- Original research
- Open Access
Reproducible quantification of cardiac sympathetic innervation using graphical modeling of carbon-11-meta-hydroxyephedrine kinetics with dynamic PET-CT imaging
- Tong Wang†^{1, 2},
- Kai Yi Wu†^{1},
- Robert C. Miner^{1},
- Jennifer M. Renaud^{1},
- Rob S. B. Beanlands^{1} and
- Robert A. deKemp^{1}Email authorView ORCID ID profile
- Received: 21 March 2018
- Accepted: 2 July 2018
- Published: 20 July 2018
Abstract
Background
Graphical methods of radiotracer kinetic modeling in PET are ideal for parametric imaging and data quality assurance but can suffer from noise bias. This study compared the Logan and Multilinear Analysis-1 (MA1) graphical models to the standard one-tissue-compartment (1TC) model, including correction for partial-volume effects, in dynamic PET-CT studies of myocardial sympathetic innervation in the left ventricle (LV) using [^{11}C]HED.
Methods
Test and retest [^{11}C]HED PET imaging (47 ± 22 days apart) was performed in 18 subjects with heart failure symptoms. Myocardial tissue volume of distribution (V_{T}) was estimated using Logan and MA1 graphical methods and compared to the 1TC standard model values using intraclass correlation (ICC) and Bland-Altman analysis of the non-parametric reproducibility coefficient (NPC).
Results
A modeling start-time of t* = 5 min gave the best fit for both Logan and MA1 (R^{2} = 0.95) methods. Logan slightly underestimated V_{T} relative to 1TC (p = 0.002), whereas MA1 did not (p = 0.96). Both the MA1 and Logan models exhibited good-to-excellent agreement with the 1TC (MA1-1TC ICC = 0.96; Logan-1TC ICC = 0.93) with no significant differences in NPC between the two comparisons (p = 0.92). All methods exhibited good-to-excellent test-retest repeatability with no significant differences in NPC (p = 0.57).
Conclusions
Logan and MA1 models exhibited similar agreement and variability compared to the 1TC for modeling of [^{11}C]HED kinetics. Using t* = 5 min and partial-volume correction produced accurate estimates of V_{T} as an index of myocardial sympathetic innervation.
Keywords
- Logan
- MA1
- One tissue compartment
- Sympathetic nervous system
- HED
Background
Developed as a positron emission tomography (PET) imaging agent to target the cardiac sympathetic nervous system, carbon-11-labeled meta-hydroxyephedrine ([^{11}C]HED) is a norepinephrine analog that is taken up by nerve terminal varicosities in the myocardium, and used to assess sympathetic nerve function [1]. Since its genesis, it has been the cornerstone PET tracer for cardiac sympathetic innervation, employed in determination of neuronal-based defects leading to improved diagnosis and prognosis for pathologies such as heart failure, arrhythmia, and cardiomyopathy, in which cardiac neuronal function is often compromised, leading to decreased catecholamine sensitivity and lowered beta adrenergic receptor density [1]. Using PET [^{11}C]HED imaging of cardiac tissues, the volume of distribution (V_{T}) of the injected radiotracer is an invaluable metric that quantifies the uptake and retention of tracer, providing an index of sympathetic nerve density and reuptake-1 transporter activity. For cardiac PET applications especially, V_{T} and other kinetic modeling parameters measured in the myocardium may be used to aid in the diagnosis of various innervation and perfusion-based pathologies.
In PET imaging studies, V_{T} is defined as the equilibrium ratio of tracer concentration in tissue to that of unmetabolized parent tracer in plasma, but this direct measurement is typically not feasible due to the long time needed to reach equilibrium. Alternatively, kinetic modeling is commonly used to determine V_{T} from a significantly shorter temporal sample following tracer injection [2]. While the physiological kinetics of [^{11}C]HED may be modeled using a two-tissue-compartment model, the one-tissue-compartment (1TC) model has been shown to provide a robust representation with optimal clinical reproducibility in myocardial uptake studies, without sacrificing the accuracy of V_{T} quantification [3]. Two graphical methods reported in the literature for kinetic modeling of reversible-binding tracers are the Logan [4] and Multilinear Analysis-1 (MA1) models [5], which are both computationally simpler than non-graphical (compartmental) methods [6], while being able to provide visual representations of kinetic parameters. The Logan method has been established as the standard graphical model to estimate V_{T} in a wide range of PET applications in the brain and heart, while MA1 was proposed as an alternative numerical formulation to estimate V_{T} with lower noise bias compared to Logan estimates [5]. Although [^{11}C]HED is a widely used tracer, a comprehensive evaluation of the performance of the graphical and non-graphical methods to quantify its kinetics has not been performed. Furthermore, the effects of partial-volume losses on quantification of V_{T} have not been well defined in the context of graphical kinetic modeling in the heart, where the effects of blood-pool spillover and motion are more apparent compared to the brain. The goal of this study was to determine a method of partial-volume correction applicable to graphical kinetic modeling and to compare the Logan and MA1 models to the standard 1TC kinetic model for accurate quantification of myocardial sympathetic innervation using dynamic [^{11}C]HED PET-CT studies.
Methods
Patient study design
Twenty-three heart failure patients were recruited as control subjects for a previous study (PET-OSA: NCT00756366) investigating the effects of continuous positive airway pressure (CPAP) on sympathetic nerve function and cardiac energetics in heart failure patients with obstructive sleep apnea (OSA) [7]. These control patients had the same inclusion and exclusion criteria as the PET-OSA study, except they did not have OSA. Patient demographics were collected at baseline and follow-up visits.
Three of the 23 patients were missing baseline or follow-up PET scans and were excluded. Two other patients were also excluded: one with atrial fibrillation at baseline that was treated before the follow-up scan and the other with uncorrectable severe motion artifact, leaving N = 18 subjects included in the final analysis. All patients provided written informed consent, according to the research protocol approved by the Human Research Ethics Board at the University of Ottawa Heart Institute.
[^{11}C]HED PET imaging
[^{11}C]HED was synthesized from [^{11}C]methyl-iodide and metaraminol-free base, with the use of standard methods for high purity and specific activity [8]. Images were obtained at baseline and follow-up (47 ± 22 days apart) on the ECAT-ART PET (Siemens/CTI, Knoxville, TN) or Discovery RX PET-VCT (GE Healthcare, Waukesha, WI) scanner, with ECG, heart rate, and blood pressure monitored at regular intervals. A transmission scan for attenuation correction was performed using Cs-137 isotope or X-ray CT [9], immediately after which 10–15 mCi (370–550 MBq) of [^{11}C]HED was injected over 30 s and a dynamic PET series was acquired over a 40-min period (10 × 10 s; 1 × 60 s; 5 × 100 s; 3 × 180 s; 4 × 300 s) [10]. Image reconstruction was performed using filtered-back-projection with a 12-mm Hann filter and all corrections enabled for quantification of radioactivity concentration [11].
Tracer kinetic modeling
Blood metabolites correction
Compartment modeling and partial volume correction
From (11), we propose that V_{T} may be estimated from V_{ROI}, with plasma-to-whole-blood and metabolite corrections as well as partial-volume effects considered explicitly.
Graphical kinetic modeling
C_{ROI}(t) and C_{p}(t) time-activity curves are used as measured input data. At a certain time (t*), the intercept term (Int) will become a constant value [17], at which point the equation becomes a linear system where the slope represents the volume of distribution in the ROI. Since the measured tissue curve C_{ROI}(t) is subject to blood spillover and partial-volume losses, only V_{ROI} may be obtained from the graphical model directly. Previous applications of this model have estimated V_{ROI} without explicit correction factors for partial-volume effects, which is required for cardiac applications. In our proposed model, Eq. (11) may be used to determine V_{T} from the slope determined by the Logan model.
V_{T}may again be determined from the V_{ROI} value estimated using MA1, according to the relation defined in (11).
Determination of t* for graphical models
The estimation start-time (t*) was varied systematically from 1.5 to 20 min for a subset of five [^{11}C]HED studies to determine the optimal value to be used for the main analysis. Goodness-of-fit was evaluated on the Logan plot as the Pearson correlation (r^{2}) of the points from t* to 40 min, indicating the subset of points best described by a line. Since the r^{2} is not effective to assess goodness-of-fit of the near-horizontal fitted plane on the MA1 plots, an alternative metric was computed using the relative standard error of the estimate (rSEE) as 1 − SEE/mean. The optimal t* was determined by comparing V_{T} values from the graphical methods to the 1TC model standard. Then, all subsequent analysis was performed using the same start-time for both Logan and MA1 models.
PET image analysis
The compartmental and graphical analysis models were implemented in the FlowQuant® analysis software (University of Ottawa Heart Institute, ON). The operator reliability of this automated software has been reported previously [18]. Briefly, the left ventricle (LV) myocardium was segmented automatically and partitioned into voxels using a 2D polar-map representation, with each voxel representing a transmural sub-region of the LV myocardial tissue. The arterial whole-blood (WB) ROI was positioned automatically at the center of the left atrioventricular valve plane. Time-activity curves were generated based on measured tracer activity in the LV cavity C_{WB}(t) and myocardial tissue C_{T}(t) ROIs, as input to the tracer kinetic models.
In each polar-map voxel, the 1TC model rate constants K_{1} and k_{2}, as well as V_{T} and the blood spillover fraction F_{WB}, were estimated using weighted least-squares regression, according to Eqs. (3), (4), and (5). The Logan and MA1 graphical models in Eqs. (12) and (13) were used to calculate LV polar-maps of V_{ROI}. Scan-specific spillover values were calculated as the polar-map median F_{WB} and the corresponding partial-volume recovery coefficient RC (1 − F_{WB}), which were then used to estimate V_{T} from the graphical model estimates of V_{ROI} according to Eq. (11). Image and data analyses were performed using MATLAB 2013b (The Mathworks, Natick, MA).
Statistical analysis
LV median V_{T} values obtained from the 1TC, Logan, and MA1 methods were tabulated. Inter-model and test-retest mean effects were evaluated using two-way repeated measures ANOVA. Bland-Altman analyses and Intra-class correlation (ICC) were employed to evaluate the inter-model (MA1 vs 1TC, and Logan vs 1TC) and test-retest (baseline vs follow-up) reliability [19, 20]. Absolute-agreement ICC with two-way mixed effects was used for the inter-model reproducibility and test-retest repeatability [21]. To correct for skew in the V_{T} distributions, V_{T} values were logarithmically transformed before the ANOVA and ICC analyses. ICC values were categorized as: ICC > 0.90 excellent, > 0.75 very good, > 0.40 good, and ≤ 0.40 poor [22]. The limits-of-agreement of repeated measures were estimated using the following: (i) median difference ± non-parametric repeatability coefficient (NPC = 1.45 × IQR) to account for the variable effect of outliers and (ii) mean difference ± coefficient-of-repeatability (CR = 1.96 × SD). √(3/N) × SD_{difference}(t_{95%, n − 1}) was used to calculate the 95% confidence intervals on the limits-of-agreement, where N is the number of pairs being analyzed [20]. Differences in V_{T} values were divided by the mean V_{T} to account for the increased variability of differences associated with increased mean V_{T}. The NPC was also reported as it is a more robust measure of repeatability [23]. Non-parametric Levene’s test was used to assess the equality of variance between groups. Bias in the Bland-Altman plots was assessed using the one-sample Wilcoxon Signed Ranked test against zero. A 2-tailed p value < 0.05 was considered statistically significant for all tests. Statistical analyses were performed using Excel 2016 (Microsoft) and SPSS 20.0 (IBM).
Results
Patient demographics
Patient characteristics (N = 18)
Description | Value |
---|---|
Age (years) | 66.5 ± 9.3 |
Body mass index (BMI) | 27.5 ± 4.9 |
Left ventricular ejection fraction (%) | 31.8 ± 6.2 |
Males | 12 (67%) |
Smoking status | |
Current | 2 (11%) |
Former | 8 (44%) |
Never | 8 (44%) |
Diabetes mellitus | 6 (33%) |
Hypertension | 12 (67%) |
Dyslipidemia | 11 (61%) |
Family history of heart disease | 8 (44%) |
Medications | |
ACE inhibitor | 16 (89%) |
Beta blocker | 8 (44%) |
Digoxin | 6 (33%) |
Diuretics | 12 (67%) |
Statin | 13 (72%) |
Acetylsalicylic acid | 14 (78%) |
Plavix | 6 (33%) |
Coumadin | 3 (17%) |
New York Heart Association (NYHA) | |
Class II | 15 (83%) |
Class III | 3 (17%) |
Ischemic cardiomyopathy | |
Previous PCI or CABG | 13 (72%) |
Previous MI | 11 (61%) |
Adjustment of start-time (t*) for graphical models
Effect of graphical modeling start-times (t*) on measured V_{T} and goodness-of-fit values (N = 5)
t* (min) | Logan V_{T} | Logan r^{2} | MA1 V_{T} | MA1 1 − rSEE |
---|---|---|---|---|
1.5 | 11.2 | 0.90 | 13.0 | 0.85 |
2 | 12.1 | 0.91 | 13.6 | 0.87 |
2.5 | 13.2 | 0.92 | 14.6 | 0.89 |
3 | 14.6 | 0.94 | 15.4 | 0.92 |
4 | 16.4 | 0.94 | 17.0 | 0.94 |
5 | 18.2 | 0.96 | 19.8 | 0.97 |
10 | 18.8 | 0.95 | 21.4 | 0.97 |
15 | 19.2 | 0.92 | 22.2 | 0.97 |
20 | 17.6 | 0.92 | 21.4 | 0.98 |
PET image analysis
Comparison of Logan and MA1 versus 1TC
[^{11}C]HED PET V_{T} Measurements (N = 18)
Parameter | 1TC | Logan* | MA1 | |||
---|---|---|---|---|---|---|
Baseline | Follow-up | Baseline | Follow-up | Baseline | Follow-up | |
V_{T} | 19.7 ± 7.8 | 21.3 ± 11.5 | 17.3 ± 8.0 | 18.8 ± 12.0 | 20.1 ± 9.5 | 22.6 ± 16.2 |
Inter-model reproducibility of V_{T} measurements (N = 36)
Models compared | ICC [95%CI] | Average Delta ± RPC | Median Delta ± NPC |
---|---|---|---|
Logan vs 1TC | 0.928 [0.432, 0.978] | − 16.3 ± 27.8% † | − 14.5 ± 26.6%^{†} |
MA1 vs 1TC | 0.955 [0.915, 0.977] | − 2.3 ± 34.2% | − 1.0 ± 26.5% |
Test-retest repeatability of kinetic models
Test-retest repeatability of V_{T} measurements (N = 18)
Model | ICC [95%CI] | Average Delta ± RPC | Median Delta ± NPC |
---|---|---|---|
1TC | 0.852 [0.645, 0.942] | 1.3 ± 56.2% | − 9.6 ± 40.9% |
Logan | 0.852 [0.646, 0.942] | 0.8 ± 73.0% | − 10.4 ± 68.1% |
MA1 | 0.837 [0.614, 0.936] | 0.9 ± 63.9% | − 5.5 ± 33.7% |
Discussion
In an effort to improve and expand the use of kinetic modeling in cardiac PET studies of sympathetic innervation, we sought to evaluate multiple kinetic models for the analysis of [^{11}C]HED studies. This was achieved by comparing the inter-method differences in V_{T} quantified by the Logan and MA1 graphical models compared to the reference 1TC model in a sample of heart failure patients and assessing the test-retest repeatability between baseline and follow-up scans. HED PET is often used to evaluate therapy or disease progression in heart failure patients; therefore, evaluation of the test-retest repeatability is most relevant in this same population, as opposed to healthy normal subjects who generally have lower sympathetic tone. The patients’ heart failure symptoms and medications were stable over the test-retest interval; therefore, any impact on the repeatability data should be minimal.
The MA1 model exhibited excellent agreement with 1TC, the Logan model exhibited good-to-excellent agreement with 1TC, and all models had good-to-excellent test-retest repeatability. Logan V_{T} values were significantly lower than MA1 and 1TC V_{T} values, while MA1 V_{T} values were not significantly different from those obtained using the 1TC model (Table 3). While 1TC is the reference standard kinetic model in this instance, graphical models such as the Logan and MA1 are computationally simpler alternatives that allow for linearized visualization and analysis of tracer kinetic data. Our findings support the reliable use of both graphical analysis methods in addition to the standard 1TC model for tracer kinetic analysis of V_{T}. These findings agree with previous studies using other PET tracers that compared various graphical models, including the Logan method, finding the results to be in agreement with standard compartment models, but computationally simpler, and potentially more robust [24–27].
In the present cardiac PET study, partial volume and spillover corrections were critical to implement into the graphical modeling calculations to avoid misinterpretation. The commonly used Logan and MA1 methods (Eqs. 12 and 13) only estimate the volume of distribution in the PET image region (V_{ROI}) as opposed to the myocardial tissue of interest (V_{T}). Compared to PET measurements in other organ systems such as the brain, in cardiac studies, the measured ROI region contains much more spillover of blood signal within and adjacent to the myocardial tissues. Our implementation of a partial-volume correction method based on estimated recovery coefficients and whole-blood spillover fractions allowed accurate measurement of myocardial V_{T} values using Logan and MA1 graphical models on a scan-specific basis. In this validation study, F_{WB} was estimated first using the 1TC with spillover model, and then used to calculate the corresponding RC values for consistent partial-volume and spillover correction of the graphical model V_{ROI} estimates. It is clear that independent estimates of RC and F_{WB} are required to determine V_{T} from V_{ROI} as shown in Eq. 11; therefore, any error in the estimation of these correction factors in practice will be propagated directly into the corresponding values of V_{T}. In the present study, the average F_{WB} value was 0.37 ± 0.07, which could be used to estimate RC and hence V_{T} in similar patient population studies with minimal added variability.
We investigated the effect of varying t* on the graphical model results (Table 2), which quantified V_{T} using the plotted values at t ≥ t*. It has been reported that t* may be deduced directly from kinetic modeling data for some tracers [5], but the method we presented used a simpler and systematic approach to determine the t* which produced the same V_{T} values on average compared to the MA1 plots. This approach is beneficial for tracers for which it is more difficult to estimate t* directly from the study data, such as those with relatively slower kinetics [28]. It also removes the need to estimate t* for each individual scan, which may be subject to variable noise effects. We propose t* = 5 min as an effective start-time for cardiac studies employing [^{11}C]HED as it also gave the highest quality of linear fit (r^{2} > 0.95) using the Logan model, in addition to MA1 estimates of V_{T} that were equal to the 1TC reference value on average. This start time was shown with our comparison of the three models to be robust, producing results for V_{T} with excellent goodness-of-fit to the graphical models and inter-method agreement. It is worth noting that a slightly later start time of 10–15 min may have provided Logan V_{T} values that correspond better with 1TC and MA1 (Fig. 1), but at the cost of a lower quality fit of the linear model and wider variability due to fewer fitted points.
Interestingly, the V_{T} values determined by Logan were significantly lower than those determined by both MA1 and 1TC, while V_{T} values determined by MA1 did not show a significant difference to those obtained from 1TC. More precisely, Logan exhibited a greater negative bias where V_{T} was underestimated relative to 1TC, whereas a bias was not present between MA1 and 1TC (Table 4). In a similar kinetic model comparison using [^{18}F]FCWAY and [^{11}C]MDL neurological tracers, Ichise et al. [6] demonstrated that the MA1 model generated higher V_{T} estimates than Logan, and that MA1 exhibited less bias compared to Logan at multiple imaging noise levels. Our results are consistent with these findings, affirming the original report of MA1 as a method to reduce the magnitude of bias induced by noise when using the Logan model [6]. Although Logan seemed to underestimate V_{T} in our study population, it should be realized that the median bias of − 14.5% relative to the 1TC gold standard did not greatly affect the inter-model reproducibility of the models, which exhibited good to excellent agreement despite the bias that was present.
The use of [^{11}C]HED to examine sympathetic function in cardiac PET is becoming increasingly widespread. Recently, it has been shown to be a powerful diagnostic and prognostic tool for patients with heart failure, arrhythmias, flow-innervation mismatches, and microvascular dysfunction in both infarcted and non-infarcted tissues [1, 29–33]. This field continues to be improved and shows promise for a wider variety of applications [34]. As cardiac innervation tracers increase in prevalence, the optimization and validation of kinetic modeling techniques becomes more important; extensions of the current study may be anticipated, such as those investigating the use of a two-tissue-compartment model to quantify cardiac NET re-uptake function more specifically. Moreover, comparisons of multiple kinetic modeling options, in particular those of a graphical nature as presented here, are possible with other cardiac innervation-based tracers such as the [^{18}F]-labeled sympathetic innervation tracers MFBG, MHPG, LMI1195, etc., for more detailed evaluation of their kinetics [35, 36].
A few limitations were present in this study. The current study is a retrospective, single-center study that examined stable heart failure patients only from the PET-OSA trial. The results may be limited by the relatively small sample size (N = 18). Larger prospective studies would be beneficial to further validate the performance of the kinetic models as proposed.
Conclusion
A start time of 5 min was found to provide the best fit for Logan and MA1 models. The MA1-1TC comparison demonstrated excellent agreement while Logan-1TC and test-retest comparisons demonstrated good-to-excellent agreement when quantifying V_{T} with partial volume correction. Although Logan underestimated V_{T} due to the recognized noise bias, Logan and MA1 both exhibited similar test-retest variability, suggesting that they may be used in addition to 1TC in the modeling of [^{11}C]HED kinetics, with benefits of greater computational simplicity and the ability to mathematically visualize kinetic parameters for better quality assurance.
Notes
Declarations
Funding
Networks of Centres of Excellence of Canada (NCE-15-P06-001), Ontario Research Foundation (ORF-RE07-021).
Availability of data and materials
The data will not be shared because it will be used in other upcoming studies.
Authors’ contributions
TW performed kinetic analysis, created figures, and wrote the manuscript. KYW performed statistical analysis, created figures and tables, and wrote the manuscript with TW. TW and RdK formulated partial volume correction. RCM processed clinical studies and assisted in creation of figures. JMR assisted in implementation of kinetic analysis tools. RSB and RdK supervised project development and analysis. All authors were involved in the editing process. All authors read and approved the final manuscript.
Ethics approval and consent to participate
All research subjects provided written informed consent, as approved by the Human Research Ethics Board at the University of Ottawa Heart Institute.
Consent for publication
Consent has been obtained from participants to publish this work.
Competing interests
RSB and RdK have received unrestricted university-industry grant funding from the Ontario Research Fund and Lantheus Medical Imaging.
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