data, such as occurs when fitting parametric polar maps of MBF. Alternatively, a K 1 estimate may be directly mapped to MBF with a simple lookup table computed using the Renkin-Crone 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 analytical formula has recently been proposed[112] 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 can be quantitatively assessed (Figure 5). For 82 Rb at rest (MBF=1.0 ml/min/g), the Renkin-Crone relation contributes between 50-70% of the variability in global MBF, while at stress (MBF=3.0) it contributes 40-50%, depending on the variance of K 1 (Figure 5) Currently, the most widely used Renkin-Crone relation for 82 Rb MBF quantification is the Lortie model[68]. As noted above, this model is based on 3D PET data (CTI/Siemens ECAT ART) without prompt gamma correction reconstructed by FBP after 2D Fourier rebinning[68]. The appropriateness of using this model with 3D PET data with prompt gamma correction 17

18 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 (Positron[114]), Corridor4DM (INVIA[115]), ImagenQ (CVIT[116]), QPET (Cedars-Sinai[117]), and syngo.pet MBF (Siemens[118]). In addition, numerous research packages are also available[113]. These software packages have been compared to one another in recent studies[65, 85, ]. DeKemp et al.[85] studied three commercial software packages with automatic processing and the same conventional pre-processing 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 The MBF RPC was similar to the statistical error in 82 Rb 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 kinetic model[85]. Tahari et al.[120] 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 al.[90] 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 18

19 criterion was chosen to be similar to test-retest repeatability results for 82 Rb rest MBF reported by Efseaff et al.[124] who compared different processing approaches and obtained a best-case test-retest repeatability coefficient (RPC) of 0.20 (24% of mean rest MBF) which was comparable to that obtained by Manabe et al.[123] (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 al.[124] and Manabe et al.[123] 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 data 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) would be increased to 38% ( 2 27%). Note that the majority of recent studies that have evaluated the clinical utility of MBF or MFR[1 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 82 Rb MBF quantification forward in the clinic. For most of these factors further data is needed to define their relative importance for MBF quantification. It is likely that weight-based 82 Rb 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. 19

20 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 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 al.[124]) 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 MFR[114] using the retention model discussed above (Model-based methods). 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 use of newer PET technology. For example, in 1998 Muzik et al.[126] studied the diagnostic utility of 13 N-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 al.[127] found that 13 N- 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 disease[128] 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 20

21 methodology found in current clinical practice. However, the clinical studies that have demonstrated the prognostic utility of MFR have primarily been single-center 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]. Stress MBF has shown promise in recent studies as a tool for evaluating coronary artery disease[130, 131], but more data are needed as well 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 current-generation 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 continues 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 21

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34 34

35 Tables 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 82 Rb 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 validation of existing methods, and to assess 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 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 Renkin-Crone extraction relation with respect to PET corrections and image reconstruction; impact on MBF precision and accuracy needs to be fully characterized 35

36 Table 2: Test-retest repeatability coefficients for myocardial blood flow from the literature Rest Stress Reference N Tracer RRR 95% C.I. % RRR 95% C.I. % Kaufmann et O-water 0.17 [0.11, 0.23] [0.63, 1.25] 25 al.[132] Wyss et al.[133] O-water 0.26 [0.13, 0.39] [0.67, 2.01] 27 Siegrist et al.[134] O-water * [0.27, 0.55] 28 Schindler et al.[135] N-ammonia 0.26 [0.17, 0.35] * [0.19, 0.37] 32 Manabe et al.[123] Rb 0.19 [0.11, 0.27] [0.55, 1.29] 27 Efseaff et al.[124] Rb 0.28 [0.21, 0.35] Efseaff et al.[124] Rb 0.20 [0.15, 0.25] Repeatability coefficients (RRR) for short term test-retest MBF that have been reported in the literature for different flow tracers with 95% confidence intervals (ml/min/g). ( ) Efseaff et al. compared several processing methods; the RRR 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. (N=number of patients; %=RRR as a percentage of mean MBF). 36

37 Figures 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 or 2007 standard[14 16, 18, 53, ]; some scanners are capable of both 2D and 3D acquisition, and some are 3D only). 37

38 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 al.[29] with kind permission from Lippincott Williams & Wilkins.) (A) (B) 38

39 Figure 3: Prompt coincidence rates (A), random fractions (B), and scatter fractions (C) versus dynamic Frame Time from rest and regadenoson stress dynamic cardiac 82 Rb PET scans in 25 normal volunteers acquired on a Siemens Biograph mct PET/CT scanner. The average peak prompts rate at Frame Time 40 sec corresponds to an average peak singles rate of 50±8 Mcps. (mean±sd, dotted lines indicate minimum and maximum) (A) 39

40 (B) (C) 40

41 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.) 41

42 Figure 5: The fraction of global MBF coefficient of variation (CV) due to the Renkin-Crone relation 1 CV(K 1) CV(MBF) as a function of CV(K 1) for 82 Rb ([68]) (A), and 13 N-ammonia ([142]) (B). The shaded region (top) indicates a typical range of CV(K 1 ) observed for clinical 82 Rb data (4-13%). MBF=myocardial blood flow (ml/min/g). (A) 42