Myocardial Blood Flow Estimates Research Articles

Abstract 15577: Diagnostic Performance of Cadmium-Zinc-Telluride Single-Photon Emission Computed Tomography versus Coronary Angiography Fractional Flow Reserve in Estimating Myocardial Blood Flow and Flow Reserve: A Diagnostic Accuracy Meta-Analysis

Introduction: Precisely estimating myocardial blood flow and flow reserve is crucial in diagnosing and managing ischemic heart disease. Coronary angiography fractional flow reserve (FFR) has been a standard tool. However, the recent emergence of Cadmium-Zinc-Telluride Single-Photon Emission Computed Tomography (CZT-SPECT) has provided an innovative diagnostic approach. Consequently, a comprehensive comparative analysis of their efficacies in evaluating myocardial blood flow and flow reserve is necessary. Hypothesis: This study aims to determine the diagnostic accuracy of CZT-SPECT compared to coronary angiography FFR in estimating myocardial blood flow and flow reserve. Methods: A systematic search until July 2022 was conducted across databases such as PubMed, Embase, and Web of Science, focusing on studies comparing CZT-SPECT and coronary angiography FFR in the estimation of myocardial blood flow and flow reserve. Utilizing the mada package in R software (version 4.0.3), we conducted diagnostic accuracy analysis by pooling metrics like sensitivity, specificity, false-positive rate estimates, diagnostic odds ratio, and positive and negative Likelihood Ratios (LR), all of which were presented with a 95% Confidence Interval (CI). Results: The meta-analysis incorporated 6 studies with a total patient population of 346. Our findings reveal that CZT-SPECT exhibited a pooled sensitivity of 79.1% (95% CI: 71.8-84.9%, I^2=0) and a specificity of 78.5% (95% CI: 70.4-84.8%, I^2=25.3%). The false-positive rate was 21.5% (95% CI: 15.2-29.6%). Moreover, CZT-SPECT demonstrated a diagnostic odds ratio of 13.75 (95% CI: 7.67-24.65), while the positive likelihood ratio was 3.67 (95% CI: 2.59-5.19), and the negative likelihood ratio stood at 0.26 (95% CI: 0.19-0.37). Conclusions: This study provides valuable insights into the diagnostic accuracy of CZT-SPECT compared to coronary angiography FFR in estimating myocardial blood flow and flow reserve. These findings indicate that while CZT-SPECT shows promising diagnostic potential, its performance is similar to coronary angiography FFR. The results will be instrumental in informing clinical decision-making and shaping future research directions in the field of cardiovascular imaging.

Prognostic Role of Dynamic CZT Imaging in Heart Failure With Preserved Ejection Fraction.

The objective of the study was to evaluate the prognostic role of myocardial flow reserve (MFR) and myocardial blood flow (MBF) estimates obtained with dynamic cadmium-zinc-telluride (CZT) imaging in the development and progression of heart failure with preserved ejection fraction (HFpEF) in patients with nonobstructive coronary artery disease (CAD) during a 12-month follow-up period. A total of 112 patients (70 men; median age of 62.5 [57.0; 69.0] years) with nonobstructive coronary artery disease were enrolled in the study. Dynamic CZT-SPECT, echocardiography, and coronary CT angiography studies were performed baseline. Distribution of patients was performed by adverse events: group 1 comprised patients with adverse outcomes (n = 25), and group 2 comprised those without it (n = 87). Based on receiver operating characteristic analysis, the levels of MFR ≤1.62 (area under the curve [AUС], 0.884; Р < 0.001), stress-MBF ≤1.35 mL/min per gram (AUС, 0.750; Р < 0.001), and NT-proBNP ≥760.5 pg/mL (AUС, 0.764; Р = 0.001) were identified as cutoff values to predict adverse outcomes. Univariate analysis revealed that type 2 diabetes mellitus ( P = 0.044), the levels of MFR ≤1.62 ( P = 0.014), stress-MBF ≤1.35 mL/min per gram ( P = 0.012), NT-proBNP ≥760.5 pg/mL ( P = 0.018), and diastolic dysfunction ( P = 0.009) were potential risk factors for the development and progression of HFpEF. Multivariate analysis demonstrated that the values of NT-proBNP ≥760.5 pg/mL (odds ratio, 1.87; 95% confidence interval, 1.17-3.62; P = 0.027) and MFR ≤1.62 (odds ratio, 2.801; 95% confidence interval, 1.19-6.55; P = 0.018) were independent predictors of adverse outcomes. Our data suggest that reduced MFR ≤1.62 obtained with dynamic CZT imaging and overexpression of NT-proBNP ≥760.5 pg/mL can individuate patients at high risk of development and progression of HFpEF during a 12-month follow-up period, independently of baseline clinical parameters and imaging variables.

Integration of quantitative absolute myocardial blood flow estimates from dynamic CZT-SPECT improves the detection of coronary artery disease

BackgroundBalanced ischemia with multi-vessel coronary artery disease (CAD) is difficult to diagnose with semiquantitative single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). Dynamic cardiac SPECT provides quantitative estimations of stenosis severity and ischemic burden by assessing myocardial flow reserve (MFR) and myocardial blood flow (MBF). The aim of this study was to evaluate the incremental value of dynamic SPECT in multi-vessel coronary artery disease (CAD). MethodsPatients with suspected CAD who underwent dynamic ECG-gated dipyridamole MPI and coronary angiography within 6 months were retrospectively reviewed. The performance of summed stress, rest and difference scores (SSS, SRS, SDS), post-stress and resting MBF (MBFs, MBFr) and MFR were compared at both patient level and vessel level. ResultsIn 32 patients with 39 stenotic vessels, 12 had three-vessel disease (38%). Globally increased SSS and impaired MBF values were significantly associated with significant CAD at the patient level, but SDS and MFR were not. Regional increases in SSS and reductions in both MBFs and MBFr were significantly associated with stenotic vessels. The best cutoff value of global MBFs to predict CAD was 3.5 ml·g−1·min−1 (area under the curve, AUC = .84, P = .002). The best cutoff value of regional MBFs to detect significant stenosis was 3.6 ml·g−1·min−1 (AUC = .74, P < .001). However, the best possible cut-off values of MFR were not found. Sex-difference in both global and regional MBFr but MBFs was found, which might result in the non-significance in MFR. ConclusionsThis study validated a clinically available method to quantify MFR using dynamic CZT-SPECT. This method improved the detectability of multi-vessel CAD, and absolute MBFs was superior to MFR and other semiquantitative MPI parameters.

Accuracy and Reproducibility of Myocardial Blood Flow Quantification by Single Photon Emission Computed Tomography Imaging in Patients With Known or Suspected Coronary Artery Disease.

Single photon emission computed tomography (SPECT) has limited ability to identify multivessel and microvascular coronary artery disease. Gamma cameras with cadmium zinc telluride detectors allow the quantification of absolute myocardial blood flow (MBF) and myocardial flow reserve (MFR). However, evidence of its accuracy is limited, and of its reproducibility is lacking. We aimed to validate 99mTc-sestamibi SPECT MBF and MFR using standard and spline-fitted reconstruction algorithms compared with 13N-ammonia positron emission tomography in a cohort of patients with known or suspected coronary artery disease and to evaluate the reproducibility of this technique. Accuracy was assessed in 34 participants who underwent dynamic 99mTc-sestamibi SPECT and 13N-ammonia positron emission tomography and reproducibility in 14 participants who underwent 2 99mTc-sestamibi SPECT studies, all within 2 weeks. A rest/pharmacological stress single-day SPECT protocol was performed. SPECT images were reconstructed using a standard ordered subset expectation maximization (OSEM) algorithm with (N=21) and without (N=30) application of spline fitting. SPECT MBF was quantified using a net retention kinetic model' and MFR was derived as the stress/rest MBF ratio. SPECT global MBF with splines showed good correlation with 13N-ammonia positron emission tomography (r=0.81, P<0.001) and MFR estimates (r=0.74, P<0.001). Correlations were substantially weaker for standard reconstruction without splines (r=0.61, P<0.001 and r=0.34, P=0.07, for MBF and MFR, respectively). Reproducibility of global MBF estimates with splines in paired SPECT scans was good (r=0.77, P<0.001), while ordered subset expectation maximization without splines led to decreased MBF (r=0.68, P<0.001) and MFR correlations (r=0.33, P=0.3). There were no significant differences in MBF or MFR between the 2 reproducibility scans independently of the reconstruction algorithm (P>0.05 for all). MBF and MFR quantification using 99mTc-sestamibi cadmium zinc telluride SPECT with spatiotemporal spline fitting improved the correlation with 13N-ammonia positron emission tomography flow estimates and test/retest reproducibility. The use of splines may represent an important step toward the standardization of SPECT flow estimation.

Classification of Myocardial Blood Flow based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging using Hierarchical Bayesian Models

AbstractDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising approach to assess microvascular blood flow (perfusion) within the myocardium, and the Fermi microvascular perfusion model is widely applied to extract estimates of the myocardial blood flow (MBF) from DCE-MRI data sets. The classification of myocardial tissues into normal (healthy) and hypoperfused (lesion) regions provides new opportunities for the diagnosis of coronary heart disease and for advancing our understanding of the aetiology of this highly prevalent disease. In the present paper, the Fermi model is combined with a hierarchical Bayesian model (HBM) and a Markov random fields prior to automate this classification. The proposed model exploits spatial context information to smooth the MBF estimates while sharpening the edges between lesions and healthy tissues. The model parameters are approximately sampled from the posterior distribution with Markov chain Monte Carlo (MCMC), and we demonstrate that this enables robust classification of myocardial tissue elements based on estimated MBF, along with sound uncertainty quantification. A well-established traditional method, based on a Gaussian mixture model (GMM) trained with the expectation–maximisation algorithm, is used as a benchmark for comparison.

Fractal analysis of 4D dynamic myocardial stress-CT perfusion imaging differentiates micro- and macrovascular ischemia in a multi-center proof-of-concept study

Fractal analysis of dynamic, four-dimensional computed tomography myocardial perfusion (4D-CTP) imaging might have potential for noninvasive differentiation of microvascular ischemia and macrovascular coronary artery disease (CAD) using fractal dimension (FD) as quantitative parameter for perfusion complexity. This multi-center proof-of-concept study included 30 rigorously characterized patients from the AMPLIFiED trial with nonoverlapping and confirmed microvascular ischemia (nmicro = 10), macrovascular CAD (nmacro = 10), or normal myocardial perfusion (nnormal = 10) with invasive coronary angiography and fractional flow reserve (FFR) measurements as reference standard. Perfusion complexity was comparatively high in normal perfusion (FDnormal = 4.49, interquartile range [IQR]:4.46–4.53), moderately reduced in microvascular ischemia (FDmicro = 4.37, IQR:4.36–4.37), and strongly reduced in macrovascular CAD (FDmacro = 4.26, IQR:4.24–4.27), which allowed to differentiate both ischemia types, p < 0.001. Fractal analysis agreed excellently with perfusion state (κ = 0.96, AUC = 0.98), whereas myocardial blood flow (MBF) showed moderate agreement (κ = 0.77, AUC = 0.78). For detecting CAD patients, fractal analysis outperformed MBF estimation with sensitivity and specificity of 100% and 85% versus 100% and 25%, p = 0.02. In conclusion, fractal analysis of 4D-CTP allows to differentiate microvascular from macrovascular ischemia and improves detection of hemodynamically significant CAD in comparison to MBF estimation.

Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning

Patient motion during dynamic PET imaging can induce errors in myocardial blood flow (MBF) estimation. Motion correction for dynamic cardiac PET is challenging because the rapid tracer kinetics of 82Rb leads to substantial tracer distribution change across different dynamic frames over time, which can cause difficulties for image registration-based motion correction, particularly for early dynamic frames. In this paper, we developed an automatic deep learning-based motion correction (DeepMC) method for dynamic cardiac PET. In this study we focused on the detection and correction of inter-frame rigid translational motion caused by voluntary body movement and pattern change of respiratory motion. A bidirectional-3D LSTM network was developed to fully utilize both local and nonlocal temporal information in the 4D dynamic image data for motion detection. The network was trained and evaluated over motion-free patient scans with simulated motion so that the motion ground-truths are available, where one million samples based on 65 patient scans were used in training, and 600 samples based on 20 patient scans were used in evaluation. The proposed method was also evaluated using additional 10 patient datasets with real motion. We demonstrated that the proposed DeepMC obtained superior performance compared to conventional registration-based methods and other convolutional neural networks (CNN), in terms of motion estimation and MBF quantification accuracy. Once trained, DeepMC is much faster than the registration-based methods and can be easily integrated into the clinical workflow. In the future work, additional investigation is needed to evaluate this approach in a clinical context with realistic patient motion.

Optimization of image sampling rate to lower the radiation dose of dynamic myocardial CT perfusion

BackgroundDynamic myocardial CT perfusion (CTP) has emerged as a potential strategy to combine anatomical and functional evaluation in a single modality. However, this method results in a high radiation dose. MethodsDynamic CTP was performed in 56 patients with suspected or known ischemic heart disease of whom 48 had complete CT-data. Datasets with reduced sampling rate of 2- and 3 RR-intervals (2RR and 3RR) were constructed post hoc. Myocardial blood flow (MBF) estimates from the 2RR and 3RR datasets were compared with estimates based on the full dataset (1RR) using the two one-sided test of equivalence for paired samples. ResultsSignificant equivalence was found for rest MBFLV (p ​< ​0.001), stress MBFLV (p ​< ​0.001) and for the CFRLV (p ​= ​0.005) when comparing 2RR blood flow estimates with the results based on the 1RR dataset. The 2RR reconstruction protocol led to an estimated reduction in radiation dose of 35.4 ​± ​3.8%. ConclusionMBF can be quantitated with dynamic CTP using a sampling strategy of one volume for every second heartbeat. This strategy could lead to a significant reduction in radiation dose.

Influence of the arterial input sampling location on the diagnostic accuracy of cardiovascular magnetic resonance stress\xa0myocardial perfusion quantification

BackgroundQuantification of myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) by cardiovascular magnetic resonance (CMR) perfusion requires sampling of the arterial input function (AIF). While variation in the AIF sampling location is known to impact quantification by CMR and positron emission tomography (PET) perfusion, there is no evidence to support the use of a specific location based on their diagnostic accuracy in the detection of coronary artery disease (CAD). This study aimed to evaluate the accuracy of stress MBF and MPR for different AIF sampling locations for the detection of abnormal myocardial perfusion with expert visual assessment as the reference.Methods Twenty-five patients with suspected or known CAD underwent vasodilator stress-rest perfusion with a dual-sequence technique at 3T. A low-resolution slice was acquired in 3-chamber view to allow AIF sampling at five different locations: left atrium (LA), basal left ventricle (bLV), mid left ventricle (mLV), apical left ventricle (aLV) and aortic root (AoR). MBF and MPR were estimated at the segmental level using Fermi function-constrained deconvolution. Segments were scored as having normal or abnormal perfusion by visual assessment and the diagnostic accuracy of stress MBF and MPR for each location was evaluated using receiver operating characteristic curve analysis.ResultsIn both normal (300 out of 400, 75 %) and abnormal segments, rest MBF, stress MBF and MPR were significantly different across AIF sampling locations (p < 0.001). Stress MBF for the AoR (normal: 2.42 (2.15–2.84) mL/g/min; abnormal: 1.71 (1.28–1.98) mL/g/min) had the highest diagnostic accuracy (sensitivity 80 %, specificity 85 %, area under the curve 0.90; p < 0.001 versus stress MBF for all other locations including bLV: normal: 2.78 (2.39–3.14) mL/g/min; abnormal: 2.22 (1.83–2.48) mL/g/min; sensitivity 91 %, specificity 63 %, area under the curve 0.81) and performed better than MPR for the LV locations (p < 0.01). MPR for the AoR (normal: 2.43 (1.95–3.14); abnormal: 1.58 (1.34–1.90)) was not superior to MPR for the bLV (normal: 2.59 (2.04–3.20); abnormal: 1.69 (1.36–2.14); p = 0.717).ConclusionsThe AIF sampling location has a significant impact on MBF and MPR estimates by CMR perfusion, with AoR-based stress MBF comparing favorably to that for the current clinical reference bLV.

Use of quantitative cardiovascular magnetic resonance myocardial perfusion mapping for characterization of ischemia in patients with left internal mammary coronary artery bypass grafts

BackgroundQuantitative myocardial perfusion mapping using cardiovascular magnetic resonance (CMR) is validated for myocardial blood flow (MBF) estimation in native vessel coronary artery disease (CAD). Following coronary artery bypass graft (CABG) surgery, perfusion defects are often detected in territories supplied by the left internal mammary artery (LIMA) graft, but their interpretation and subsequent clinical management is variable.MethodsWe assessed myocardial perfusion using quantitative CMR perfusion mapping in 38 patients with prior CABG surgery, all with angiographically-proven patent LIMA grafts to the left anterior descending coronary artery (LAD) and no prior infarction in the LAD territory. Factors potentially determining MBF in the LIMA–LAD myocardial territory, including the impact of delayed contrast arrival through the LIMA graft were evaluated.ResultsPerfusion defects were reported on blinded visual analysis in the LIMA–LAD territory in 27 (71%) cases, despite LIMA graft patency and no LAD infarction. Native LAD chronic total occlusion (CTO) was a strong independent predictor of stress MBF (B = − 0.41, p = 0.014) and myocardial perfusion reserve (MPR) (B = − 0.56, p = 0.005), and was associated with reduced stress MBF in the basal (1.47 vs 2.07 ml/g/min; p = 0.002) but not the apical myocardial segments (1.52 vs 1.87 ml/g/min; p = 0.057). Extending the maximum arterial time delay incorporated in the quantitative perfusion algorithm, resulted only in a small increase (3.4%) of estimated stress MBF.ConclusionsPerfusion defects are frequently detected in LIMA–LAD subtended territories post CABG despite LIMA patency. Although delayed contrast arrival through LIMA grafts causes a small underestimation of MBF, perfusion defects are likely to reflect true reductions in myocardial blood flow, largely due to proximal native LAD disease.

Impact of the Choice of Native T1 in Pixelwise Myocardial Blood Flow Quantification.

BackgroundQuantification of myocardial blood flow (MBF) from dynamic contrast‐enhanced (DCE) MRI can be performed using a signal intensity model that incorporates T1 values of blood and myocardium.PurposeTo assess the impact of T1 values on pixelwise MBF quantification, specifically to evaluate the influence of 1) study population‐averaged vs. subject‐specific, 2) diastolic vs. systolic, and 3) regional vs. global myocardial T1 values.Study TypeProspective.SubjectsFifteen patients with chronic coronary heart disease.Field Strength/Sequence3T; modified Look–Locker inversion recovery for T1 mapping and saturation recovery gradient echo for DCE imaging, both acquired in a mid‐ventricular short‐axis slice in systole and diastole.AssessmentMBF was estimated using Fermi modeling and signal intensity nonlinearity correction with different T1 values: study population‐averaged blood and myocardial, subject‐specific systolic and diastolic, and segmental T1 values. Myocardial segments with perfusion deficits were identified visually from DCE series.Statistical TestsThe relationships between MBF parameters derived by different methods were analyzed by Bland–Altman analysis; corresponding mean values were compared by t‐test.ResultsUsing subject‐specific diastolic T1 values, global diastolic MBF was 0.61 ± 0.13 mL/(min·g). It did not differ from global MBF derived from the study population‐averaged T1 (P = 0.88), but the standard deviation of differences was large (0.07 mL/(min·g), 11% of mean MBF). Global diastolic and systolic MBF did not differ (P = 0.12), whereas global diastolic MBF using systolic (0.62 ± 0.13 mL/(min·g)) and diastolic T1 values differed (P < 0.05). If regional instead of global T1 values were used, segmental MBF was lower in segments with perfusion deficits (bias = −0.03 mL/(min·g), −7% of mean MBF, P < 0.05) but higher in segments without perfusion deficits (bias = 0.01 mL/(min·g), 1% of mean MBF, P < 0.05).Data ConclusionWhereas cardiac phase‐specific T1 values have a minor impact on MBF estimates, subject‐specific and myocardial segment‐specific T1 values substantially affect MBF quantification.Level of Evidence3Technical Efficacy Stage3

Quantification of Myocardial Blood Flow by Machine Learning Analysis of Modified Dual Bolus MRI Examination

Contrast-enhanced magnetic resonance imaging (MRI) is a promising method for estimating myocardial blood flow (MBF). However, it is often affected by noise from imaging artefacts, such as dark rim artefact obscuring relevant features. Machine learning enables extracting important features from such noisy data and is increasingly applied in areas where traditional approaches are limited. In this study, we investigate the capacity of machine learning, particularly support vector machines (SVM) and random forests (RF), for estimating MBF from tissue impulse response signal in an animal model. Domestic pigs (n = 5) were subjected to contrast enhanced first pass MRI (MRI-FP) and the impulse response at different regions of the myocardium (n = 24/pig) were evaluated at rest (n = 120) and stress (n = 96). Reference MBF was then measured using positron emission tomography (PET). Since the impulse response may include artefacts, classification models based on SVM and RF were developed to discriminate noisy signal. In addition, regression models based on SVM, RF and linear regression (for comparison) were developed for estimating MBF from the impulse response at rest and stress. The classification and regression models were trained on data from 4 pigs (n = 168) and tested on 1 pig (n = 48). Models based on SVM and RF outperformed linear regression, with higher correlation (RSVM2 = 0.81, RRF2 = 0.74, Rlinear_regression2 = 0.60; ρSVM = 0.76, ρRF = 0.76, ρlinear_regression = 0.71) and lower error (RMSESVM = 0.67 mL/g/min, RMSERF = 0.77 mL/g/min, RMSElinear_regression = 0.96 mL/g/min) for predicting MBF from MRI impulse response signal. Classifier based on SVM was optimal for detecting impulse response signals with artefacts (accuracy = 92%). Modified dual bolus MRI signal, combined with machine learning, has potential for accurately estimating MBF at rest and stress states, even from signals with dark rim artefacts. This could provide a protocol for reliable and easy estimation of MBF, although further research is needed to clinically validate the approach.

Noninvasive estimation of quantitative myocardial blood flow with Tc-99m MIBI by a compartment model analysis in rat

BackgroundWe aimed to investigate the use of dynamic cardiac planar images to estimate myocardial blood flow (MBF) by a compartment model analysis using time-to-peak (TP) map and compared it by the microsphere technique in rat. Positron emission tomography is considered the gold standard method, but is not available everywhere. By contrast, although myocardial perfusion imaging (MPI) with single-photon tracers is more widely available, it may be difficult to obtain adequate region of interest (ROI) settings. We proposed using the TP map to set the ROI, and hypothesized that this method could facilitate the measurement of absolute MBF by MPI in rat. MethodsTwenty-one normal rats were studied. Dynamic planar images with Tc-99m MIBI were obtained, and input function and cardiac ROIs were set using the obtained TP map. MBF was estimated by a one-compartment model analysis with the Renkin-Crone model and by the microsphere technique. ResultsThe MBFs from these two methods were significantly correlated. A negative proportional bias was observed, but no significant difference was observed between the mean MBFs calculated with each method. ConclusionsMBF estimation by a compartment model analysis using TP map could facilitate absolute MBF measurement in rats.

Preclinical Validation of a Single-Scan Rest/Stress Imaging Technique for 13N-Ammonia Positron Emission Tomography Cardiac Perfusion Studies.

We previously proposed a technique for quantitative measurement of rest and stress absolute myocardial blood flow (MBF) using a 2-injection single-scan imaging session. Recently, we validated the method in a pig model for the long-lived radiotracer 18F-Flurpiridaz with adenosine as a pharmacological stressor. The aim of the present work is to validate our technique for 13NH3. Nine studies were performed in 6 pigs; 5 studies were done in the native state and 4 after infarction of the left anterior descending artery. Each study consisted of 3 dynamic scans: a 2-injection rest-rest single-scan acquisition (scan A), a 2-injection rest/stress single-scan acquisition (scan B), and a conventional 1-injection stress acquisition (scan C). Variable doses of adenosine combined with dobutamine were administered to induce a wide range of MBF. The 2-injection single-scan measurements were fitted with our nonstationary kinetic model (MGH2). In 4 studies, 13NH3 injections were paired with microsphere injections. MBF estimates obtained with our method were compared with those obtained with the standard method and with microspheres. We used a model-based method to generate separate rest and stress perfusion images. In the absence of stress (scan A), the MBF values estimated by MGH2 were nearly the same for the 2-radiotracer injections (mean difference: 0.067±0.070 mL·min-1·cc-1, limits of agreement: [-0.070 to 0.204] mL·min-1·cc-1), showing good repeatability. Bland-Altman analyses demonstrated very good agreement with the conventional method for both rest (mean difference: -0.034±0.035 mL·min-1·cc-1, limits of agreement: [-0.103 to 0.035] mL·min-1·cc-1) and stress (mean difference: 0.057±0.361 mL·min-1·cc-1, limits of agreement: [-0.651 to 0.765] mL·min-1·cc-1) MBF measurements. Positron emission tomography and microsphere MBF measurements correlated closely. Very good quality perfusion images were obtained. This study provides in vivo validation of our single-scan rest-stress method for 13NH3 measurements. The 13NH3 rest/stress myocardial perfusion imaging procedure can be compressed into a single positron emission tomography scan session lasting less than 15 minutes.

Reliable quantification of myocardial sympathetic innervation and regional denervation using [11C]meta-hydroxyephedrine PET.

Cardiac sympathetic nervous system (SNS) dysfunction is associated with poor prognosis in chronic heart failure patients. This study characterized the reproducibility and repeatability of [11C]meta-hydroxyephedrine (HED) positron emission tomography (PET) quantification of cardiac SNS innervation, regional denervation, and myocardial blood flow (MBF). Dynamic HED PET-CT scans were performed 47 ± 22days apart in 20 patients with stable heart failure and reduced ejection fraction. Three observers, blinded to clinical data, used FlowQuant® to evaluate the test-retest repeatability and inter- and intra-observer reproducibility of HED tracer uptake and clearance rates to measure global (LV-mean) retention index (RI), volume of distribution (VT), and MBF. Values were also compared with and without regional partial-volume correction. Regional denervation was quantified as %LV defect size of values < 75% of the LV-maximum. Test-retest repeatability and observer reproducibility were evaluated using intra-class-correlation (ICC) and Bland-Altman coefficient of repeatability (NPC). Intra- and inter-observer correlations of both VT and RI were excellent (ICC = 0.93-0.99). Observer reproducibility (NPC = 3-13%) was lower than test-retest repeatability (NPC = 12-61%). Both regional (%LV defect size) and global (LV-mean) measures of sympathetic innervation were more repeatable using the simple RI model compared to VT (NPC = 12% vs. 19% and 30% vs. 54%). Using either model, quantification of regional denervation (defect size) was consistently more reliable than the global LV-mean values of RI or VT. Regional partial-volume correction degraded repeatability of both the global and regional VT measures by 2-12%. Test-retest repeatability of MBF estimation was relatively poor (NPC = 30-61%) compared with the RI. Quantitative measures of global and regional SNS innervation were most repeatable using the simple RI method of analysis compared with the more complex VT. Observer variability was significantly lower than the test-retest repeatability using a highly automated analysis program. These results support the use of the simple RI method for reliable analysis of HED PET images in clinical research studies for future evaluation of new therapies and for risk stratification in patients with heart failure.

Blood pool and tissue phase patient motion effects on 82rubidium PET myocardial blood flow quantification

BackgroundPatient motion can lead to misalignment of left ventricular volumes of interest and subsequently inaccurate quantification of myocardial blood flow (MBF) and flow reserve (MFR) from dynamic PET myocardial perfusion images. We aimed to identify the prevalence of patient motion in both blood and tissue phases and analyze the effects of this motion on MBF and MFR estimates. MethodsWe selected 225 consecutive patients that underwent dynamic stress/rest rubidium-82 chloride (82Rb) PET imaging. Dynamic image series were iteratively reconstructed with 5- to 10-second frame durations over the first 2 minutes for the blood phase and 10 to 80 seconds for the tissue phase. Motion shifts were assessed by 3 physician readers from the dynamic series and analyzed for frequency, magnitude, time, and direction of motion. The effects of this motion isolated in time, direction, and magnitude on global and regional MBF and MFR estimates were evaluated. Flow estimates derived from the motion corrected images were used as the error references. ResultsMild to moderate motion (5-15 mm) was most prominent in the blood phase in 63% and 44% of the stress and rest studies, respectively. This motion was observed with frequencies of 75% in the septal and inferior directions for stress and 44% in the septal direction for rest. Images with blood phase isolated motion had mean global MBF and MFR errors of 2%-5%. Isolating blood phase motion in the inferior direction resulted in mean MBF and MFR errors of 29%-44% in the RCA territory. Flow errors due to tissue phase isolated motion were within 1%. ConclusionsPatient motion was most prevalent in the blood phase and MBF and MFR errors increased most substantially with motion in the inferior direction. Motion correction focused on these motions is needed to reduce MBF and MFR errors.

SLICR super-voxel algorithm for fast, robust quantification of myocardial blood flow by dynamic computed tomography myocardial perfusion imaging.

We created and evaluated a processing method for dynamic computed tomography myocardial perfusion imaging (CT-MPI) of myocardial blood flow (MBF), which combines a modified simple linear iterative clustering algorithm (SLIC) with robust perfusion quantification, hence the name SLICR. SLICR adaptively segments the myocardium into nonuniform super-voxels with similar perfusion time attenuation curves (TACs). Within each super-voxel, an α-trimmed-median TAC was computed to robustly represent the super-voxel and a robust physiological model (RPM) was implemented to semi-analytically estimate MBF. SLICR processing was compared with another voxel-wise MBF preprocessing approach, which included a spatiotemporal bilateral filter (STBF) for noise reduction prior to perfusion quantification. Image data from a digital CT-MPI phantom and a porcine ischemia model were evaluated. SLICR was -fold faster than voxel-wise RPM and other model-based methods while retaining sufficient resolution to show clinically relevant features, such as a transmural perfusion gradient. SLICR showed markedly improved accuracy and precision, as compared with other methods. At a simulated MBF of 100mL/min-100g and a tube current-time product of 100mAs (50% of nominal), the MBF estimates were , , and for SLICR, the voxel-wise Johnson-Wilson model, and a singular value decomposition-model independent method with STBF, respectively. SLICR estimated MBF precisely and accurately ( ) at 25% nominal dose, while other methods resulted in larger errors. With the porcine model, the SLICR results were consistent with the induced ischemia. SLICR simultaneously accelerated and improved the quality of quantitative perfusion processing without compromising clinically relevant distributions of perfusion characteristics.

P5286Cardiac magnetic resonance for quantification of absolute myocardial blood flow: comparison with [15O]H2O positron emission tomography

Abstract Background Quantification of absolute myocardial perfusion using positron emission tomography (PET) augments diagnostic performance and aids risk stratification in patients with coronary artery disease (CAD). Similar to PET, cardiac magnetic resonance imaging (CMR) allows for quantification of absolute myocardial blood flow (MBF). To date, quantitative CMR perfusion has not been compared with [15O]H2O PET, which is considered to be the gold standard for quantification of MBF. Purpose The purpose of this study was to compare CMR measurements of absolute myocardial perfusion to those obtained using [15O]H2O PET. Methods Fifty-one patients with stable CAD underwent [15O]H2O PET followed by CMR within 7 days. Late gadolinium enhancement was used to rule out presence of scar tissue. CMR perfusion imaging was performed using a single bolus dual sequence technique. Myocardial perfusion was quantified for the three main vascular territories at rest and during vasodilator stress. Results Pooled CMR estimates of absolute MBF correlated well with PET (r=0.79; p&lt;0.001) and showed good agreement (intraclass correlation coefficient [ICC]=0.78; p&lt;0.001). However, rest and stress MBF correlated weakly (r=0.31; p&lt;0.001 for rest MBF and r=0.35; p&lt;0.001 for stress MBF, figure) and showed poor agreement between PET and CMR (ICC=0.24; p&lt;0.001 for rest MBF and ICC=0.34; p&lt;0.001 for stress MBF). Similarly, CMR derived coronary flow reserve (CFR) correlated weakly (r=0.28; p&lt;0.001) and showed poor agreement with PET derived CFR (ICC=0.23; p&lt;0.001). Nevertheless, for a cut-off value of 2.3 mL/min/g for stress MBF and 2.5 for CFR, CMR and PET were concordant in 116 (79%) vascular territories for stress MBF and 104 (75%) vascular territories for CFR. Per vessel – CMR vs PET perfusion Conclusions Resting MBF, stress MBF and CFR measurements correlated poorly with [15O]H2O PET. Nevertheless, stress MBF and CFR were concordant between CMR and [15O]H2O PET in 79% and 75% of vascular territories, respectively. Acknowledgement/Funding None

Accuracy of Myocardial Blood Flow Estimation From Dynamic Contrast-Enhanced Cardiac CT Compared With PET

Background The accuracy of absolute myocardial blood flow (MBF) from dynamic contrast-enhanced cardiac computed tomography acquisitions has not been fully characterized. We evaluate computed tomography (CT) compared with rubidium-82 positron emission tomography (PET) MBF estimates in a high-risk population. Methods In a prospective trial, patients receiving clinically indicated rubidium-82 PET exams were recruited to receive a dynamic contrast-enhanced cardiac computed tomography exam. The CT protocol included a rest and stress dynamic portion each acquiring 12 to 18 cardiac-gated frames. The global MBF was estimated from the PET and CT exam. Results Thirty-four patients referred for cardiac rest-stress PET were recruited. Of the 68 dynamic contrast-enhanced cardiac computed tomography scans, 5 were excluded because of injection errors or mismatched hemodynamics. The CT-derived global MBF was highly correlated with the PET MBF (r=0.92; P<0.001) with a mean difference of 0.7±26.4%. The CT MBF estimates were within 20% of PET estimates ( P<0.02) with a mean of (1) MBF for resting flow of PET versus CT of 0.9±0.3 versus 1.0±0.2 mL/min per gram and (2) MBF for stress flow of 2.1±0.7 versus 2.0±0.8 mL/min per gram. Myocardial flow reserve was -14±28% underestimated with CT (PET versus CT myocardial flow reserve, 2.5±0.6 versus 2.2±0.6). The proposed rest+stress+computed tomography angiography protocol had a dose length product of 598±76 mGy×cm resulting in an approximate effective dose of 8.4±1.1 mSv. Conclusions In a high-risk clinical population, a clinically practical dynamic contrast-enhanced cardiac computed tomography provided unbiased MBF estimates within 20% of rubidium-82 PET. Although unbiased, the CT estimates contain substantial variance with an standard error of the estimate of 0.44 mL/min per gram. Myocardial flow reserve estimation was not as accurate as individual MBF estimates.

Calibration-free beam hardening correction for myocardial perfusion imaging using CT.

PurposeComputed tomography myocardial perfusion imaging (CT‐MPI) and coronary CTA have the potential to make CT an ideal noninvasive imaging gatekeeper exam for invasive coronary angiography. However, beam hardening (BH) artifacts prevent accurate blood flow calculation in CT‐MPI. BH correction methods require either energy‐sensitive CT, not widely available, or typically, a calibration‐based method in conventional CT. We propose a calibration‐free, automatic BH correction (ABHC) method suitable for CT‐MPI and evaluate its ability to reduce BH artifacts in single “static‐perfusion” images and to create accurate myocardial blood flow (MBF) in dynamic CT‐MPI.MethodsIn the algorithm, we used input CT DICOM images and iteratively optimized parameters in a polynomial BH correction until a BH‐sensitive cost function was minimized on output images. An input image was segmented into a soft tissue image and a highly attenuating material (HAM) image containing bones and regions of high iodine concentrations, using mean HU and temporal enhancement properties. We forward projected HAM, corrected projection values according to a polynomial correction, and reconstructed a correction image to obtain the current iteration's BH corrected image. The cost function was sensitive to BH streak artifacts and cupping. We evaluated the algorithm on simulated CT and physical phantom images, and on preclinical porcine with optional coronary obstruction and clinical CT‐MPI data. Assessments included measures of BH artifact in single images as well as MBF estimates. We obtained CT images on a prototype spectral detector CT (SDCT, Philips Healthcare) scanner that provided both conventional and virtual keV images, allowing us to quantitatively compare corrected CT images to virtual keV images. To stress test the method, we evaluated results on images from a different scanner (iCT, Philips Healthcare) and different kVp values.ResultsIn a CT‐simulated digital phantom consisting of water with iodine cylinder insets, BH streak artifacts between simulated iodine inserts were reduced from 13 ± 2 to 0 ± 1 HU. In a similar physical phantom having higher iodine concentrations, BH streak artifacts were reduced from 48 ± 6 to 1 ± 5 HU and cupping was reduced by 86%, from 248 to 23 HU. In preclinical CT‐MPI images without coronary obstruction, BH artifact was reduced from 24 ± 6 HU to less than 5 ± 4 HU at peak enhancement. Standard deviation across different regions of interest (ROI) along the myocardium was reduced from 13.26 to 6.86 HU for ABHC, comparing favorably to measurements in the corresponding virtual keV image. Corrections greatly reduced variations in preclinical MBF maps as obtained in normal animals without obstruction (FFR = 1). Coefficients of variations were 22% (conventional CT), 9% (ABHC), and 5% (virtual keV). Moreover, variations in flow tended to be localized after ABHC, giving result which would not be confused with a flow deficit in a coronary vessel territory.ConclusionThe automated algorithm can be used to reduce BH artifact in conventional CT and improve CT‐MPI accuracy particularly by removing regions of reduced estimated flow which might be misinterpreted as flow deficits.