Copyright © 2005, The European Society of Cardiology
Quantitative diagnosis of stress-induced myocardial ischemia using analysis of contrast echocardiographic parametric perfusion images*
Noninvasive Cardiac Imaging Laboratory, Department of Medicine, Section of Cardiology, University of Chicago, MC5084, 5841 S. Maryland Avenue, Chicago, IL 60637, USA
Received 9 May 2005; received in revised form 12 July 2005; accepted after revision 28 July 2005.
rlang{at}medicine.bsd.uchicago.edu
* Corresponding author. Tel.: +1 773 702 1842; fax: +1 773 702 1034. vmoravi{at}medicine.bsd.uchicago.edu
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Abstract |
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Aims Parametric imaging of myocardial perfusion provides useful visual information for the diagnosis of coronary artery disease (CAD). We developed a technique for automated detection of perfusion defects based on quantitative analysis of parametric perfusion images and validated it against coronary angiography.
Methods and results Contrast-enhanced, apical 2-, 3- and 4-chamber images were obtained at rest and with dipyridamole in 34 patients with suspected CAD. Images were analyzed to generate parametric perfusion images of the standard contrast-replenishment model parameters A, β and A·β. Each parametric image was divided into six segments, and mean parameter value (MPV) was calculated for each segment. Segmental MPV ratio between stress and rest was defined as a flow reserve index (FRI). Receiver operating characteristics (ROC) analysis was used in a Study group (N=17) to optimize FRI threshold and the minimal number of abnormal segments per vascular territory (LAD and non-LAD), required for automated detection of stress-induced perfusion defects. The optimized detection algorithm was then tested prospectively in the remaining 17 patients (Test group). LAD and non-LAD stenosis >70% was found in 19 and 17 patients, respectively. In the Study group, FRI threshold was: LAD=0.95 and non-LAD=0.68, minimal number of abnormal segments was four and two, correspondingly. Sensitivity, specificity and accuracy in the Test group were: 75%, 67% and 71% in the LAD, and 75%, 75% and 75% in the non-LAD territories.
Conclusion Automated quantitative analysis of contrast echocardiographic parametric perfusion images is feasible and may aid in the objective detection of CAD.
Keywords: Ultrasound imaging; Contrast media; Myocardial blood flow
Recent technological developments in contrast echocardiography have transformed myocardial perfusion imaging into an easier, more reliable and reproducible technique. One of the major advancements in this field was the use of destructive high-energy ultrasound pulses during continuous infusion of contrast.1–3 This technique is frequently referred to as "flash-echo" and allows the detection of perfusion defects by visualizing lack of or delay in post-flash contrast replenishment.3,4 Yet, the utility of this methodology in clinical practice could be further enhanced by the availability of accurate and objective detection of perfusion defects rather than subjective visual interpretation of contrast replenishment dynamics.5,6
Parametric perfusion imaging, which is currently commercially available (Q-Lab software, Philips), was initially developed to improve the visualization of perfusion defects. This approach also represents the first step toward objective detection of perfusion defects. Parametric images are constructed using quantitative analysis of contrast replenishment dynamics.5,7 Time-curves of myocardial videointensity measured over consecutive cardiac cycles are used to calculate model-based parameters related to perfusion, such as time-constant or rate of contrast replenishment.1,8 For each pixel, the value of the calculated parameter is displayed in a color overlay, resulting in a parametric perfusion image. In these images, under-perfused areas of the myocardium, which are associated with low pixel values, are color-coded differently from normally perfused areas.
Although parametric perfusion images may aid in the visual detection of perfusion defects, the interpretation of the color overlays remains subjective and dependent on the observer's experience. This is especially true during stress testing, when images are affected by elevated heart rate and increased cardiac translation. Accordingly, our goal was to develop and test a technique for automated detection of stress-induced perfusion defects based on quantitative analysis of parametric perfusion images. To achieve this goal, our technique was initially optimized in a group of patients with suspected coronary artery disease (CAD), and then applied to an additional group of patients to prospectively test its performance in diagnosing dipyridamole-induced perfusion abnormalities. In both groups, coronary angiography was used as the anatomic "gold standard" for comparisons, which were performed on a coronary vascular territory basis.
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Methods |
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Thirty-four patients (26 men, 66±7 years old) referred for clinically indicated coronary angiography for suspected CAD were imaged (Sonos 7500, Philips, probe S3) during continuous intravenous infusion of the contrast agent Definity (Bristol–Myers Squibb; 1.3ml in 50ml saline at 10ml/h). Imaging was performed in the apical 2-, 3- and 4-chamber views in a power modulation mode with a mechanical index of 0.1, triggered every consecutive cardiac cycle at end-systole. Bubble destruction was achieved using a packet of five high-intensity (mechanical index 1.6) pulses. Flash-echo image sequences were acquired at rest and during a vasodilator stress achieved by a 4min infusion of dipyridamole (0.14mg/kg/min) with image acquisition starting 10min after the onset of infusion. This study was approved by the Institutional Review Board and all patients provided written informed consent.
Image analysis
Flash-echo image sequences were analyzed using commercial software (Q-Lab, Philips) to generate parametric perfusion images.5 Briefly, contrast replenishment following the destructive pulses was analyzed according to indicator dilution principles, which model myocardial contrast enhancement by 
, where VI is the videointensity, A represents the difference between maximal bubble destruction and steady-state contrast enhancement and β is the characteristic constant. Theoretically, the value of A is associated with tissue blood volume, whereas β is proportional to myocardial blood flow and A·β is associated with post-flash filling rate.1,9 Parametric perfusion images of A, β and A·β were generated and displayed as color overlays using a fixed color map (Fig. 1).
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Custom software was developed to analyze all parametric perfusion images. Each of the three apical images (i.e. 2-, 3- and 4-chamber views) was divided into six segments using a standard segmentation scheme.10 In each segment, the mean value of the displayed parameter (A, β and A·β) was computed for resting and stress images. Then, the ratio of the mean values between stress and rest was used as the flow reserve index (FRI). This procedure resulted in three different ratios for each segment: A-FRI, β-FRI and A·β-FRI, corresponding to the three calculated parameters. These ratios were expected to be high in normally perfused segments, and low in under-perfused segments supplied by partially or totally occluded coronary arteries. Stress-induced perfusion defects were detected in a segment if measured FRI value was below a threshold value, which was optimized using receiver operating characteristics (ROC) analysis described below.
Myocardial segments were grouped according to coronary vascular territory: 11 segments in the left anterior descending (LAD) territory and seven segments in the non-LAD territory (Fig. 2). Analysis was performed separately for each territory. Dipyridamole-induced perfusion abnormality in a coronary vascular territory was diagnosed when the number of segments with abnormally low FRI in that territory exceeded a threshold number of segments, which was defined as the tolerance parameter and also optimized using ROC analysis.
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ROC analysis
The technique was initially optimized in a subgroup of 17 patients (Study group). Specifically, for each type of parametric image (A, β and A·β), two thresholds necessary for automated detection of stress-induced perfusion abnormalities were optimized to yield maximum levels of agreement with coronary angiography. These included the FRI threshold and the threshold number of segments per perfusion territory. To this effect, agreement with coronary angiography was noted when dipyridamole-induced perfusion abnormality was detected in a vascular territory supplied by a coronary artery with evidence of
70% stenosis. The optimization was performed using receiver operating characteristics (ROC) analysis. For each perfusion territory (i.e. LAD and non-LAD), an ROC curve was created for each type of parametric image (A, β and A·β) and tolerance parameter values 1, 2, 3 and 4, resulting in 12 curves per perfusion territory. Each curve was created by calculating the sensitivity and specificity of the automated detection versus angiography, while varying FRI threshold between 0.1 and 1.5 in 0.01 increments. For each perfusion territory, the area under each ROC curve, which reflects the accuracy of the detection scheme, was used to choose the optimal settings, including image type and the tolerance parameter. Then, from this particular curve, the corresponding FRI threshold was determined by choosing the point of highest accuracy.
Performance testing
Initial testing of the performance of our algorithm with the optimal settings included calculating its sensitivity, specificity, positive and negative predictive values (PPV and NPV) and overall accuracy against the coronary angiography reference in the Study group. To further evaluate the performance of the optimized algorithm, it was then tested prospectively by calculating the sensitivity, specificity, PPV and NPV and overall accuracy in the remaining 17 patients (Test group).
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Coronary angiography indicated that nine of 34 patients did not have significant stenosis, eight patients had single-vessel LAD stenosis >70%, six patients had significant (>70%) either single or double vessel non-LAD stenosis, whereas the remaining 11 patients had both LAD and non-LAD disease. Seven of 34 patients had previous myocardial infarction. Patients were randomly assigned to either the Study group or the Test Group, while maintaining a roughly equal number of patients from each of the above categories in both groups.
Fig. 3 shows an example of A·β and β parametric images calculated from two flash-echo sequences acquired in the apical 4-chamber view, obtained at rest (left) and 10min after onset of dipyridamole infusion (middle) in a patient with no significant coronary stenosis. In this example, the relatively low values of A·β and β in the lateral wall, indicated by shades of red, improved with stress, as reflected by the shades of yellow and green in the same area of the myocardium. Consequently, the values of segmental A·β-FRI and β-FRI (right) are 1.0, indicating an increase in perfusion in most segments with dipyridamole, reflecting normal flow reserve in both coronary vascular territories.
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The ROC analysis for the LAD perfusion territory indicated that A·β parametric images with the tolerance parameter of four segments resulted in the highest accuracy of automated detection of perfusion defects. The area under the corresponding ROC curve was 0.72, while the areas under all ROC curves calculated for the LAD territory with the β-images and the A-images were less than 0.6. The optimal threshold for A·β-FRI was 0.95 (Fig. 4, left). In the non-LAD perfusion territory, β parametric images with the tolerance parameter of two segments resulted in the highest accuracy of automated detection of perfusion defects. The area under the corresponding ROC curve was 0.80, while the areas under all ROC curves calculated for the non-LAD territory with the A·β-images and the A-images were less than 0.6. The optimal threshold of β-FRI was 0.68 (Fig. 4, right).
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Fig. 5 shows an example of A·β parametric images obtained in a patient with 90% proximal LAD stenosis. The parametric image obtained from the resting sequence (left) shows normal perfusion, as reflected by the shades of green throughout most of the LV myocardium. In contrast, the image obtained with dipyridamole (middle) depicts a stress-induced defect in the mid and apical septum as well as apical lateral wall (indicated by shades of yellow and orange), consistent with significant stenosis in the proximal LAD artery. Indeed, the A·β-FRI in these three segments was below the 0.95 threshold (right), reflecting reduced coronary flow reserve in the LAD territory.
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Fig. 6 shows an example of β parametric images obtained in a patient with 90% circumflex stenosis. The parametric perfusion image calculated from the resting sequence (left) shows reduced perfusion in the apical segments (yellow and orange) and normal perfusion in the rest of the myocardium. In contrast, the stress parametric image (middle) shows: (i) normal perfusion in most of the apex, indicating normal flow reserve in this region, and (ii) a defect in the mid-lateral wall (shades of red), consistent with significant stenosis in the circumflex coronary artery. The value of β-FRI in the mid-lateral segment was only 0.1, well below the 0.68 threshold (right), reflecting abnormal coronary flow reserve in the circumflex territory.
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The performance of the automated detection scheme against coronary angiography for both the Study and Test groups is shown in Table 1. In the Study group, the accuracy of the automated detection was 75% in the LAD territory and 70% in the non-LAD territory. Although prospective testing resulted in slightly lower performance levels in the Test group, the overall accuracy of above 70% in both perfusion territories still remained within a clinically acceptable range.
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Discussion |
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The flash-echo technique is currently the "state of the art" approach for echocardiographic assessment of myocardial perfusion.11 Its value in the diagnosis of CAD has been previously described.12–17 Parametric perfusion imaging is a recent addition to this methodology which was developed to facilitate the visualization of perfusion defects. Nevertheless, the interpretation of parametric perfusion images remains subjective, experience-dependent5 and thus prone to wide inter-measurement variability. This limitation may hinder widespread use of this technique in the clinical setting. To the best of our knowledge, this study is the first to develop and validate a technique for automated and objective detection of stress-induced perfusion defects based on quantitative segmental analysis of these parametric perfusion images.
Typically, methods for automated detection of abnormalities that are based on a priori knowledge of a threshold value are developed by acquiring data in large groups of normal subjects and establishing a normal range of the variable of interest. However, this methodology may be inappropriate for optimizing multi-variable analysis procedures, wherein the variables are not independent and one of them cannot be defined in the normal population. Indeed, our approach for automated detection of perfusion defects represents such case. This detection algorithm relies on a priori knowledge of three variables: (i) which type of parametric image, A, β or A·β, is optimal for each coronary vascular territory, (ii) threshold FRI value, and (iii) tolerance parameter reflecting the minimum number of segments with abnormally low FRI per vascular territory, necessary for diagnosing an abnormality in that territory. Not only these three variables are inter-dependent, but also the tolerance parameter cannot be defined in a population comprising only normal subjects.
Accordingly, we chose a different approach. ROC analysis is a standard statistical tool that allows the optimization of complex, inter-dependent, multi-variable problems. This is achieved by serially calculating the levels of agreement with an independent reference technique for each possible combination of values of the variables of interest and determining which combination results in optimal performance. Our Study group was used to optimize the three variables of interest against coronary angiography. The technique with the optimized thresholds was then applied prospectively to the Test group. This study design allowed us to verify the ability of our algorithm to automatically detect dipyridamole-induced perfusion defects and establish the true accuracy of this technique in patients who were not part of the optimization process.
Although coronary angiography is considered the invasive "gold standard" for clinical assessment of myocardial perfusion measurements, it does not directly determine myocardial perfusion, as assessed by myocardial contrast echocardiography. Although angiography assesses coronary flow, which is related to perfusion, this relationship is not simple due to individual differences in coronary anatomy as well as the existence of collateral circulation. Nevertheless, stenosis of 70% on angiography is commonly used in literature to corroborate perfusion defects, which was also part of our study design. It is well recognized that the diagnosis of coronary stenosis of lower degree is more challenging as is the interpretation of its clinical impact on an individual patient. Non-surprisingly, our attempt to correlate lower grade stenosis with perfusion defects markedly reduced the accuracy of the automated detection. Importantly, the accuracy of the automated technique against critical stenosis was found to be relatively high in both groups of patients, and remained within a clinically acceptable range of above 70% even when tested prospectively. These accuracy levels were comparable to those of other quantitative vasodilator stress myocardial contrast echocardiographic studies.5,18–22
The automated detection of stress-induced perfusion defects was slightly more accurate in the Study group as compared to the Test group. This finding was expected, since the algorithm was optimized for the Study group. An interesting observation was that optimal detection of stress-induced perfusion defects was achieved by analyzing different types of parametric images for the LAD and the non-LAD territories. While A·β-images were superior for detecting defects in the LAD territory, β-images provided better results in the non-LAD territory. A possible explanation is that the basal segments, mostly perfused by non-LAD coronary arteries, are affected by acoustic shadowing that reduces the level of steady-state contrast enhancement, reflected by the parameter A. The fact that parameter β is, at least theoretically, independent of the parameter A may explain the better diagnostic value of β-images in these segments. These differences may also explain the differences in FRI threshold values between the two perfusion territories. The difference between the tolerance parameters in the two territories is likely to be directly related to the total number of segments in each territory seen in the three apical views. In both territories, the tolerance parameters roughly represent one-third of the total number of segments.
One of the limitations of this study is the relatively small number of patients in this study, which precluded the determination of a threshold value optimized for each segment separately. Such extensive optimization may result in even further improvement in the performance of the automated technique. Another limitation is the suboptimal quality of flash-echo sequences obtained in patients with poor acoustic windows as a result of previous surgery. In addition, the fact that inter-measurement variability of the automated detection was not reported could be viewed as a limitation of our study. However, the use of parametric images for visualizing perfusion defects was previously validated by other investigators.5,7 Our goal was not to reproduce these findings, but rather to develop and test an automated technique for the detection of pharmacologically induced perfusion abnormalities based on quantitative analysis of parametric perfusion images. Since this analysis is fully automated, there is no associated inter-measurement variability.
In conclusion, this study demonstrated the feasibility of noninvasive automated detection of dipyridamole stress-induced perfusion defects by analysis of parametric perfusion images. Following multi-variable optimization and prospective testing, this new technique was found to be accurate within the clinically acceptable range, with the important advantage of being fully automated and thus free of inter-measurement variability, once the parametric images are created. Accordingly, this approach may contribute toward accurate echocardiographic diagnosis of ischemic heart disease.
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* This work has been supported by research grant from Bristol–Myers Squibb and a Grant-in-Aid from the American Heart Association (VMA).
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