ATS
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

Ann Thorac Surg 2008;86:1546-1553. doi:10.1016/j.athoracsur.2008.06.072
© 2008 The Society of Thoracic Surgeons
This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to Personal Folders
Right arrow Download to citation manager
Right arrow Author home page(s):
Michael K. Pasque
Right arrow Permission Requests
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Cupps, B. P.
Right arrow Articles by Pasque, M. K.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Cupps, B. P.
Right arrow Articles by Pasque, M. K.
Related Collections
Right arrow Myocardial infarction
Right arrowRelated Article

Original Articles: Adult Cardiac

Myocardial Viability Mapping by Magnetic Resonance-Based Multiparametric Systolic Strain Analysis

Brian P. Cupps, PhDa, Douglas R. Bree, MDb, Jason R. Wollmuth, MDc, Analyn C. Howells, RNa, Rochus K. Voeller, MDa, Joseph G. Rogers, MDd, Michael K. Pasque, MDa,*

a Division of Cardiothoracic Surgery, Washington University School of Medicine, St. Louis, Missouri
b Willowbrook Cardiovascular Associates, Houston, Texas
c St. Charles Medical Center and Heart Center, Bend, Oregon
d Department of Medicine, Duke University Medical Center, Durham, North Carolina

Accepted for publication June 13, 2008.

* Address correspondence to Dr Pasque, Barnes-Jewish Hospital, One Barnes-Jewish Hospital Plaza, Suite 3103 Queeny Tower, St. Louis, MO 63110 (Email: pasquem{at}wustl.edu).


   Abstract
Top
 Abstract
Introduction
 Patients and Methods
 Results
 Comment
 Acknowledgments
 References
 
Background: Regional myocardial contractility can be characterized by three-dimensional left ventricular (LV) multiparametric strain maps generated from sequential magnetic resonance imaging of radiofrequency tissue-tagging grid point displacements.

Methods: Normal average and standard deviation values for each of three strain indices at 15,300 LV points were determined from a normal volunteer human strain database (n = 50) by application of magnetic resonance–based three-dimensional strain analysis. Patient-specific multiparametric strain data from each ischemic cardiomyopathy patient (n = 20) were then submitted to a point-by-point comparison (n = 15,300 LV points) to the normal strain database. The resulting 15,300 composite multiparametric Z-score values (standard deviation from normal average) were color-contour mapped over patient-specific three-dimensional LV geometry to detect the abnormal contractile patterns associated with myocardial infarction and nonviable myocardium.

Results: The average multiparametric strain composite Z-score from each LV region (n = 120) was compared with the respective clinical standard viability testing result and used to construct a receiver-operator characteristic curve. The area under the curve was 0.941 (p < 0.001; 95% confidence interval: 0.897 to 0.985). A regional average Z-score threshold of 1.525 (> 1.525 being nonviable) resulted in a sensitivity of 90% and a specificity of 90%. Corresponding positive and negative predictive values were 84% and 95%, respectively.

Conclusions: The clinical application of magnetic resonance–based multiparametric strain analysis allowed accurate regional characterization and visualization of LV myocardial viability.


   Introduction
Top
 Abstract
 Introduction
Patients and Methods
 Results
 Comment
 Acknowledgments
 References
 
Catheter-based or cardiac surgical coronary revascularization procedures are the most commonly applied therapeutic interventions in patients with ischemic cardiomyopathy. Although many clinical factors are important in directing these high-risk interventions, the presence of viable myocardium in the distribution of target atherosclerotic vessels remains the foundation for intervention algorithms. Revascularizing viable myocardium improves outcomes, whereas high-risk revascularization procedures directed at nonviable myocardium do not improve outcomes over medical therapy alone [1–3]. Improving the accuracy of the detection and full characterization of the viable myocardium that directs the implementation of revascularization procedures is critical in our goal to improve outcomes in this high-risk patient population.

Currently utilized viability testing methodologies, such as thallium single-photon emission computed tomography (SPECT) scanning, positron emission tomography (PET) scanning, delayed-enhancement magnetic resonance imaging (MRI), and dobutamine echocardiography are effective in directing clinical decision-making algorithms. These modalities, however, are limited in the accuracy of their regional and transmural characterization of myocardial viability by the qualitative nature of their output images. This qualitative image output predisposes to interobserver variability in the interpretation of regional involvement in irreversible ischemic injury. For example, multiple interpreters may variably characterize an area of nonviable myocardium that resulted from left anterior descending coronary artery occlusion and which overlaps anterior, anterolateral, and anteroseptal regions, as well as extending a variable distance from base to apex in each of these left ventricular (LV) regions.

In a similar fashion, quantification of the degree of transmural injury is also limited with current modalities. Myocardial ischemia resulting from coronary artery occlusive disease is well known to affect the myocardium in a nonuniform transmural distribution, with the subendocardium being more susceptible to ischemia than the epicardial myocardium [3]. The ability to accurately differentiate isolated subendocardial infarction from transmural injury has important clinical implications in regard to prognosis, as well as the appropriate application of therapeutic intervention algorithms. Thallium SPECT scanning, PET scanning, delayed-enhancement MRI, and dobutamine echocardiography can only be expected to offer a qualitative characterization of transmural inhomogeneity. Once again, a lack of quantification of transmural viability predisposes to inconsistency in observer interpretation.

Our choice of tagged MRI as a modality for myocardial viability determination is based not only on its highly accurate temporal and spatial characterization of ventricular anatomy, but also and most importantly, on its ability to measure displacements and strain in the myocardial wall throughout ventricular systole [4–8]. Utilizing software developed in our laboratory, the displacement of these tag lines can be utilized to generate three-dimensional LV strain maps [6]. Application of a fitting algorithm to the point displacement information allows the interrogation of this strain map at 15,300 points in the LV, thereby characterizing all strain indices at virtually any point in the LV myocardium.

In addition to maintaining a high degree of regional and transmural discrimination, the large volume of mathematical information that describes systolic strain throughout the left ventricle also lends itself favorably to subsequent optimization of the visual display of regional viability results. Unlike most other scanning modalities whose primary output is an analogue image, the multiparametric strain tool generates a highly complex mathematical description of three-dimensional myocardial contraction that can be postprocessed to generate images that are most advantageous to clinical interpretation and inclusion in decision-making algorithms and the electronic medical record.

The hypothesis that MRI-based multiparametric systolic strain analysis can be effectively and efficiently implemented in the clinical assessment of patients with ischemic cardiomyopathy is tested in this initial clinical application. We further hypothesize that this methodology can accurately characterize regional myocardial viability when compared with current clinical standard viability testing methods. In reporting results, the importance of a head to head visual comparison of multiparametric strain three-dimensional images to thallium SPECT and PET images is emphasized in addition to standard regional average comparisons. It is well recognized that patient-specific coronary distribution, and therefore the injury distribution after coronary occlusion, does not necessarily respect the somewhat arbitrary regional boundaries commonly utilized in the reporting of viability results.


   Patients and Methods
Top
 Abstract
 Introduction
 Patients and Methods
Results
 Comment
 Acknowledgments
 References
 
Patient Characteristics
Twenty patients (aged 59.9 ± 9.3 years) with significant coronary artery disease, defined as more than 70% stenosis in at least one major coronary vessel, and at least one akinetic or severely hypokinetic LV wall segment underwent MRI-based multiparametric strain analysis. Patients with significant valvular disease or primary cardiomyopathy were excluded. A separate group of 50 healthy volunteers, who served as controls, also underwent strain analysis and contributed LV systolic strain data to our normal subject strain database. The control group consisted of 27 females and 23 males with an average age of 32.8 ± 10.6 years. The racial makeup of the control group was as follows: 41 were Caucasian, 5 were Asian, 2 were African American, and 1 each were Native American and more than one race. The Human Research Protection Office at Washington University, St. Louis, Missouri, approved the study, and all subjects gave informed written consent.

Magnetic Resonance Imaging
Imaging was performed in a 1.5 Tesla MR scanner (Sonata; Siemens Medical Systems, Malvern, Pennsylvania). Multiples short-axis image sets were acquired in parallel planes at 8-mm intervals extending from the plane of the mitral valve to the apex of the heart. Additionally, four sets of long-axis images oriented radially and intersecting the centroid of the ventricle were also obtained. For each selected imaging plane, a single-slice MR tagged image was collected with a sequence consisting of a spatial modulation of magnetization radiofrequency tissue-tagging preparation [9, 10] followed by a two-dimensional balanced steady-state free precession cine image acquisition. Image acquisition was synchronized with real-time electrocardiogram at the time of the MRI scanning. Typical imaging parameters were as follows: tag spacing 8 mm; slice thickness 8 mm; repetition time 30.3 ms; echo time 2.2 ms; field of view 306 x 350 mm; and image matrix 168 x 256.

Strain Calculations
The method used to compute strain in this study has been described previously [6, 11], so only a summary is provided here. Manually identified wall boundaries from the tagged MR images were used to construct a finite element model of the left ventricle. Using the attachment points of the right ventricular free wall with the septum as landmarks, a consistent finite element mesh for the model was constructed consisting of six hexahedral elements for the basal and middle regions of the LV and six pentahedral elements covering the apex (Fig 1). Three-dimensional systolic displacements were computed from the deformation of the tag surfaces using a previously described and validated method [6]. Analysis of the displacement data was carried out in the finite element software package StressCheck (ESRD, St. Louis, Missouri). A continuous representation of the displacement field was obtained over the domain of the model from a least squares fitting of the MRI measured displacement data utilizing the finite element basis functions. This fitting was done using the measurement analysis feature of the StressCheck program. Strain values were then computed from the results of this fitting.


Figure 1
View larger version (78K):
[in this window]
[in a new window]

  		Fig 1. Region-based left ventricular finite element model. The attachment points of the right ventricular free wall to the septum serve as fiducial landmarks to consistently and uniformly divide the left ventricle into six clinical standard regions. Each region is then divided into basal, middle, and apical segments. The standardized finite element mesh for the left ventricular model consists, therefore, of six hexahedral elements each for the basal and middle regions of the left ventricle and six pentahedral elements covering the apex.

 
Multiparametric Systolic Strain Z-Score Color-Contour Mapping
Raw systolic myocardial strain information draws clinical meaning and utility only as it relates the individual patient's point-specific regional contractile function to normal. To define "normal," therefore, a normal human strain database was developed. Utilizing MRI data from 50 normal volunteers, average and standard deviation values were computed for circumferential strain, longitudinal strain, and the minimum principal strain angle [12] at 15,300 points in an evenly spaced grid over the entire human LV finite element model. Strain varies throughout the human left ventricle and is sensitive to regional and transmural position [13, 14]. The characterization of a patient-specific strain value at a discrete point in the LV myocardium must therefore be in relation to the normal average value of that parameter at that position in the left ventricle.

The normal strain database allows the "normalization" of patient-specific raw strain values by their direct correlation to normal average and standard deviation ranges for each strain parameter at each of 15,300 discrete myocardial points. Mathematically, this is expressed by calculation of a Z-score, which is a simple numerical expression of the relationship of the patient-specific raw strain value to the normal average strain value. The Z-score is computed as the difference between the measured strain value and the normal average divided by the normal standard deviation at a particular point. A Z-score of 1.87, for example, means that the raw patient value for that strain parameter at that point in the left ventricle falls 1.87 standard deviations from the normal average of that particular strain parameter at that point in the left ventricle.

The normalization of patient-specific individual strain component raw values allows the combination of multiple strain parameters, with their variable normal ranges, into a single multiparametric strain composite index. The patient-specific multiparametric Z-score for any one of the individual 15,300 points throughout the left ventricle, therefore, relates that patient's contractile function at that point in the myocardium to normal by relating its multiparametric strain, as expressed by a composite of three equally weighted strain components, to the "normal" multiparametric strain at that point in the left ventricle. The patient-specific multiparametric composite Z-score values for each of 15,300 LV points are then subjected to three-dimensional color-contour mapping (Figs 2 and 3). Go To statistically compare the clinical viability standard to the MRI-based multiparametric analysis, average values of the multiparametric strain Z-score were computed for each of six standardized LV regions (anteroseptal, anterior, anterolateral, posterolateral, posterior, posteroseptal).


Figure 2
View larger version (70K):
[in this window]
[in a new window]

  		Fig 2. Thallium single-photon emission computed tomography (SPECT) viability imaging versus magnetic resonance imaging (MRI)–based multiparametric strain Z-score color-contour modeling. Similarity is demonstrated between the blue areas of irreversible abnormal perfusion in the left-sided thallium SPECT image and the yellow (Z-score > 1.5) and red (Z-score > 2.25) areas of the corresponding MRI-based multiparametric strain Z-score color-contour images on the right. For each of three representative patients, a thallium SPECT scan on the left is compared with its corresponding multiparametric strain Z-score three-dimensional color-contour model on the right. Each three-dimensional multiparametric strain image was rotated to obtain the anatomically analogous view of the myocardial region of interest. The common finding of extension of infarction boundaries across multiple regions of the left ventricle is well demonstrated. (ANT = anterior; INF = inferior; LAT = lateral; SEPT = septal.)

 

Figure 3
View larger version (70K):
[in this window]
[in a new window]

  		Fig 3. Magnetic resonance imaging (MRI)–based multiparametric strain Z-score color-contour modeling versus positron emission tomography (PET) viability imaging. The PET scan results from 3 representative patients are presented in two-dimensional cross-sectional format. A corresponding two-dimensional slice was obtained for comparison by subjecting the MRI-based multiparametric strain Z-score three-dimensional color-contour model to a postprocessing cutting tool. In each comparison, the PET scan is found on the left with a comparable slice through our model in the same patient being found immediately to the right. Nonviable areas on the PET images are represented by a gap in the red, yellow, or green circular contour of the ventricular cross-section. Once again, similarities are demonstrated between the PET nonviable regions on the left and the yellow (Z-score > 1.5) and red (Z-score > 2.25) areas of the corresponding MRI-based multiparametric strain Z-score color-contour images on the right. The propensity of irreversible injury from myocardial infarction to extend across multiple regional boundaries of the heart is again apparent.

 
Clinical Standard Viability Testing
All of the patients enrolled in this trial had clinical questions of viability owing to regional myocardial dysfunction. Prior viability testing had been performed in 19 of 20 (18 thallium SPECT or PET, or both; one late-enhancement MRI). The 20th patient was initially referred with a clinical history of one akinetic left ventricular wall with questionable viability. After being enrolled in the study, she was later judged, by repeat echocardiography at our institution, to have normal function in all regions. She was therefore considered viable in all regions and was not subjected to further viability testing before undergoing coronary artery bypass grafting for severe three-vessel disease. Imaging subspecialists, who were blinded to the MRI-based multiparametric strain analysis results, characterized each of six LV regions in each of the patients as either predominantly viable or predominantly nonviable. Including the six viable regions in the 20th patient, this generated a total of 120 regions for comparison with the MRI-based multiparametric strain analysis results.

Statistical Analysis
For each of 120 LV regions, the clinical standard viability testing result was assumed true. A comparison of the multiparametric strain average composite Z-score between viable and nonviable segments was done using an unpaired t test assuming equal variances. A receiver-operator characteristic (ROC) curve was generated to assess the accuracy of the regional Z-scores in distinguishing viable from nonviable myocardium and to identify a threshold value of this measurement that would optimize sensitivity and specificity. To determine the sensitivity and specificity (in regard to detection of nonviable myocardium) the results of the MRI-based multiparametric strain analysis viability testing were then compared region for region with the clinical standard viability testing result. Sensitivity was defined as the proportion of clinical standard nonviable ventricular regions that tested nonviable with the MRI-based multiparametric strain analysis. Specificity was defined as the proportion of clinical standard viable ventricular regions that tested viable with the MRI-based multiparametric strain analysis. All calculations were carried out using the SPSS statistical package (SPSS, Chicago, Illinois).


   Results
Top
 Abstract
 Introduction
 Patients and Methods
 Results
Comment
 Acknowledgments
 References
 
Of 120 total LV test regions, 41 (34%) were declared predominantly nonviable, and 79 (66%) predominantly viable by the clinical standard viability testing. Multiparametric strain analysis average Z-score values were calculated for each of 120 test ventricular regions. The results of both analyses are displayed in Table 1. The average multiparametric strain Z-score in the clinical standard nonviable segments was significantly increased compared with the average in the viable segments (2.20 ± 0.69 versus 0.89 ± 0.60, p < 0.001). Average multiparametric strain Z-scores from each of the regions were used to construct a ROC curve (Fig 4). The area under the curve was found to be 0.941 (p < 0.001) with a 95% confidence interval having a lower bound of 0.897 and an upper bound of 0.985. Utilizing a multiparametric strain regional average Z-score of 1.525 as a cutoff value for viability (> 1.525 being nonviable) resulted in an optimized sensitivity of 90% and a specificity of 90%. The corresponding positive and negative predictive values were 84% and 95%, respectively.


View this table:
[in this window]
[in a new window]

  		Table 1 Regional Average Multiparametric Strain Z-Scores
 

Figure 4
View larger version (12K):
[in this window]
[in a new window]

  		Fig 4. Receiver-operator characteristic (ROC) curve generated from multiparametric Z-score data. The average multiparametric strain composite Z-score from each left ventricular region (n = 120) was compared with the respective clinical standard viability testing result and used to construct a ROC curve. The area under the curve was found to be 0.941 (p < 0.001) with a 95% confidence interval having a lower bound of 0.897 and an upper bound of 0.985.

 
The regional and transmural accuracy of MRI-based multiparametric Z-score three-dimensional color-contour mapping on patient-specific LV geometry is demonstrated by visual comparisons of representative multiparametric strain images to their respective thallium SPECT (Fig 2) and PET (Fig 3) images.


   Comment
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Comment
Acknowledgments
 References
 
The tracking of the systolic deformation of radiofrequency tissue-tagging grids by sequential MRI scanning uniquely allows the accurate generation of three-dimensional LV systolic strain maps from the resulting intramyocardial point displacement information. This capability has ushered in a new era in the quantification of regional and transmural myocardial contractile function. Multiple strain indices can be simultaneously clinically assessed by these methods. Three of these indices (circumferential strain, longitudinal strain, and the minimum principal strain angle [12]) have demonstrated accuracy in the characterization of regional and transmural myocardial function [11, 15, 16].

Our ability to accurately characterize LV strain in the clinical setting has been further enhanced by the establishment of a normal human strain database (n = 50). This strain database includes the normal average and standard deviation for all strain parameters at each of 15,300 points in the normal human left ventricle. Normalization of patient-specific raw strain parameters by Z-score calculation utilizing this normal strain database enables the combination of multiple strain parameters, with their widely variable raw data ranges, into a multiparametric clinical strain tool. This strain tool can thereby characterize point-specific regional and transmural myocardial contractile function by accurately relating the patient-specific raw strain parameter information to the normal average and standard deviation. In this first clinical implementation of MRI-based multiparametric strain analysis, our results strongly suggest that its accuracy in characterizing regional myocardial contractile function has indeed resulted in accuracy in the identification and localization of postinfarction irreversible myocardial injury.

The MRI-based multiparametric strain methodology utilized in this investigation generates large volumes of numerical data describing the patient-specific systolic strain characteristics of the left ventricle. Because three different strain parameters are assessed at each of 15,300 points, this results in the comparison of 45,900 patient-specific strain values from each individual patient to their respective point-specific strain normal ranges. The direct relation of patient-specific data to "normal" optimizes the clinical utility of the output of this viability tool, as all medical testing results must ultimately be interpreted in relation to the expected normal human results.

The highly mathematical nature of the output from this strain analysis stands in sharp contrast to other viability testing modalities that generate images alone as primary output. They can only be taken back to numerical data with considerable difficulty. The quantitative numerical foundation for this strain viability tool allows the use of advanced computational techniques to present the results to the clinician in the most visually optimized manner. The resulting interpretation of the viability testing by the clinician is thus facilitated and optimized by the expression of the results in a three-dimensional color-coded format that specifically relates patient-specific raw strain values directly to the normal expected values.

The foundation of the characterization of regional viability by multiparametric strain analysis is the accurate quantification of regional and transmural myocardial contractile function. That regional multiparametric strain Z-scores greater than 1.52 accurately identify nonviable myocardium (as determined by clinical standard viability testing) is a reassuring additional attribute of a modality whose primary mission is to quantify regional myocardial contractile function. Magnetic resonance imaging–based multiparametric strain analysis is the first viability testing modality that utilizes highly quantified, observer-independent regional contractile function information to provide the viability characterization rather than relying upon surrogate markers for contractile function, such as perfusion or substrate metabolism. The foundational obligatory mission of the heart is to pump blood and improving this contractile capability is the whole point of determining viability in the setting of ischemic cardiomyopathy. In addition to identifying nonviable regions, MRI-based multiparametric Z-score mapping is the first clinically applicable methodology to also render a complete regional and transmural quantification of myocardial contractile function in the viable regions of the heart. A full characterization of contractile function for all regions of the heart is made available to the clinician to allow, among other things, the quantification of remote ventricular wall remodeling effects that occur secondary to the localized primary infarction injury.

Limitations
Even though MRI-based multiparametric strain analysis is based upon far more clinically measured transmural wall displacement information than any other currently utilized clinical modality, concerns have arisen regarding the number of measured data points relative to those extrapolated by the fitting algorithm employed in this methodology. When applied to hearts with normal wall thickness, currently utilized tissue-tagging grid dimensions limit the number of tag lines that can fit transmurally and therefore contribute to the density of measured displacement information in the radial direction. Our experience with radial strain analysis has in fact confirmed this limitation, with radial strain consistency and uniformity lagging behind that of circumferential and longitudinal strain measurements. This is, however, precisely where the strengths of a multiparametric strain analysis are demonstrated. The ability to normalize raw patient-specific strain component data by comparison with a normal human strain database uniquely allows the combination of parameters so that the analysis can be focused upon the strengths of the MRI tissue-tagging grid density. Specifically, it allows the three strain parameters that are based on the highest density of tag line data (circumferential, longitudinal, and minimum principal strain directions) to be combined into a single multiparametric strain index. This ability to focus the strain analysis on these directions—which in fact represent the directions of maximal fiber contraction—almost certainly contributed to the demonstrated accuracy of this methodology in the clinical detection of the abnormal contractile patterns that are associated with nonviable myocardium.

The lack of a pathologic "gold standard" to directly quantify regional and transmural nonviable myocardium in each of the 20 enrolled patients is a limitation that has been encountered during the initial clinical implementation of all of the currently available viability detection methodologies. Since no pathology specimens were available, the results obtained by multiparametric strain testing were instead compared with the clinical standard viability testing results obtained in the course of each patient's clinical management at a large, university-based teaching hospital. The qualitative nature of the regional viability discrimination of the clinical standard testing modality (primarily thallium SPECT and PET) compounded this limitation because areas of infarction often partially overlie multiple LV regions. The actual images obtained in 6 different patients whose infarcts extended over more than one region are therefore included in the results section of this manuscript. These images clearly demonstrate the need to move away from the qualitative regional descriptions utilized in the report rendered by the imaging subspecialist to the clinicians involved in patient management. In contrast, the true regional extension and transmurality of the discrete areas of injury are all accurately displayed to the clinician by MRI-based multiparametric strain Z-score color-contour mapping.

In conclusion, MRI-based multiparametric strain Z-score color-contour mapping allows quantification and direct visualization of patient-specific regional contractile function in a format that specifically relates it to the expected norm. The accuracy of this regional quantification of myocardial contractile function appears to be such that it can very reasonably duplicate the results obtained with current clinical standard myocardial viability testing.


   Acknowledgments
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Comment
 Acknowledgments
References
 
Supported by NIH Grants HL069967 and HL064869.


   References
Top
 Abstract
 Introduction
 Patients and Methods
 Results
 Comment
 Acknowledgments
 References
 

  1. Allman KC, Shaw LJ, Hachamovitch R, Udelson JE. Myocardial viability testing and impact of revascularization on prognosis in patients with coronary artery disease and left ventricular dysfunction: a meta-analysis J Am Coll Cardiol 2002;39:1151-1158.[Abstract/Free Full Text]
  2. Ragosta M, Beller GA, Watson DD, et al. Quantitative planar rest-redistribution 201Tl imaging in detection of myocardial viability and prediction of improvement in left ventricular function after coronary bypass surgery in patients with severely depressed left ventricular function Circulation 1993;87:1630-1641.[Abstract/Free Full Text]
  3. Thornhill RE, Prato FS, Wisenberg G. The assessment of myocardial viability: a review of current diagnostic imaging approaches J Cardiovasc Magn Reson 2002;4:381-410.[Medline]
  4. Denney TS, Prince JL. Reconstruction of 3D left ventricular motion from planar tagged cardiac MR images: an estimation theoretic approach IEEE Trans Med Imaging 1995;14:413-421.[Medline]
  5. Huang J, Abendschein D, Davila-Roman VG, Amini AA. Spatio-temporal tracking of myocardial deformations with a 4-D B-spline model from tagged MRI IEEE Trans Med Imaging 1999;18:957-972.[Medline]
  6. Moulton MJ, Creswell LL, Downing SW, et al. Spline surface interpolation for calculating 3-D ventricular strains from MRI tissue tagging Am J Physiol 1996;270:H281-H297.[Medline]
  7. Young AA, Axel L. Three-dimensional motion and deformation of the heart wall: estimation with spatial modulation of magnetization—a model-based approach Radiology 1992;185:241-247.[Abstract/Free Full Text]
  8. Young AA, Kraitchman DL, Dougherty L, Axel L. Tracking and finite element analysis of stripe deformation in magnetic resonance imaging IEEE Trans Med Imaging 1995;14:413-421.[Medline]
  9. Axel L, Dougherty L. MR imaging of motion with spatial modulation of magnetization Radiology 1989;171:841-845.[Abstract/Free Full Text]
  10. Axel L, Dougherty L. Heart wall motion: improved method of spatial modulation of magnetization for MR imaging Radiology 1989;172:349-350.[Abstract/Free Full Text]
  11. Moustakidis P, Cupps BP, Pomerantz BJ, et al. Noninvasive, quantitative assessment of left ventricular function in ischemic cardiomyopathy J Surg Res 2004;116:187-196.[Medline]
  12. Cupps BP, Pomerantz BJ, Krock, MD, et al. Principal strain orientation in the normal human left ventricle Ann Thorac Surg 2005;79:1338-1343.[Abstract/Free Full Text]
  13. Moore CC, Lugo-Olivieri CH, McVeigh ER, Zerhouni EA. Three-dimensional systolic strain patterns in the normal human left ventricle: characterization with tagged MR imaging Radiology 2000;214:453-466.[Abstract/Free Full Text]
  14. Young AA, Imai H, Chang CN, Axel L. Two-dimensional left ventricular deformation during systole using magnetic resonance imaging with spatial modulation of magnetization Circulation 1994;89:740-752[published erratum appears in Circulation 1994;90:1584].[Abstract/Free Full Text]
  15. Bree D, Wollmuth JR, Cupps BP, et al. Low-dose dobutamine tissue-tagged magnetic resonance imaging with 3-dimensional strain analysis allows assessment of myocardial viability in patients with ischemic cardiomyopathy Circulation 2006;114(Suppl 1):33-36.
  16. Pomerantz BJ, Wollmuth JR, Krock, MD, et al. Myocardial systolic strain is decreased after aortic valve replacement in patients with aortic insufficiency Ann Thorac Surg 2005;80:2186-2192.[Abstract/Free Full Text]

Related Article

Invited Commentary
Amedeo Anselmi and Carlo Nicola De Cecco
Ann. Thorac. Surg. 2008 86: 1553. [Extract] [Full Text] [PDF]



This article has been cited by other articles:


Home page
 Ann. Thorac. Surg.Home page
A. Anselmi and C. N. De Cecco
Invited Commentary
Ann. Thorac. Surg., November 1, 2008; 86(5): 1553 - 1553.
[Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to Personal Folders
Right arrow Download to citation manager
Right arrow Author home page(s):
Michael K. Pasque
Right arrow Permission Requests
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Cupps, B. P.
Right arrow Articles by Pasque, M. K.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Cupps, B. P.
Right arrow Articles by Pasque, M. K.
Related Collections
Right arrow Myocardial infarction
Right arrowRelated Article

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
ANN THORAC SURG ASIAN CARDIOVASC THORAC ANN EUR J CARDIOTHORAC SURG
J THORAC CARDIOVASC SURG ICVTS ALL CTSNet JOURNALS