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Published online before print December 21, 2005, 10.1148/radiol.2382041697

(Radiology 2005;238:464.)

A more recent version of this article appeared on December 1, 2005
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© RSNA, 2005

Cardiac Imaging

Perfusion Impairment in Patients with Normal-appearing Coronary Arteries: Identification with Contrast-enhanced MR Imaging1

Luíz Francisco Rodrigues de Ávila, MD, PhD, Juliano Lara Fernandes, MD, Carlos Eduardo Rochitte, MD, PhD, Giovanni G. Cerri, MD, PhD and José Parga Filho, MD, PhD

1 From the Cardiovascular Magnetic Resonance Laboratory Heart Institute (L.F.R.d.A., J.L.F., C.E.R., J.P.F.) and Institute of Radiology (G.G.C.), University of São Paulo Medical School, Coord Diagnostico por Imagem, Av Dr Enéas de Carvalho Aguiar 44, São Paulo, SP 05403-000, Brazil. Received December 3, 2004; revision requested December 15; revision received February 18, 2005; accepted March 15; final version accepted, June 28. Address correspondence to J.L.F. (e-mail: jlaraf{at}terra.com.br).


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Purpose: To prospectively determine the feasibility of using first-pass magnetic resonance (MR) imaging to distinguish between myocardial segments in patients with coronary artery disease (CAD) of different degrees of obstruction and those in patients with normal-appearing coronary arteries.

Materials and Methods: The study was approved by the institutional ethics committee, and all patients provided informed consent. First-pass contrast material–enhanced MR imaging was performed at rest and after the infusion of dipyridamole in 37 patients (29 men, eight women; mean age, 57.2 years ± 10.5 [standard deviation]) who had positive exercise test results or a clinical history of CAD. Myocardial segments were divided into five groups according to the degree of obstruction in the supplying artery. Signal intensity upslope, peak signal intensity, and time to peak signal intensity, as well as hyperemia-to-rest (HR) ratios for each of these three variables, were analyzed for each segment by using a generalized linear model.

Results: Signal intensity upslope in patients with normal coronary arteries at angiography was significantly higher than that in patients with CAD (P < .001). Signal intensity upslope for segments in patients without CAD was significantly different from that for normal-appearing segments in patients with CAD (P < .001). Signal intensity upslope (P < .05) and peak signal intensity (P < .01) enabled the differentiation of segments with more than 70% reduction in luminal diameter from those in all other groups. HR ratios demonstrated findings that were similar to those obtained by using each signal intensity variable alone.

Conclusion: First-pass MR imaging can be used to distinguish segments with different degrees of obstructive CAD. Importantly, MR imaging can help identify segments with impaired perfusion and normal-appearing coronary arteries in patients with CAD and can demonstrate obstructive lesions in other territories.

© RSNA, 2005


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Although mortality rates for patients with coronary artery disease (CAD) have been declining in the past 2 decades, ischemic heart disease still remains a major public health problem (1). Diagnosis and risk stratification are essential for the care of these patients (2).

The clinically accepted reference standard for the identification of obstructive lesions that lead to decreased coronary perfusion and subsequent myocardial ischemia is coronary angiography. Although it is still regarded as the definitive method for demonstrating CAD, coronary angiography is invasive and has relatively low accuracy in estimating the physiologic importance of coronary stenosis (3). Because of these limitations and because coronary angiography can provide information on only the anatomic status of a coronary obstruction but not on its functional importance or the status of the myocardial microcirculation, a number of noninvasive methods have been developed to aid in the detection of myocardial ischemia (4).

Compared with other noninvasive methods, magnetic resonance (MR) imaging has been increasingly accepted as an imaging modality that could improve the identification of patients with CAD. By permitting an integrated examination of cardiac function, myocardial perfusion, and viability, MR imaging has become important in the management of both acute and chronic coronary syndromes (5,6). The most traditionally used method in MR imaging for the examination of myocardial perfusion is first-pass analysis with peripherally injected contrast material (7). The quantitative assessment of first-pass contrast material–enhanced perfusion at MR imaging by using signal intensity–time curves (8) results in sensitivities and specificities of 90%–91% and 62%–85%, respectively, when compared with radionuclide scanning or coronary angiography (9,10). The most commonly used curves provide information on the signal intensity upslope, peak signal intensity, and time to peak signal intensity.

It is not completely clear if any of these variables alone can aid in the identification and discrimination of segments perfused by coronary arteries with different degrees of obstruction at coronary angiography. Moreover, quantitative analyses of myocardial first-pass perfusion can demonstrate changes in microcirculation (11,12). The purpose of our study, therefore, was to prospectively determine the feasibility of using MR imaging to distinguish between myocardial segments in patients with CAD of different degrees of obstruction and those in patients with normal-appearing coronary arteries.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 References
 
Patient Population
From January 2003 to February 2004, 40 consecutive ambulatory patients with clinical indications for diagnostic coronary angiography were selected for this study. Three patients did not complete the MR examination owing to claustrophobia and were therefore excluded. This resulted in a total of 37 patients (29 men, eight women; mean age, 57.2 years ± 10.5 [standard deviation]). All patients had risk factors for CAD and either positive exercise test results or a typical clinical history of exertional angina and no previous myocardial infarction. The myocardial infarction exclusion was assessed by interviewing the patient, performing rest electrocardiography, and determining the absence of delayed enhancement on subsequent MR images. Other exclusion criteria involved the diagnosis of unstable angina, symptomatic valve disease, atrial fibrillation, history of heart failure, the presence of an implanted defibrillator or pacemaker, and other contraindications to MR imaging. All patients were instructed to withhold the ingestion of foods or drinks containing caffeine 36 hours prior to the examination. No antiischemic medication was changed or withheld before the examination. The study was approved by the institutional ethics committee, and all patients provided written informed consent.

MR Imaging Protocol
All images were obtained with a 1.5-T unit (Signa Horizon CVi; General Electric, Milwaukee, Wis) that used a four-element phased-array coil. Patients were examined in the supine position, and heart rate, electrocardiographic results, and respiratory rate were monitored. Arterial pressure was also assessed during the examination by using an intermittent noninvasive device (Omega 1400; In Vivo Research Laboratories, Jacksonville, Fla).

An 18-gauge cannula was inserted into an antecubital vein and connected to a power injector pump (Spectris MR Injector; MedRad, Pittsburgh, Pa). Another 20-gauge cannula was then inserted and connected to the pump for the administration of the stress agent dipyridamole (Persantin; Farmacia USP, Sao Paulo, Brazil).

Localizer images were obtained after the patient was positioned on the examination table. Cine short-axis MR images were obtained, and these images were used to plan the subsequent perfusion protocol. Six to eight sections were acquired every two heartbeats and were selected by the same physician (L.F.R.d.A., with 7 years of experience in cardiac MR imaging) for all patients and for perfusion images in order to cover the entire left ventricle. An electrocardiographically triggered enhanced fast gradient echo train pulse sequence (6.9/1.8 [repetition time msec/echo time msec]; flip angle, 10°; matrix, 160 x 160; field of view, 320–360 mm; section thickness, 8 mm; one signal acquired; and echo train length of four) was used.

Dipyridamole was first infused at a dose of 0.56 mg per kilogram body weight for 4 minutes. Six minutes after the start of infusion—that is, at the maximum effect of dipyridamole—a bolus of 0.05 mmol/kg (0.1 mL/kg) gadopentetate dimeglumine (Magnevist; Schering, Berlin, Germany) was infused at an injection rate of 5 mL/sec and was followed by a bolus of 20 mL of normal saline, which was administered by using a power injector. Throughout the acquisition of the 50–70 images, patients held their breath during end expiration for as long as possible. Afterward, aminophylline (Farmacia USP) was infused at a dose of 3 mg per kilogram body weight to reverse the effects of dipyridamole. Twenty minutes after the end of this infusion, rest perfusion images were obtained by using the same MR imaging parameters and gadopentetate dimeglumine doses that were mentioned previously.

Coronary Angiography and Image Interpretation
Patients underwent diagnostic coronary angiography up to 15 days before or after MR imaging. No interventions were performed before MR imaging. Cineangiograms were evaluated off line in two different planes by the consensus of two experienced physicians with 15 and 9 years of experience in coronary angiography interpretation, respectively. Physicians were blinded to the results of perfusion examinations. In all patients, the presence, site, and degree of stenosis were recorded for the left main artery, the left anterior descending artery, the left circumflex artery, and the right coronary artery or one of its major branches. The degree of the stenosis, which was expressed as a percentage, was analyzed visually with reference to the proximal normal vessel segment; significant stenoses were defined as those that measured more than 50% of the vessel diameter.

MR Image Interpretation
MR images were processed by using a computer software program (MASS Plus; Leiden University, Leiden, the Netherlands) and were analyzed in consensus by two different readers (L.F.R.d.A. and J.P.F., with 7 and 8 years of experience, respectively) who were blinded to the angiographic results. Images were sequentially displayed and were grouped according to each of the first 30 cardiac phases (representing the contrast-enhanced first pass) of the 50–70 images acquired. Semiautomatic tracing was performed for all 30 images, and manual correction was applied.

Six sections were analyzed that covered the left ventricle from base to apex. Each section was divided into eight myocardial sectors, and one additional sector was recorded in the center of the left ventricle to serve as a reference for the upslope of signal intensity that was not affected by the myocardium. At the time of this study, the assignment of segments to coronary arteries was performed according to the published guidelines and was available through the software used (13). More recent guidelines (14) have updated this model, but these updates were not available at the time of our study. Segments were assigned to each respective coronary lesion in either the main artery or one of its side branches. A total of 1776 images (ie, 37 patients, eight segments per section, and six sections acquired) were used as data points at rest, and the same number of images was used as data points at hyperemia.

Myocardial segments were divided into five different groups. Group A consisted of segments in patients with no obstruction seen at coronary angiography. Group B consisted of segments that corresponded to territories of unobstructed arteries in patients with lesions that demonstrated any degree of stenosis at coronary angiography. Group C consisted of segments that corresponded to territories of arteries that contained lesions and demonstrated a reduction in luminal diameter of 40% or less. Group D consisted of segments that corresponded to territories irrigated by arteries that contained lesions and demonstrated a reduction in luminal diameter of 41%–70%. Group E consisted of segments that corresponded to territories irrigated by arteries that contained lesions and demonstrated a reduction in luminal diameter of more than 70%. These cutoff points were chosen to make the classification of the degree of obstruction more accurate by means of visual analysis of minimum, intermediate, or severe stenosis.

For each myocardial segment, the region of interest provided the mean signal intensity and resulted in a signal intensity–time curve. The region of interest for each segment was placed in consensus by the two readers who interpreted the MR data and was defined according to the myocardial distance, which ranged from 10% of the endocardial contour to 20% of the previously traced epicardial contour. From this generated curve, three variables were defined—that is, signal intensity upslope, which was calculated on the basis of three derivatives of the ascending signal intensity curve; peak signal intensity, which was defined as the maximum value of signal intensity in a determined sector; and time to peak signal intensity, which was defined as the difference in the time from the peak signal intensity and the minimum signal intensity–time point before the initial upslope of the curve.

To make the comparisons between segments and different patients more accurate, peak signal intensity and signal intensity upslope values were normalized for each patient according to the baseline values obtained from the first image acquired before contrast material administration and the upslope of the signal intensity in the left ventricle cavity. This would minimize any inhomogeneities created by the position of the surface coils and account for the differences in relative plasma volumes between patients. Finally, we also calculated the hyperemia-to-rest (HR) ratios for each of the previously mentioned variables (ie, signal intensity upslope, peak signal intensity, and time to peak signal intensity) to estimate the perfusion reserve index for each segment.

Statistical Analysis
Baseline variables (ie, age, sex, risk factors, medications, serum parameters, blood pressure, and resting heart rate) were normally distributed and were compared by using an unpaired Student t test for continuous data and a Fisher exact test for categoric values. The data on signal intensity upslope, peak signal intensity, and time to peak signal intensity were not normally distributed according to the Kolmogorov-Smirnov test. Therefore, nonparametric tests were used, and the results were presented as medians and ranges. For the comparison of these results, we used a generalized linear model that was fit by using the Genmod (generalized estimating equations) repeated measures procedure (SAS 8.02; SAS Institute, Cary, NC). This was done to account for possible clustering and interactions between the interdependent sections and segments. All of the reported probability values were two tailed, and a P value of less than .05 was considered to indicate a statistically significant difference. Tests were performed by using two statistical software packages (Statview 5.0 and SAS 8.02; SAS Institute).


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 References
 
Baseline Patient Characteristics
Baseline patient characteristics are shown in Table 1. We compared the baseline characteristics of patients with normal coronary arteries with those of patients with any obstructive coronary lesions but found no statistical differences between the two groups.


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  		Table 1. Baseline Patient Characteristics

 
Signal Intensity Upslope
At rest, there were significant differences (P < .001) in the signal intensity upslope between the five groups of segments (Figure, Table 2). When each group was compared individually, we found significant differences for segments in group A compared with those in all other groups (P < .001 for each comparison). There was a significant (17.19%) reduction in signal intensity upslope for segments in group A (median, 18.91; range, 2.80–88.03) versus those in group B (median, 15.66; range, 2.46–39.05) (P < .001). No differences were observed at rest within the other groups when directly compared with each other.



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  		Graph demonstrates signal intensity upslope for segments in each group. Segments were analyzed at rest (gray bars) and after infusion of dipyridamole (black bars). For all groups, a significant increase in signal intensity upslope was noted between rest and dipyridamole infusion (P < .001). Segments in group A had a higher signal intensity upslope than those in all other groups during rest or hyperemia (P < .001). Bars represent the median, and error bars represent the standard error of the median. au = Arbitrary units.

 

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  		Table 2. Signal Intensity Upslope during Rest and Hyperemia

 
After dipyridamole infusion, all groups showed an expected and significant increase in signal intensity upslope (P < .001), with the greater increase observed in group A (P = .04). As noted, there was a progressive decrease in signal intensity upslope for each of the five groups at rest (P < .001). Again, the signal intensity upslope for segments in group A was significantly higher than that for segments in all other groups (P < .001 for each comparison). The difference between group A and group B at hyperemia (26.11%) was significantly increased compared with the difference between group A and group B at rest (17.19%) (P = .04). A significant difference that was not found at rest was observed between segments in group E and those in groups B, C, and D (P = .01, P = .01, and P = .03, respectively) after dipyridamole infusion.

Peak Signal Intensity
When analyzing the values obtained for peak signal intensity (Table 3), we observed a significant decrease both at rest and after dipyridamole administration when comparing all groups (P < .001). Unlike the findings observed for signal intensity upslope, however, no significant differences were found at rest between segments in group A (median, 1.87; range, 1.10–3.99) and those in all other groups except group E, which demonstrated considerable lumen obstruction (median, 1.67; range, 1.16–2.19) (P < .001). After the infusion of dipyridamole, a significant increase in peak signal intensity was observed in all groups (P < .001 for each group). After dipyridamole infusion, it was possible to individually discriminate segments in group E from those in all other groups (P < .01 for each group). In this situation, however, peak signal intensity could not be used to differentiate between segments in group A (median, 2.63; range, 1.40–7.67) and normal-appearing segments in group B (median, 2.53; range, 1.37–4.71) (P = .6).


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  		Table 3. Peak Signal Intensity during Rest and Hyperemia

 
Time to Peak Signal Intensity
At rest, no significant trend was observed in time to peak signal intensity when comparing all groups of segments. After dipyridamole infusion, a significant decrease in time to peak signal intensity was seen only for segments in patients with normal coronary arteries (median, 896 msec; range, 336–2016 msec) versus those in patients with CAD (median, 784; range, 448–1680 msec) (P = .02). In this situation, a significant difference in time to peak signal intensity was observed among the five groups (P < .001), and patients in group A (time to peak signal intensity, 784 msec) could be discriminated from those in groups B, C, D, and E (time to peak signal intensity, 1008, 1008, 1120, and 1120 msec, respectively) (P < .001 for all comparisons).

HR Ratios
Data for HR ratios are shown in Table 4. The HR ratio for signal intensity upslope was significantly different in all groups (P < .001), but no differences were found regarding the HR ratio for either peak signal intensity or time to peak signal intensity. The HR ratio for signal intensity upslope for segments in group A was significantly higher than that for segments in all other groups (P < .001 for each group). The HR ratio for time to peak signal intensity was also significantly different in group A compared with all other groups (P < .001 for each difference).


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  		Table 4. HR Ratios for Signal Intensity Upslope, Peak Signal Intensity, and Time to Peak Signal Intensity

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
References
 
Findings from this study demonstrate that first-pass myocardial perfusion MR imaging can, in a stepwise fashion, demonstrate myocardial ischemia in segments with different degrees of coronary obstruction. The detection of myocardial ischemia was more pronounced with analysis of the signal intensity upslope curve than with peak signal intensity, time to peak signal intensity, and HR ratios. To the best of our knowledge, these results are the first to show that this technique can demonstrate impaired perfusion in segments that are supplied by normal-appearing epicardial arteries in patients with obstructive lesions in other coronary territories.

Reduced Perfusion in Normal-appearing Segments
Reduced perfusion in normal-appearing segments may be explained by several possibilities. One reason might be the involvement of coronary microcirculation. Microcirculatory impairment, as well as endothelial dysfunction, could already be affecting coronary circulation in patients who have CAD, even if no visible obstruction is seen in the conduit epicardial arteries at coronary angiography (15–17). Dipyridamole acts in the microcirculation by increasing intracellular levels of adenosine, thereby provoking potent vasodilation by reducing resistance in these small arteries (18,19). This vasodilation, which is a well-known mechanism of dipyridamole, leads to increased perfusion in normal segments and reduced perfusion in segments with impaired blood flow (20). When microcirculation is deficient, a decrease in blood flow is seen, and the arrival of the MR contrast material is slower, thereby explaining the reduction in perfusion that is observed. On the other hand, perfusion is increased in areas with normal microcirculation where hyperemia is induced, thereby leading to a rapid distribution of contrast material.

While this effect is well known and is enhanced in patients with obstructed epicardial arteries, the vasodilation mechanism appears to be impaired even for normal-appearing regions in patients with CAD and can be detected at MR imaging. Other mechanisms (eg, parts of the segment being supplied by obstructed arteries or collateral flow from normal coronary arteries creating diminished flow in the original segment) might also be involved in the explanation of our findings and need further investigation.

Nevertheless, the capability of MR imaging to facilitate the identification of these regions has also been seen in patients with syndrome X, which is characterized by typical angina, abnormal exercise test results, and no angiographic coronary lesions (12), as well as in patients with impaired reperfusion after successful primary angioplasty (11). Our data show that regions with normal coronary arteries already have impaired perfusion and might contribute to the overall symptoms of ischemia or reduced myocardial function in these patients. Similar findings have also been reported in previous studies on positron emission tomography (PET) that compared individuals who had normal segments with those who had CAD (21,22), as well as in studies that evaluated remote regions (23) or coronary flow in relatives of patients with CAD (24).

The results of all studies on PET show that stress perfusion in patients without CAD is significantly different from that in patients with normal-appearing coronary arteries. Our data are in accordance with those obtained in previous studies on PET because we also observed the same differences during hyperemia for all three variables reported. No study results on PET, however, demonstrated a decrease in myocardial perfusion at rest in patients who had CAD and unimportant lesions. This apparent discrepancy might be explained by the fact that, while PET measures only the final absolute values of perfusion, perfusion at MR imaging can be used to assess dynamic changes in signal intensity for a determined area of the myocardium, which is reproduced by means of the signal intensity upslope curve. In fact, our results support this by showing a decrease in signal intensity upslope only when group B was compared with group A. This finding was not reproduced during the analysis of the peak signal intensity curve, which suggests that blood flow in regions that are irrigated by normal coronary arteries in patients with CAD reaches absolute values that are similar to those found in normal individuals but may differ in the slope of the curve needed to achieve the same level of perfusion.

Aside from this possible explanation, known limitations of PET that are not found in MR imaging might also limit the possibility of using PET to demonstrate changes in perfusion at rest. Limitations of PET include the lower transmural resolution, which is needed to compare relative differences among the segments of myocardium, and the large range of normal values that are found in different studies and populations (25,26).

Finally, the finding of rest perfusion defects at PET is associated with myocardium hibernation, a characteristic that was not found in our patients. The degree of reduction in myocardial perfusion that is needed to initiate and maintain a state of hibernation is not well established (25), and the reduction in myocardial perfusion that was seen in our study might be below a theoretic threshold that would cause such a state. These small reductions could be missed at PET and may be identifiable only by examining the signal intensity upslope at MR imaging. Our data, however, do not allow a firm conclusion in this respect. Nevertheless, we believe our data suggest that MR imaging can be especially useful in the identification of patients who have impaired microcirculation but no apparent lesions at coronary angiography.

Signal Intensity Curves
The variable that was found to be the most useful in distinguishing different groups of segments was signal intensity upslope. This finding was in accordance with that of previous reports by other authors (27–31). Signal intensity upslope allowed for the discrimination of patients without CAD from those in all other groups both at rest and at hyperemia. The use of signal intensity upslope might therefore permit the identification of patients who have a low risk for CAD or who have alterations in endothelial function. Signal intensity upslope could also be used to differentiate segments with clinically important lesions (ie, those with more than 70% reduction in luminal diameter) from all other segments at hyperemia by demonstrating areas of the myocardium that could be suitable for revascularization (32).

While signal intensity upslope helped distinguish each segment group studied, the other two variables did not show such robust results. This limitation of peak signal intensity has been previously demonstrated (28) and led us to investigate whether the time to peak signal intensity was altered in each of the groups. Despite the suggestive theoretic background for this hypothesis, the time to peak signal intensity did not add further information to that which was already provided by signal intensity upslope and peak signal intensity.

Finally, we looked at the HR ratio for all three variables mentioned. This ratio was based on the known concept of coronary flow reserve and was used to determine a perfusion reserve index for each segment (33–35). In theory, this index would be superior to any of the variables alone because it permits the correction of any signal intensity differences within the images. While the numbers obtained with signal intensity upslope showed a reduction in the HR ratio that correlated with an increase in the degree of coronary obstruction, the magnitude of this increase was below the expected physiologic response of an increase in coronary flow, which was up to four times that of basal flow observed during induced hyperemia (36). The ratio of peak signal intensity to time to peak signal intensity was also not consistent with this expected increase in blood flow and did not permit the identification of patients or normal individuals.

Clinical Implications
The ability to detect segments in the myocardium that have impaired perfusion but contain normal coronary arteries may demonstrate the potential clinical importance of identifying patients with increased risk of cardiovascular disease who would otherwise be considered at lower risk. Because atherosclerotic disease is multifocal (37) and because of the effects of different vascular beds (38), the finding of altered microcirculation in other regions of the heart besides those that contain obstructive lesions can have an effect on the overall risk in these patients. While the identification of these areas does not serve as a tool to recognize patients with subclinical atherosclerosis (because such patients may have lesions in other territories), the findings presented here could be used to further stratify patients, with possibly substantial changes in the way such patients are cared for (39). These patients might receive more intensive risk factor modification (40,41) or be candidates for investigation with other imaging modalities to further identify patients with endothelial dysfunction or high-risk, vulnerable plaques (42).

The finding that MR imaging could be used to identify patients who have a substantial degree of obstruction (>70% stenosis) also has potential clinical implications and indicates that these patients might be suitable for earlier or more aggressive invasive imaging and intervention. To sum up, the results of this study might offer further possibilities through MR imaging for expanding the diagnosis and possibly the treatment of patients with CAD, thereby shortening the path from imaging to screening (43).

Limitations of the Study
One of the major limitations of our study was not being able to compare our findings directly with those of other studies that assessed myocardial perfusion, especially with quantitative PET. However, this comparison has been performed before (32) and has demonstrated a strong correlation between the two methods. Such comparisons have also verified the robustness of MR imaging both in measuring myocardial perfusion and in demonstrating defects in the microcirculation. We believe that the previously published data on MR imaging may validate the results presented in our current study.

Regarding the MR imaging methods, we used 8-mm sections; thinner sections might have helped to better distinguish perfusion deficits and may have altered part of the results. Nevertheless, owing to the great number of segments analyzed, we believe that this effect was small and therefore would not have altered our final conclusions. Moreover, the trade-off of reduced signal-to-noise ratio, which is created by the use of thinner sections, might have negatively affected the interpretation of signal intensity curves. We also did not assess our data for the presence and extension of collateral flow on coronary angiograms. The presence of collateral flow from unobstructed arteries to territories of impaired epicardial flow has an effect on the regional myocardial perfusion to that segment (44). Thus, patients with considerable collateral flow might have increased myocardial perfusion in segments with obstructed coronary arteries. It would be difficult to estimate the effect of these observations on the data presented, but on the basis of results obtained by other investigators, we believe that this would not pose a considerable problem to the interpretation of the study (45) and would in fact enhance the data presented because myocardial signal intensity curves would be higher owing to the presence of collateral vessels with areas of reduced perfusion.

Finally, the use of every other heartbeat for the acquisition of perfusion images might have affected the results by generating lower curve upslopes (46); this may explain the difficulty in discriminating between the different degrees of coronary obstructions among the groups with established CAD. This does not, however, interfere with the main findings of our study regarding the distinction of normal segments in patients without CAD or the identification of segments supplied by coronary arteries with clinically important lesions.

In conclusion, our findings demonstrate that first-pass perfusion MR imaging is a valuable tool for the assessment of myocardium perfusion. The quantitative assessment of the signal intensity–time curve is feasible and can be used to differentiate segments with significant obstructions from all other segments. Most importantly, first-pass perfusion MR imaging can be used to distinguish segments in normal individuals from those in normal-appearing areas of the myocardium in patients with diffuse CAD. The identification of these regions at MR imaging might have an effect on risk stratification and treatment strategies for these patients and could be assessed in larger clinical studies.


   FOOTNOTES
 

Abbreviations: CAD = coronary artery disease • HR = hyperemia to rest

Authors stated no financial relationship to disclose.

Author contributions: Guarantors of integrity of entire study, L.F.R.d.A., J.L.F., C.E.R.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; literature research, J.L.F.; clinical studies, L.F.R.d.A., C.E.R.; statistical analysis, L.F.R.d.A., J.L.F., C.E.R.; and manuscript editing, L.F.R.d.A., J.L.F.


   References
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

  1. American Heart Association. 1999 heart and stroke statistical update. Dallas, Tex: American Heart Association, 1999.
  2. Gibbons RJ, Abrams J, Chatterjee K, et al. ACC/AHA 2002 guideline update for the management of patients with chronic stable angina: summary article—a report of the American College of Cardiology/American Heart Association Task Force on practice guidelines (Committee on the Management of Patients With Chronic Stable Angina). J Am Coll Cardiol 2003;41:159–168.[Free Full Text]
  3. Marcus ML, Harrison DG, White CW, McPherson DD, Wilson RF, Kerber RE. Assessing the physiologic significance of coronary obstructions in patients: importance of diffuse undetected atherosclerosis. Prog Cardiovasc Dis 1988;31:39–56.[CrossRef][Medline]
  4. Litchfield RL. Noninvasive tests for cardiac risk stratification: which ones are most prognostic? Postgrad Med 2004;115:30–36.[Medline]
  5. Chiu CW, So NM, Lam WW, Chan KY, Sanderson JE. Combined first-pass perfusion and viability study at MR imaging in patients with non-ST segment-elevation acute coronary syndromes: feasibility study. Radiology 2003;226:717–722.[Abstract/Free Full Text]
  6. Viswamitra S, Higgins CB, Meacham DF, Mehta JL. Magnetic resonance imaging in myocardial ischemia. Curr Opin Cardiol 2004;19:510–516.[CrossRef][Medline]
  7. Atkinson DJ, Burstein D, Edelman RR. First-pass cardiac perfusion: evaluation with ultrafast MR imaging. Radiology 1990;174:757–762.[Abstract/Free Full Text]
  8. Jerosch-Herold M, Wilke N. MR first pass imaging: quantitative assessment of transmural perfusion and collateral flow. Int J Card Imaging 1997;13:205–218.[CrossRef][Medline]
  9. Ishida N, Sakuma H, Motoyasu M, et al. Noninfarcted myocardium: correlation between dynamic first-pass contrast-enhanced myocardial MR imaging and quantitative coronary angiography. Radiology 2003;229:209–216.[Abstract/Free Full Text]
  10. Paetsch I, Jahnke C, Wahl A, et al. Comparison of dobutamine stress magnetic resonance, adenosine stress magnetic resonance, and adenosine stress magnetic resonance perfusion. Circulation 2004;110:835–842.[Abstract/Free Full Text]
  11. Taylor AJ, Al-Saadi N, Abdel-Aty H, Schulz-Menger J, Messroghli DR, Friedrich MG. Detection of acutely impaired microvascular reperfusion after infarct angioplasty with magnetic resonance imaging. Circulation 2004;109:2080–2085.[Abstract/Free Full Text]
  12. Panting JR, Gatehouse PD, Yang GZ, et al. Abnormal subendocardial perfusion in cardiac syndrome X detected by cardiovascular magnetic resonance imaging. N Engl J Med 2002;346:1948–1953.[Abstract/Free Full Text]
  13. Imaging guidelines for nuclear cardiology procedures. II. American Society of Nuclear Cardiology. J Nucl Cardiol 1999;6:G47–G84.[CrossRef][Medline]
  14. Hendel RC, Wackers FJ, Berman DS, et al. American Society of Nuclear Cardiology consensus statement: reporting of radionuclide myocardial perfusion imaging studies. J Nucl Cardiol 2003;10:705–708.[CrossRef][Medline]
  15. L'Abbate A, Sambuceti G, Neglia D. Myocardial perfusion and coronary microcirculation: from pathophysiology to clinical application. J Nucl Cardiol 2002;9:328–337.[CrossRef][Medline]
  16. Bonetti PO, Lerman LO, Lerman A. Endothelial dysfunction: a marker of atherosclerotic risk. Arterioscler Thromb Vasc Biol 2003;23:168–175.[Abstract/Free Full Text]
  17. De Bruyne B, Hersbach F, Pijls NH, et al. Abnormal epicardial coronary resistance in patients with diffuse atherosclerosis but "normal" coronary angiography. Circulation 2001;104:2401–2406.[Abstract/Free Full Text]
  18. Gould KL. Noninvasive assessment of coronary stenoses by myocardial perfusion imaging during pharmacologic coronary vasodilatation. I. Physiologic basis and experimental validation. Am J Cardiol 1978;41:267–278.
  19. Johnston DL, Daley JR, Hodge DO, Hopfenspirger MR, Gibbons RJ. Hemodynamic responses and adverse effects associated with adenosine and dipyridamole pharmacologic stress testing: a comparison in 2,000 patients. Mayo Clin Proc 1995;70:331–336.[Medline]
  20. Gould KL, Kirkeeide RL, Buchi M. Coronary flow reserve as a physiologic measure of stenosis severity. J Am Coll Cardiol 1990;15:459–474.[Abstract]
  21. Muzik O, Duvernoy C, Beanlands RS, et al. Assessment of diagnostic performance of quantitative flow measurements in normal subjects and patients with angiographically documented coronary artery disease by means of nitrogen-13 ammonia and positron emission tomography. J Am Coll Cardiol 1998;31:534–540.[Abstract/Free Full Text]
  22. Dayanikli F, Grambow D, Muzik O, Mosca L, Rubenfire M, Schwaiger M. Early detection of abnormal coronary flow reserve in asymptomatic men at high risk for coronary artery disease using positron emission tomography. Circulation 1994;90:808–817.[Abstract/Free Full Text]
  23. Yoshinaga K, Katoh C, Noriyasu K, et al. Reduction of coronary flow reserve in areas with and without ischemia on stress perfusion imaging in patients with coronary artery disease: a study using oxygen 15-labeled water PET. J Nucl Cardiol 2003;10:275–283.[CrossRef][Medline]
  24. Sdringola S, Patel D, Gould KL. High prevalence of myocardial perfusion abnormalities on positron emission tomography in asymptomatic persons with a parent or sibling with coronary artery disease. Circulation 2001;103:496–501.[Abstract/Free Full Text]
  25. Heusch G. Hibernating myocardium. Physiol Rev 1998;78:1055–1085.[Abstract/Free Full Text]
  26. Rahimtoola SH. Hibernating myocardium has reduced blood flow at rest that increases with low-dose dobutamine. Circulation 1996;94:3055–3061.[Free Full Text]
  27. Walsh EG, Doyle M, Lawson MA, Blackwell GG, Pohost GM. Multislice first-pass myocardial perfusion imaging on a conventional clinical scanner. Magn Reson Med 1995;34:39–47.[Medline]
  28. de Roos A, Niezen RA, Lamb HJ, Dendale P, Reiber JH, van der Wall EE. MR of the heart under pharmacologic stress. Cardiol Clin 1998;16:247–265.[CrossRef][Medline]
  29. van Rugge FP, van der Wall EE, Bruschke AV. New developments in pharmacologic stress imaging. Am Heart J 1992;124:468–485.[CrossRef][Medline]
  30. Sensky PR, Jivan A, Hudson NM, et al. Coronary artery disease: combined stress MR imaging protocol—one-stop evaluation of myocardial perfusion and function. Radiology 2000;215:608–614.[Abstract/Free Full Text]
  31. Wilke N, Jerosch-Herold M, Wang Y, et al. Myocardial perfusion reserve: assessment with multisection, quantitative, first-pass MR imaging. Radiology 1997;204:373–384.[Abstract/Free Full Text]
  32. Schwitter J, Nanz D, Kneifel S, et al. Assessment of myocardial perfusion in coronary artery disease by magnetic resonance: a comparison with positron emission tomography and coronary angiography. Circulation 2001;103:2230–2235.[Abstract/Free Full Text]
  33. Hundley WG, Lange RA, Clarke GD, et al. Assessment of coronary arterial flow and flow reserve in humans with magnetic resonance imaging. Circulation 1996;93:1502–1508.[Abstract/Free Full Text]
  34. Al-Saadi N, Nagel E, Gross M, et al. Noninvasive detection of myocardial ischemia from perfusion reserve based on cardiovascular magnetic resonance. Circulation 2000;101:1379–1383.[Abstract/Free Full Text]
  35. Cullen JH, Horsfield MA, Reek CR, Cherryman GR, Barnett DB, Samani NJ. A myocardial perfusion reserve index in humans using first-pass contrast-enhanced magnetic resonance imaging. J Am Coll Cardiol 1999;33:1386–1394.[Abstract/Free Full Text]
  36. Uren NG, Melin JA, De Bruyne B, Wijns W, Baudhuin T, Camici PG. Relation between myocardial blood flow and the severity of coronary-artery stenosis. N Engl J Med 1994;330:1782–1788.[Abstract/Free Full Text]
  37. Schoenhagen P, Tuzcu EM, Ellis SG. Plaque vulnerability, plaque rupture, and acute coronary syndromes: (multi)-focal manifestation of a systemic disease process. Circulation 2002;106:760–762.[Free Full Text]
  38. Sutton-Tyrrell K, Kuller LH, Matthews KA, et al. Subclinical atherosclerosis in multiple vascular beds: an index of atherosclerotic burden evaluated in postmenopausal women. Atherosclerosis 2002;160:407–416.[CrossRef][Medline]
  39. Fowkes FG, Price JF, Leng GC. Targeting subclinical atherosclerosis. BMJ 1998;316:1764.[Free Full Text]
  40. O'Keefe JH Jr, Cordain L, Harris WH, Moe RM, Vogel R. Optimal low-density lipoprotein is 50 to 70 mg/dL: lower is better and physiologically normal. J Am Coll Cardiol 2004;43:2142–2146.[Abstract/Free Full Text]
  41. Khaleeli E, Peters SR, Bobrowsky K, Oudiz RJ, Ko JY, Budoff MJ. Diabetes and the associated incidence of subclinical atherosclerosis and coronary artery disease: implications for management. Am Heart J 2001;141:637–644.[CrossRef][Medline]
  42. Olin JW, Kaufman JA, Bluemke DA, et al. Atherosclerotic vascular disease conference: Writing Group IV—imaging. Circulation 2004;109:2626–2633.[Free Full Text]
  43. Shaw LJ, Redberg RF. From clinical trials to public health policy: the path from imaging to screening. Am J Cardiol 2001;88:62E–65E.[Medline]
  44. Koerselman J, van der Graaf Y, de Jaegere PP, Grobbee DE. Coronary collaterals: an important and underexposed aspect of coronary artery disease. Circulation 2003;107:2507–2511.[Free Full Text]
  45. Sensky PR, Samani NJ, Reek C, Cherryman GR. Magnetic resonance perfusion imaging in patients with coronary artery disease: a qualitative approach. Int J Cardiovasc Imaging 2002;18:373–383.[CrossRef][Medline]
  46. Thiele H, Plein S, Ridgway JP, et al. Effects of missing dynamic images on myocardial perfusion reserve index calculation: comparison between an every heartbeat and an alternate heartbeat acquisition. J Cardiovasc Magn Reson 2003;5:343–352.[CrossRef][Medline]




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