CLINICAL RESEARCH: CARDIAC IMAGING
Diagnostic Performance of Stress Cardiac Magnetic Resonance Imaging in the Detection of Coronary Artery DiseaseA Meta-Analysis
Kiran R. Nandalur, MD*,*,
Ben A. Dwamena, MD* ,
Asim F. Choudhri, MD ,
Mohan R. Nandalur, MD and
Ruth C. Carlos, MD, MS*
* Department of Radiology, University of Michigan Health System, Ann Arbor, Michigan
Department of Nuclear Medicine, Veterans Affairs, Ann Arbor Health Care System, Ann Arbor, Michigan
Department of Radiology, University of Virginia Health System, Charlottesville, Virginia
Department of Cardiovascular Medicine, Georgetown University/Washington Hospital Center, Washington, DC
Manuscript received February 23, 2007;
revised manuscript received May 9, 2007,
accepted June 25, 2007.
* Reprint requests and correspondence: Dr. Kiran Nandalur, William Beaumont Hospital, 3601 W. Thirteen Mile Road, Royal Oak, Michigan 48073. (Email: nandalurk{at}yahoo.com).
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Abstract
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Objectives: The purpose of our study was to conduct an evidence-based evaluation of stress cardiac magnetic resonance imaging (MRI) in the diagnosis of coronary artery disease (CAD).
Background: Stress cardiac MRI has recently emerged as a noninvasive method in the detection of CAD, with 2 main techniques in use: 1) perfusion imaging; and 2) stress-induced wall motion abnormalities imaging.
Methods: We examined studies from January 1990 to January 2007 using MEDLINE and EMBASE. A study was included if it: 1) used stress MRI as a diagnostic test for CAD ( 50% diameter stenosis); and 2) used catheter X-ray angiography as the reference standard.
Results: Thirty-seven studies (2,191 patients) met the inclusion criteria, with 14 datasets (754 patients) using stress-induced wall motion abnormalities imaging and 24 datasets (1,516 patients) using perfusion imaging. Stress-induced wall motion abnormalities imaging demonstrated a sensitivity of 0.83 (95% confidence interval [CI] 0.79 to 0.88) and specificity of 0.86 (95% CI 0.81 to 0.91) on a patient level (disease prevalence = 70.5%). Perfusion imaging demonstrated a sensitivity of 0.91 (95% CI 0.88 to 0.94) and specificity of 0.81 (95% CI 0.77 to 0.85) on a patient level (disease prevalence = 57.4%).
Conclusions: In studies with high disease prevalence, stress cardiac MRI, using either technique, demonstrates overall good sensitivity and specificity for the diagnosis of CAD. However, limited data are available regarding use of either technique in populations with low disease prevalence.
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Abbreviations and Acronyms
| | CAD = coronary artery disease | | LR = likelihood ratio | | MRI = magnetic resonance imaging | | SPECT = single-photon emission tomography |
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Noninvasive imaging for the evaluation of coronary artery disease (CAD) is currently largely performed by: 1) anatomical imaging, such as coronary multidetector computed tomography or coronary magnetic resonance angiography, which directly visualize the arteries; or 2) functional imaging, such as single-photon emission tomography (SPECT) or echocardiography, which evaluate the hemodynamic sequelae of coronary obstructive disease.
While the accuracy of these respective modalities has been assessed extensively, the diagnostic capabilities of stress cardiac magnetic resonance imaging (MRI), which appears promising given its excellent depiction of wall motion, high contrast, spatial resolution, and lack of ionizing radiation, has only been examined by studies of limited sample size. This has led to studies with wide confidence intervals (CIs) for sensitivity and specificity and potentially unreliable estimates of performance. Moreover, stress cardiac MRI is performed with 2 very different techniques: 1) dynamic first-pass perfusion imaging, which assesses for inducible perfusion defects, indicating impaired perfusion reserve; and 2) stress-induced wall motion abnormalities imaging, which evaluates for impairment of regional endocardial excursion and myocardial thickening, also indicating underlying ischemia. To overcome these issues and to provide an evidence-based evaluation of the clinical utility of stress MRI, we performed a comprehensive meta-analysis of all currently published studies comparing stress MRI with catheter-based X-ray angiography in the diagnosis of CAD.
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Methods
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Data sources and searches..
We searched MEDLINE and EMBASE for English and non-English literature published from January 1990 to January 2007 evaluating for the presence of CAD in native or non-native coronary arteries by stress MRI and catheter-based X-ray angiography in the same patients. The search included medical subject headings for magnetic resonance, perfusion, wall motion, and coronary angiography with the exploded term "coronary artery disease." Moreover, we evaluated bibliographies of retrieved articles, review articles, and textbooks. The retrieved studies were examined for potentially duplicate or overlapping data. Corresponding investigators were contacted for clarification when data were unclear or inadequate. Meeting abstracts provide insufficient information regarding their data, lack finality regarding the results, and were excluded.
Study selection.
We included a study if: 1) it used stress MRI as a diagnostic test for obstructive CAD, with 50% diameter stenosis selected as the threshold for significant CAD, using catheter-based X-ray angiography as the reference standard; and 2) reported cases in absolute numbers of true positive, false positive, true negative, and false negative results or stated data adequate to derive this information. Studies were eligible regardless of whether they were referred for suspected or known CAD and regardless of technique used for stress MRI. Studies were excluded if: 1) performed in phantom-only models; 2) animals; 3) normal healthy volunteers without catheter-based X-ray angiography correlation; or 4) included <10 patients.
Data extraction and quality assessment.
Two independent investigators performed data extraction. Inconsistencies were resolved by discussion and consensus. Data were recorded, as available, at the coronary territory level (left anterior descending, left circumflex, and right coronary arteries) and patient level. Study quality and applicability were assessed by a modified checklist based on the Quality Assessment Tool for Diagnostic Accuracy guidelines by 2 independent investigators, with discrepancies solved by consensus (1).
Data synthesis and statistical analysis.
Categorical variables from studies are presented as percentages and continuous variables as mean values. The main analysis was performed at the patient level, as most studies provided this level of information. Secondary analyses were performed at the coronary territory level. We applied the bivariate mixed-effects regression model for treatment trial meta-analysis and modified for synthesis of diagnostic test data assuming binomial errors distribution for sensitivity and specificity (2,3). Between-study variability was assessed assuming correlated normally distributed random effects for logit (sensitivity) and logit (specificity) with the degree of correlation between studies predictive of an implicit threshold effect. We derived summary sensitivity and specificity as functions of the estimated model parameters with associated 95% CIs.
Estimate of clinical utility.
The positive likelihood ratio (LR+) measures the likelihood that a positive (abnormal) stress MRI would be expected in a patient with CAD, whereas the negative LR (LR–) measures the likelihood that a negative (normal) stress MRI would be expected in a patient without CAD. As a measure of test performance, the LR has advantages over sensitivity and specificity as it changes with disease prevalence and can be used to calculate post-test probability. Positive likelihood ratio and LR– are defined with the following formulas: LR+ = sensitivity/(1 – specificity) and LR– = (1 – sensitivity)/specificity.
We examined clinical utility of each method by means of Bayes' theorem, where pretest probability = prevalence of disease and post-test probability = LR x pretest probability/[(1 – pretest probability) x (1 – LR)]. Assuming that the study samples are representative of the entire population, an estimate of the pretest probability of CAD can be calculated from the global or subgroup-specific prevalence of this disorder across the studies. The weighted mean percentage of CAD of the prevalence of CAD in patients who underwent stress MRI was used as the pretest probability. The post-test probability was evaluated by changing the pretest probability into pretest odds with the following equation: odds = probability/(1 – probability). The post-test odds were then derived by multiplying together the pretest odds and the LR. Finally, the post-test odds were converted to probabilities by utilizing the following equation: probability = odds/(odds + 1). These results are represented in a graph of conditional probabilities displaying the post-test probability of CAD, if the test was negative or positive, for a given pretest probability.
Assessment of heterogeneity.
Heterogeneity of the results between the studies was assessed graphically by forest plots and statistically using the quantity I
2 that describes the percentage of total variation across studies attributable to heterogeneity rather than chance.
Statistical analyses were performed with Stata 9.0 (Stata Corp., Chicago, Illinois).
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Results
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The literature process is summarized in Figure 1. Database searches identified 48 potentially relevant citations. Thirty-seven studies were included, with 11 being excluded because: 1) they had overlapping data; or 2) it was not possible to calculate absolute figures from the presented data. Study and population characteristics of the included studies are summarized in Table 1
(4–40).
Data on diagnostic accuracy were available for the 37 studies with a total of 2,191 patients, with 14 comparisons (754 patients) using stress-induced wall motion abnormalities imaging and 24 comparisons (1,516 patients) using perfusion imaging. One study directly compared stress-induced wall motion abnormalities imaging and perfusion imaging in the same patients (79 patients) (32). Results of the individual studies on a per-patient and per-coronary territory level are summarized in Table 2.
Patient-level summary performance estimates.
After pooling 14 datasets (1,183 patients after exclusion of 50 patients secondary to unsuccessful MRI), perfusion imaging demonstrated a sensitivity of 0.91 (95% CI 0.88 to 0.94) and specificity of 0.81 (95% CI 0.77 to 0.85), compared with catheter-based X-ray angiography (Fig. 2A). The prevalence of CAD in this group was 57.4% (679 of 1,183). After pooling 13 datasets (735 patients after exclusion of 5 patients secondary to unsuccessful MRI), stress-induced wall motion abnormalities imaging demonstrated a sensitivity of 0.83 (95% CI 0.79 to 0.88) and specificity of 0.86 (95% CI 0.81 to 0.91) for CAD at the subject level (Fig. 2B). The prevalence of CAD in this group was 70.5% (518 of 735). Overall, these summary estimates show good sensitivity and specificity for CAD at the patient level. Analysis of stress-induced wall motion abnormalities imaging with dobutamine or exercise as the stressor (excluding studies utilizing dipyridamole) demonstrates an improved sensitivity of 0.85 (95% CI 0.82 to 0.90) with a comparable specificity of 0.86 (95% CI 0.81 to 0.91).
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Figure 2 Forest Plot of Sensitivity and Specificity
(A, B) Forest plot of patient-level sensitivity and specificity of stress perfusion imaging compared with coronary angiography. (C, D) Forest plot of patient level sensitivity and specificity of stress-induced wall motion abnormalities imaging compared with coronary angiography. Solid squares = point estimate of each study (area indicates relative contribution of the study to meta-analysis); horizontal lines = 95% confidence interval (CI).
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Evaluating clinical utility, the positive LR for perfusion MRI is 5.10 (95% CI 3.92 to 6.28); the negative LR, 0.11 (95% CI 0.07 to 0.15). For stress-induced wall motion abnormalities imaging, the positive LR is 5.24 (95% CI 3.28 to 7.21); the negative LR, 0.19 (95% CI 0.15 to 0.24). Using the rule of thumb that for a diagnostic test to be useful it should have a high positive LR (>5) (i.e., good at ruling in a disease) and a low negative LR (<0.2) (i.e., good at ruling out disease), both methods are good at confirming and excluding CAD. For each test, Figure 3
shows the effect of a positive or negative result on pretest probabilities.
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Figure 3 Plot of Conditional Probabilities for PI and IWMA
Post-test probabilities are shown as a function of pretest probability for patients with positive results on perfusion imaging (PI), positive results on wall motion abnormalities imaging (IWMA), negative results on PI, and negative results on IWMA.
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Coronary territory-level summary performance estimates..
Per-coronary territory meta-analysis of perfusion imaging pooled 16 datasets with 1,911 coronary territories and demonstrated a sensitivity of 0.84 (95% CI 0.80 to 0.87) and specificity of 0.85 (95% CI 0.81 to 0.88). Per-coronary territory meta-analysis of stress-induced wall motion abnormalities imaging pooled 4 datasets with 289 coronary territories and demonstrated a sensitivity of 0.79 (95% CI 0.71 to 0.86) and specificity of 0.93 (95% CI 0.81 to 1.0), although notably limited by a small study size.
Assessment of heterogeneity.
Analysis at the patient level demonstrated moderate heterogeneity in sensitivities between perfusion imaging studies (I2 = 0.44, p = 0.04) and specificities between stress-induced wall motion abnormality studies (I2 = 0.73, p < 0.001). At the coronary territory level, heterogeneity was present for between-study specificities for both perfusion (I2 = 0.62, p < 0.001) and stress-induced wall motion abnormality studies (I2 = 0.85, p < 0.001).
Quality grading by study is shown in Table 3.
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Discussion
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Stress MRI has recently emerged as a promising alternative to nuclear SPECT and stress echocardiography in the noninvasive functional evaluation of CAD. Our study examined the diagnostic performance data of stress MRI from multiple centers throughout the world describing populations with a relatively high prevalence of disease, 57% in the perfusion imaging group and 71% in the stress-induced wall motion abnormalities imaging group. At the patient level, we found that the 2 main techniques, perfusion imaging and stress-induced wall motion abnormalities imaging, used in stress MRI demonstrated similar good specificities (perfusion imaging: 81%, stress-induced wall motion abnormalities imaging: 86%) and sensitivities (perfusion imaging: 91%, stress-induced wall motion abnormalities imaging: 83%). With the exclusion of stress-induced wall motion abnormalities studies utilizing dipyridamole, which may be a less effective stressor for this technique, and inclusion of those with dobutamine or exercise, the sensitivity of wall motion imaging was improved (85%) without a notable change in the specificity (40). At the coronary territory level, perfusion imaging and stress-induced wall motion abnormalities imaging showed overlapping sensitivities and specificities that were good overall.
The wide range of prevalence of CAD in the examined studies likely reflects institutional referral patterns and clinical thresholds for imaging patients. As post-test probability depends on disease prevalence, practical use of the results relies on cognizance of CAD prevalence at any individual medical center. In our study, the summary sensitivities and specificities for perfusion imaging and stress-induced wall motion abnormalities imaging were attained in patients selected to undergo an invasive examination, catheter-based X-ray angiography, and thus had relatively high probability of CAD. This selection bias is illustrated by the high prevalence of disease in both technique populations. Limited empiric data are available in low disease prevalence populations, except in a few studies (9,14). Moreover, similar studies in a low-risk population would be difficult to conduct given the strong prognostic value of a negative stress MRI (41).
The diagnostic capabilities of stress MRI, especially with perfusion imaging, appear comparable if not superior to SPECT and stress echocardiography. Underwood et al. (42) examined 79 studies with 8,964 patients using SPECT in the diagnosis of CAD and found a sensitivity of 86% and specificity of 74%, with the caveat that the low specificity may be partially due to referral bias. Ishida et al. (12) directly compared perfusion imaging and SPECT in 69 patients who also underwent catheter-based X-ray angiography and found a significantly greater area under the receiver-operating characteristic curve for perfusion imaging compared with that in SPECT, and Sakuma et al. (22) found superior but not statistically significant diagnostic accuracy for perfusion imaging compared with that in SPECT in 40 patients. The favorable capabilities of stress MRI, specifically perfusion imaging, are likely due to superior spatial resolution compared with that in SPECT allowing for the distinction between subendocardial and transmural defects, which is important because subendocardial perfusion defects can indicate ischemia at an early stage. Single-photon emission computed tomography also has the disadvantages of radiation exposure and attenuation artifacts. Although, notably, stress MRI, with perfusion imaging or stress-induced wall motion abnormalities imaging, can be limited by availability, claustrophobia, obesity, poor gating, and motion artifact, along with the use of pharmacologic stressors, and attaining the appropriate heart rate (for stress-induced wall motion abnormalities imaging). With regard to stress exercise echocardiography, Schuijf et al. (43) pooled 15 studies with 1,849 patients and found a weighted mean sensitivity and specificity of 84% and 82% for the detection of CAD, and a weighted mean sensitivity and specificity of 80% and 84% in 28 studies with 2,246 patients using dobutamine echocardiography. Nagel el al. (31) found stress-induced wall motion abnormalities imaging to have significantly higher sensitivity and specificity than dobutamine stress echocardiography in a study with over 170 patients.
The American College of Cardiology recently reported that stress cardiac MRI, with either technique, is indicated for detection of CAD in symptomatic patients with intermediate pretest probability, who have uninterpretable electrocardiograms or are unable to exercise (44). They also stated that stress cardiac MRI is of uncertain usefulness in symptomatic patients with a high pretest probability of CAD. From our analysis, the clinical utility of stress cardiac imaging, using either technique, is most evident when the test is negative, decreasing the probability of CAD to at or below 20% in patients with low-to-intermediate pretest probability of CAD (<60%). A positive test appears more useful with intermediate-to-high pretest probabilities (>40%), where the post-test probability of disease would exceed approximately 80%. Consequently, our analysis supports the use of stress cardiac MRI in patients with intermediate pretest probability disease, as both a positive and negative test confer a relatively acceptable post-test probability of disease, while its role in high pretest probability of disease needs to be further evaluated, given the utility of a positive test but questionable usefulness if negative.
Our study contains several limitations. Significant heterogeneity was identified in multiple performance characteristics; thus the results and potential clinical application should be interpreted with caution. Not all of the included studies provided comprehensive data on the patient and coronary territory level, although numerous investigators were contacted to provide additional data. Moreover, limited or ambiguous information was provided by many studies regarding the number of examinations that were not interpretable, which can lead to false estimates of sensitivity and specificity. Future studies should be promoted to have more rigorous reporting of results. Publication bias, favoring studies with positive results, also confounds comprehensive evaluation. Quality assessment and data abstraction were performed by independent reviewers, with disagreement settled by consensus. Consequently, quantitative agreement between the reviewers could not be examined. An anatomy-based gold standard, catheter-based angiography was utilized, which is imperfect, particularly with regard to physiological information. Stress MRI technology has been evolving since the earliest studies and newer additional techniques, such as the addition of coronary magnetic resonance angiography, double contrast bolus technique, and delayed enhancement infarction imaging, which were utilized in only a small number of studies in the current meta-analysis, may further improve the diagnostic properties of stress MRI.
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Conclusions
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Stress perfusion MRI, with either perfusion imaging or wall-motion imaging, has good sensitivity and specificity in the diagnosis of CAD, in patients with a high prevalence of disease. However, we recommend cautious clinical application of the results, given the limited data available for a low disease prevalence population.
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Acknowledgments
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The authors would like to thank Ricardo C. Cury, MD, Eike Nagel, MD, and Sven Plein, MD, PhD, for clarification or provision of data.
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References
|
|---|
1. Whiting P, Rutjes AW, Reitsma JB, Bossuyt PM, Kleijnen J. The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews BMC Med Res Methodol 2003;3:25.[CrossRef][Medline]2. Reitsma JB, Glas AS, Rutjes AW, Scholten RJ, Bossuyt PM, Zwinderman AH. Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews J Clin Epidemiol 2005;58:982-990.[CrossRef][Web of Science][Medline] 3. Swets JA. Measuring the accuracy of diagnostic systems Science 1988;240:1285-1293.[Abstract/Free Full Text] 4. 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] 5. al-Saadi N, Gross M, Paetsch I, et al. Dobutamine induced myocardial perfusion reserve index with cardiovascular MR in patients with coronary artery disease J Cardiovasc Magn Reson 2002;4:471-480.[CrossRef][Web of Science][Medline] 6. Bunce NH, Reyes E, Keegan J, et al. Combined coronary and perfusion cardiovascular magnetic resonance for the assessment of coronary artery stenosis J Cardiovasc Magn Reson 2004;6:527-539.[CrossRef][Web of Science][Medline] 7. 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] 8. Cury RC, Cattani CA, Gabure LA, et al. Diagnostic performance of stress perfusion and delayed-enhancement MR imaging in patients with coronary artery disease Radiology 2006;240:39-45.[Abstract/Free Full Text] 9. Doyle M, Fuisz A, Kortright E, et al. The impact of myocardial flow reserve on the detection of coronary artery disease by perfusion imaging methods: an NHLBI WISE study J Cardiovasc Magn Reson 2003;5:475-485.[CrossRef][Web of Science][Medline] 10. Giang TH, Nanz D, Coulden R, et al. Detection of coronary artery disease by magnetic resonance myocardial perfusion imaging with various contrast medium doses: first European multi-centre experience Eur Heart J 2004;25:1657-1665.[Abstract/Free Full Text] 11. Ibrahim T, Nekolla SG, Schreiber K, et al. Assessment of coronary flow reserve: comparison between contrast-enhanced magnetic resonance imaging and positron emission tomography J Am Coll Cardiol 2002;39:864-870.[Abstract/Free Full Text] 12. 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] 13. Kawase Y, Nishimoto M, Hato K, Okajima K, Yoshikawa J. Assessment of coronary artery disease with nicorandil stress magnetic resonance imaging Osaka City Med J 2004;50:87-94.[Medline] 14. Klem I, Heitner JF, Shah DJ, et al. Improved detection of coronary artery disease by stress perfusion cardiovascular magnetic resonance with the use of delayed enhancement infarction imaging J Am Coll Cardiol 2006;47:1630-1638.[Abstract/Free Full Text] 15. Nagel E, Klein C, Paetsch I, et al. Magnetic resonance perfusion measurements for the noninvasive detection of coronary artery disease Circulation 2003;108:432-437.[Abstract/Free Full Text] 16. Okuda S, Tanimoto A, Satoh T, et al. Evaluation of ischemic heart disease on a 1.5 Tesla scanner: combined first-pass perfusion and viability study Radiat Med 2005;23:230-235.[Medline] 17. Panting JR, Gatehouse PD, Yang GZ, et al. Echo-planar magnetic resonance myocardial perfusion imaging: parametric map analysis and comparison with thallium SPECT J Magn Reson Imaging 2001;13:192-200.[CrossRef][Web of Science][Medline] 18. Pilz G, Bernhardt P, Klos M, Ali E, Wild M, Hofling B. Clinical implication of adenosine-stress cardiac magnetic resonance imaging as potential gatekeeper prior to invasive examination in patients with AHA/ACC class II indication for coronary angiography Clin Res Cardiol 2006;95:531-538.[CrossRef][Web of Science][Medline] 19. Plein S, Greenwood JP, Ridgway JP, Cranny G, Ball SG, Sivananthan MU. Assessment of non–ST-segment elevation acute coronary syndromes with cardiac magnetic resonance imaging J Am Coll Cardiol 2004;44:2173-2181.[Abstract/Free Full Text] 20. Plein S, Radjenovic A, Ridgway JP, et al. Coronary artery disease: myocardial perfusion MR imaging with sensitivity encoding versus conventional angiography Radiology 2005;235:423-430.[Abstract/Free Full Text] 21. Rieber J, Huber A, Erhard I, et al. Cardiac magnetic resonance perfusion imaging for the functional assessment of coronary artery disease: a comparison with coronary angiography and fractional flow reserve Eur Heart J 2006;27:1465-1471.[Abstract/Free Full Text] 22. Sakuma H, Suzawa N, Ichikawa Y, et al. Diagnostic accuracy of stress first-pass contrast-enhanced myocardial perfusion MRI compared with stress myocardial perfusion scintigraphy AJR Am J Roentgenol 2005;185:95-102.[Abstract/Free Full Text] 23. 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] 24. 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][Web of Science][Medline] 25. Takase B, Nagata M, Kihara T, et al. Whole-heart dipyridamole stress first-pass myocardial perfusion MRI for the detection of coronary artery disease Jpn Heart J 2004;45:475-486.[CrossRef][Medline] 26. Thiele H, Plein S, Breeuwer M, et al. Color-encoded semiautomatic analysis of multi-slice first-pass magnetic resonance perfusion: comparison to tetrofosmin single photon emission computed tomography perfusion and X-ray angiography Int J Cardiovasc Imaging 2004;20:371-384.[CrossRef][Web of Science][Medline] 27. Baer FM, Smolarz K, Jungehulsing M, et al. Feasibility of high-dose dipyridamole-magnetic resonance imaging for detection of coronary artery disease and comparison with coronary angiography Am J Cardiol 1992;69:51-56.[CrossRef][Web of Science][Medline] 28. Baer FM, Voth E, Theissen P, Schneider CA, Schicha H, Sechtem U. Coronary artery disease: findings with GRE MR imaging and Tc-99m-methoxyisobutyl-isonitrile SPECT during simultaneous dobutamine stress Radiology 1994;193:203-209.[Abstract/Free Full Text] 29. Hundley WG, Hamilton CA, Thomas MS, et al. Utility of fast cine magnetic resonance imaging and display for the detection of myocardial ischemia in patients not well suited for second harmonic stress echocardiography Circulation 1999;100:1697-1702.[Abstract/Free Full Text] 30. Jahnke C, Paetsch I, Gebker R, Bornstedt A, Fleck E, Nagel E. Accelerated 4D dobutamine stress MR imaging with k-t BLAST: feasibility and diagnostic performance Radiology 2006;241:718-728.[Abstract/Free Full Text] 31. Nagel E, Lehmkuhl HB, Bocksch W, et al. Noninvasive diagnosis of ischemia-induced wall motion abnormalities with the use of high-dose dobutamine stress MRI: comparison with dobutamine stress echocardiography Circulation 1999;99:763-770.[Abstract/Free Full Text] 32. 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] 33. Paetsch I, Jahnke C, Ferrari VA, et al. Determination of interobserver variability for identifying inducible left ventricular wall motion abnormalities during dobutamine stress magnetic resonance imaging Eur Heart J 2006;27:1459-1464.[Abstract/Free Full Text] 34. Pennell DJ, Underwood SR, Ell PJ, Swanton RH, Walker JM, Longmore DB. Dipyridamole magnetic resonance imaging: a comparison with thallium-201 emission tomography Br Heart J 1990;64:362-369.[Abstract/Free Full Text] 35. Pennell DJ, Underwood SR, Manzara CC, et al. Magnetic resonance imaging during dobutamine stress in coronary artery disease Am J Cardiol 1992;70:34-40.[CrossRef][Web of Science][Medline] 36. Rerkpattanapipat P, Gandhi SK, Darty SN, et al. Feasibility to detect severe coronary artery stenoses with upright treadmill exercise magnetic resonance imaging Am J Cardiol 2003;92:603-606.[CrossRef][Web of Science][Medline] 37. Schalla S, Klein C, Paetsch I, et al. Real-time MR image acquisition during high-dose dobutamine hydrochloride stress for detecting left ventricular wall-motion abnormalities in patients with coronary arterial disease Radiology 2002;224:845-851.[Abstract/Free Full Text] 38. van Rugge FP, van der Wall EE, de Roos A, Bruschke AV. Dobutamine stress magnetic resonance imaging for detection of coronary artery disease J Am Coll Cardiol 1993;22:431-439.[Abstract] 39. van Rugge FP, van der Wall EE, Spanjersberg SJ, et al. Magnetic resonance imaging during dobutamine stress for detection and localization of coronary artery diseaseQuantitative wall motion analysis using a modification of the centerline method. Circulation 1994;90:127-138.[Abstract/Free Full Text] 40. Zhao S, Croisille P, Janier M, et al. Comparison between qualitative and quantitative wall motion analyses using dipyridamole stress breath-hold cine magnetic resonance imaging in patients with severe coronary artery stenosis Magn Reson Imaging 1997;15:891-898.[CrossRef][Web of Science][Medline] 41. Ingkanisorn WP, Kwong RY, Bohme NS, et al. Prognosis of negative adenosine stress magnetic resonance in patients presenting to an emergency department with chest pain J Am Coll Cardiol 2006;47:1427-1432.[Abstract/Free Full Text] 42. Underwood SR, Anagnostopoulos C, Cerqueira M, et al. Myocardial perfusion scintigraphy: the evidence Eur J Nucl Med Mol Imaging 2004;31:261-291.[CrossRef][Web of Science][Medline] 43. Schuijf JD, Shaw LJ, Wijns W, et al. Cardiac imaging in coronary artery disease: differing modalities Heart 2005;91:1110-1117.[Free Full Text] 44. Hendel RC, Patel MR, Kramer CM, et al. ACCF/ACR/SCCT/SCMR/ASNC/NASCI/SCAI/SIR 2006 appropriateness criteria for cardiac computed tomography and cardiac magnetic resonance imaging: a report of the American College of Cardiology Foundation/American College of Radiology, Society of Cardiovascular Computed Tomography, Society for Cardiovascular Magnetic Resonance, American Society of Nuclear Cardiology, North American Society for Cardiac Imaging, Society for Cardiovascular Angiography and Interventions, and Society of International Radiology J Am Coll Cardiol 2006;48:1475-1497.[Free Full Text]
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Eur. Heart J.,
February 2, 2008;
29(4):
434 - 435.
[Full Text]
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A. N. DeMaria, J. J. Bax, O. Ben-Yehuda, P. Clopton, G. K. Feld, G. S. Ginsburg, B. H. Greenberg, J. D. Knoke, W. Y.W. Lew, J. A.C. Lima, et al.
Highlights of the year in JACC 2007.
J. Am. Coll. Cardiol.,
January 29, 2008;
51(4):
490 - 512.
[Full Text]
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Abstract
Methods