Virtual Electrophysiological Study in a 3-Dimensional Cardiac Magnetic Resonance Imaging Model of Porcine Myocardial Infarction

Journal of the American College of Cardiology © 2012 by the American College of Cardiology Foundation Published by Elsevier Inc.

Vol. 60, No. 5, 2012 ISSN 0735-1097/$36.00 http://dx.doi.org/10.1016/j.jacc.2012.03.029

Virtual Electrophysiological Study in a 3-Dimensional Cardiac Magnetic Resonance Imaging Model of Porcine Myocardial Infarction Jason Ng, PHD,* Jason T. Jacobson, MD,* Justin K. Ng, MS,* David Gordon, MD, PHD,* Daniel C. Lee, MD,* James C. Carr, MD,† Jeffrey J. Goldberger, MD* Chicago, Illinois Objectives

This study sought to test the hypothesis that “virtual” electrophysiological studies (EPS) on an anatomic platform generated by 3-dimensional magnetic resonance imaging reconstruction of the left ventricle can reproduce the reentrant circuits of induced ventricular tachycardia (VT) in a porcine model of myocardial infarction.

Background

Delayed-enhancement magnetic resonance imaging has been used to characterize myocardial infarction and “gray zones,” which are thought to reflect heterogeneous regions of viable and nonviable myocytes.

Methods

Myocardial infarction by coronary artery occlusion was induced in 8 pigs. After a recovery period, 3-dimensional cardiac magnetic resonance images were obtained from each pig in vivo. Normal areas, gray zones, and infarct cores were classified based on voxel intensity. In the computer model, gray zones were assigned slower conduction and longer action potential durations than those for normal myocardium. Virtual EPS was performed and compared with results of actual in vivo programmed stimulation and noncontact mapping.

Results

The left ventricular volumes ranged from 97.8 to 166.2 cm3, with 4.9% to 17.5% of voxels classified as infarct zones. Six of the 7 pigs in which VT developed during actual EPS were also inducible with virtual EPS. Four of the 6 pigs that had simulated VT had reentrant circuits that approximated the circuits seen with noncontact mapping, whereas the remaining 2 had similar circuits but propagating in opposite directions.

Conclusions

This initial study demonstrates the feasibility of applying a mathematical model to magnetic resonance imaging reconstructions of the left ventricle to predict VT circuits. Virtual EPS may be helpful to plan catheter ablation strategies or to identify patients who are at risk of future episodes of VT. (J Am Coll Cardiol 2012;60:423–30) © 2012 by the American College of Cardiology Foundation

Sudden cardiac death (SCD) is a major health issue in the United States, affecting as many as 400,000 people yearly (1). Most SCDs are due to arrhythmias, namely, ventricular tachycardia (VT) or ventricular fibrillation. The ability to identify at-risk patients before they experience cardiac arrest is critical because prophylactic implantable cardioverterdefibrillators can significantly reduce mortality. Although current techniques and algorithms identify populations that are at

From the *Division of Cardiology and Bluhm Cardiovascular Institute, Feinberg School of Medicine, Northwestern University, Chicago, Illinois; and the †Department of Radiology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois. This study was supported by grants from the NIH (1 R21 HL094902-01) and from St. Jude Medical Systems. Dr. Jacobson consults for St. Jude Medical Systems. Dr. Carr consults for and has grant support from Siemens Healthcare and Astellas. Dr. Lee and Dr. Goldberger receive grant support from St. Jude Medical. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. Manuscript received January 3, 2012; revised manuscript received February 23, 2012, accepted March 12, 2012.

higher risk of SCD, they lack the ability to truly differentiate the low-risk group from the high-risk group (2). Electrical mapping and pacing during animal (3,4) and clinical (5–7) studies have demonstrated that VTs after myocardial infarction (MI) are often macroreentrant circuits supported by an anatomic substrate resulting from infarct scarring. These circuits can be complex, containing areas of slow conduction and multiple pathways of reentry. Stevenson et al. (8) showed that some of the pathways may be critical, whereas others may simply be bystander areas which when interrupted through ablation will not terminate the arrhythmia. It has been shown that magnetic resonance imaging (MRI) with contrast can be used to detect scarring after MI. It has also been shown that infarct size is a better predictor of inducible VT than the left ventricular ejection fraction (9), highlighting the importance of the infarct substrate in the generation of VT. Infarct size measure by MRI has also been shown to be an independent predictor of mortality (10)

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and spontaneous VT (11). The surrounding border zones, also known as gray zones, are thought 3D ⴝ 3-dimensional to be a heterogeneous mix of EPS ⴝ electrophysiological viable and nonviable myocarstudy dium and have been shown to LV ⴝ left ventricle correlate with post-MI mortality MI ⴝ myocardial infarction (12), clinical VT (13), and VT MRI ⴝ magnetic resonance inducibility (14). Infarct transimaging murality measured from MRI SCD ⴝ sudden cardiac has also been shown to be a death predictor of ventricular arrhythVEPS ⴝ virtual mias (15). Because of the strong electrophysiological study correlation observed between the VT ⴝ ventricular infarct morphologies observed on tachycardia MRI and pathologic analysis (16), we hypothesized that virtual electrophysiological study (VEPS) consisting of the combination of: 1) anatomic characterization of infarct morphology by 3-dimensional (3D) MRI in vivo; and 2) the computer simulation of cardiac electrophysiology could be used to predict the characteristics of induced VT during actual electrophysiological study (EPS). This hypothesis was tested using a porcine model of chronic MI. Abbreviations and Acronyms

Methods Animal model of MI. Eight pigs weighing 55 to 75 kg were used for this study. The closed chest coronary occlusion protocol to induce MI was described previously (17) and presented in more detail in the Online Appendix. Briefly, 300 ml of agarose gel beads diluted in 1.5 ml of saline solution was injected by a balloon catheter into either the left circumflex artery or the left anterior descending coronary artery just distal to the second marginal or diagonal branch to cause infarction. The pigs were then allowed 4 to 8 weeks to recover. The experimental protocol was approved by the Animal Care and Use Committee of Northwestern University. Contrast-enhanced 3D MRI. After the recovery period, cardiac magnetic resonance images were obtained from the pigs under general anesthesia (1% to 2.5% isoflurane) using a Siemens 3.0-T Trio MRI scanner (Siemens Medical Solutions, Erlangen, Germany) in the Center for Advanced MRI at Northwestern University. A free-breathing 3D phase-sensitive inversion recovery turbo FLASH pulse sequence was used for acquisition (18). The average 3D phase-sensitive inversion recovery scan time was 9.8 ⫾ 3.4 min. Phase-sensitive inversion recovery reconstruction was used to eliminate the need for precise setting of the inversion time, and parallel imaging was used to improve the acquisition speed. Image data were collected during free breathing by synchronizing the acquisition to the respiratory cycle using a crossed slice navigator. This technique yields near-isotropic spatial resolution with voxel sizes of 1.8 ⫻ 1.9 ⫻ 1.8 mm. Images were acquired approximately 15 to

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20 min after an intravenous injection of contrast (0.2 mmol/kg of gadopentetate dimeglumine, Magnevist, Bayer HealthCare, Leverkusen, Germany). Noncontact mapping of VT. EPS was performed after the 4- to 8-week recovery period within 3 days of MRI. Noncontact mapping (19) was performed using a commercial system (EnSite 3000, Endocardial Solutions, Inc., St. Paul, Minnesota), which recorded signals from a 64electrode array mounted on a 9-French catheter positioned in the left ventricle (LV) via a retrograde transaortic approach. The system creates a 3D geometry on which sequential isopotential maps constructed from ⬎3,000 virtual unipolar electrograms are displayed. Induction of VT was attempted by programmed ventricular stimulation in the right ventricle and LV with as many as 3 extrastimuli after a drive train of 8 paced beats at 2 basic cycle lengths. The virtual unipolar electrograms of any induced tachycardias (either by programmed ventricular stimulation or catheter manipulation) were saved for offline determination of arrhythmia characteristics and scar exit sites. Episodes ⬎30 s were considered sustained VT. MRI processing. All image processing of MRI data was performed using custom-designed software. The regions corresponding to the LV were then manually segmented. The segmented data were then linearly interpolated for a resulting resolution of 0.45 ⫻ 0.475 ⫻ 0.45 mm. Each voxel of the LV was classified as either normal, gray zone, or nonviable infarct core using a modification of a previously published algorithm by Schmidt et al. (14). First, the enhanced area of the MRI corresponding to the infarct was manually approximated. The selected area was overestimated to include normal regions at the boundaries of the scar. The nonselected area was therefore classified as normal myocardium. The classification algorithm was not applied to the nonselected area to avoid false detection of scar due to noise or artifacts. Within the selected scar region, all voxels with intensity values less than the mean ⫹ 3 SDs of the intensities of the selected normal myocardium were classified as completely viable. A second threshold was used to determine whether the remaining voxels were classified as part of the gray zone or part of the infarct core. Threshold values between 10% and 50% of the range bounded by the mean and 3 SDs of the normal myocardium intensities on the lower end and the maximum intensity of the identified scar area on the upper end were used at 5% increments. Figure 1 shows how the distribution of gray zone as seen on the epicardium increases relative to the nonviable scar as the threshold value is increased from 10% to 50%. Mathematical modeling of cardiac electrophysiology. Three-dimensional isotropic computer models were implemented using the 3D geometry of the LV constructed from the MRI. The Fenton-Karma 3-current ventricular action potential model (20) was used for this study. The tissueconduction model was created by the coupling of neighboring cells in a 3D lattice with current diffusion controlled by a diffusion constant D. A scaled membrane voltage variable

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0, 400 ms, and 210 ms with pulse width of 2 ms. Stimulation was performed in the LV at 1 basal site, 1 apical site, and 2 sites midway between the base and the apex at either side of the scar for a total of 36 simulations per pig (4 sites ⫻ 9 threshold values). An arrhythmia was considered sustained if it lasted at least 10 s. Simulations were automatically terminated if no sustained activation was detected after stimulation. Animations of the simulated VT were viewed at 50% transmurality of the LV to evaluate the endocardial activation sequences. Comparison of any VTs induced by virtual EPS with actual VTs captured by noncontact mapping was performed by 2 reviewers. Simulations were repeated with the assumption of: 1) no gray zone; and 2) no gray zone or infarct core to confirm that the combined infarct and gray zone characteristics were primarily responsible for sustaining VT. Figure 1

Myocardium/Scar Classification

The scar profiles of a pig left ventricle with gray zone thresholds increasing from 10% to 50% are shown. As the viability thresholds increase, the infarct core area decreases, whereas the gray zone area increases.

u is dependent on a fast inward current Jfi, a slow inward current Jsi, a slow outward current Jso, and a stimulus current Jstim as described by the following partial differential equation: ⭸u ⭸t

⫽ ⵜ ·D ⵜ u ⫺ 共Jfi ⫹ Jsi ⫹ Jso ⫺ Jstim兲

[1]

The ordinary differential equations describing the FentonKarma gating variables were integrated with the Rush and Larsen method (21) with adaptive time steps of 0.01 ms to 0.1 ms. The partial differential equation was solved with the Euler forward method. The no-flux boundary condition was used (20). The parameters of the Fenton-Karma model were altered using the procedure recommended by Oliver and Krassowka (22) to obtain the restitution curve characteristics shown in Figure 2. The gray zone areas were assigned parameters to mimic the longer action potential duration observed in healed infarction relative to normal myocardium (a roughly 30- to 40-ms difference) (23). The diffusion constants were chosen to obtain conduction velocity values of 0.33 m/s and 0.17 m/s for the normal and gray zone areas, respectively. Simulations were performed on workstations equipped with a dual-processor motherboard and 2 Intel Xeon Quad Core processors with clock speeds of 3.16 GHz with parallelization across the 8 total processing cores. Virtual electrophysiological study. A VEPS for each LV model at each of the gray zone/infarct core thresholds (10% to 50% at 5% increments) was performed. The following protocol was performed while blinded to the actual inducibility of the 8 pigs and the characteristics of any induced VT. Programmed stimulation consisted of 3 beats at times

Figure 2

Model Restitution Curves

The restitution curves of action potential duration (A) and conduction velocity (B) assigned to the normal and gray zone myocardium are displayed. The action potential durations at 90% repolarization (APD90) of the gray zone areas (shown in red) are longer action for a given diastolic interval than those of the normal restitution curve (shown in blue). Normal areas are assigned conduction velocities that are higher than those of the gray zones at any given diastolic interval.

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Inducibility EPS and VEPS atand Different Zone Threshold Levels Table 1 With Inducibility With EPS VEPS Gray at Different Gray Zone Threshold Levels Gray Zone/Infarct Core Threshold Pig #

Inducible With EPS?

1

No

2

Yes

3

Yes

x x

4

Yes

5

Yes

6

Yes

7

Yes

8

Yes

10%

15%

20%

25%

30%

35%

40%

45%

x

x

x

x

x

x

50%

x

x

x

x

x ⫽ inducibility with VEPS; EPS ⫽ electrophysiological study; VEPS ⫽ virtual electrophysiological study.

Statistics. The Student’s t test was used to compare the actual and simulated VT cycle lengths. Pearson’s correlation coefficients were used to correlate VT cycle lengths with LV and infarct volumes. Values of p ⬍ 0.05 were considered statistically significant. Results MRI characteristics. The left ventricularmyocardium, as determined by MRI, had volumes ranging from 97.8 to 166.2 cm3 (mean 134 ⫾ 22 cm3). The left circumflex artery was occluded in 4 pigs, and the left anterior diagonal descending artery was occluded in 4 pigs. The infarct volumes when considering both infarct cores and gray zones composed 4.9% to 17.5% (mean 10 ⫾ 4%) of the total LV myocardium volume. Electrophysiological study. Sustained monomorphic VT could be induced at least twice in 7 of the 8 pigs during EPS. Six pigs had VTs induced from the LV, whereas VT in 1 pig was induced from the right ventricle. Two of the pigs had 2 different VT circuits. The VTs had a mean cycle length of 280 ⫾ 51 ms. Virtual electrophysiological study. A set of 36 simulations for each pig required on average 14 ⫾ 2 h to complete. Table 1 shows the results of VEPS at each of the gray zone/infarct core threshold. Each of the thresholds labeled “x” correspond to having VT lasting at least 10 s. Pig 1 had inducible VT with VEPS but not with actual EPS, whereas pig 7 had inducible VT with actual EPS but not with VEPS. There was no significant difference between the mean cycle lengths of the actual VT and the virtual VT (280 ⫾ 51 ms vs. 263 ⫾ 61 ms; p ⫽ 0.58). The comparison of cycle lengths for each pig is shown in Table 2. Repeating the VEPS on the 8 heart models with the assumption of no gray zone (all infarct core) and no gray zone or infarct core resulted in no VT. Simulated VT was induced via a common mechanism for all episodes, as illustrated in the 3D membrane potential maps shown in Figure 3. The 3D maps show the simulated membrane potentials of the LV epicardial surface with blue areas representing resting tissue and red areas representing depolarized tissue. The gray regions are scar. The first paced beat, located outside but near the infarct area, at time 0

propagates through both the normal and gray zone regions of the left ventricle. The second beat at time 400 ms also propagates through both the normal and gray zone areas. The second beat in normal areas have action potential durations of 197 ms, whereas the second beat in the gray zones have action potential durations of 236 ms. The third beat at 610 ms propagates through the normal region but blocks at a scar isthmus due to the longer refractory period of the gray zone in the isthmus. The wavefront then propagates around the infarct area to the other side, at which point the gray zone is repolarized and the wavefront is able to re-enter. VT then follows. VEPS versus EPS. Six pigs had VT induced with both VEPS and actual EPS. Figure 4 shows the comparison of the endocardial membrane potential maps obtained by VEPS and the corresponding noncontact unipolar voltage maps obtained during EPS for the 4 pigs where good agreement in the scar exit sites were seen. Complete agreement of the VT circuits and scar exit sites was seen in pigs #3, #5, and #6. In pig #8, 2 exit sites were seen in the simulated VT circuit, 1 of which appears to match the exit site of the actual VT. Figure 5 shows the results for 2 pigs where the VT exit sites did not match. However, examination of the VT circuits revealed that activation propagated in opposite directions in the simulated versus actual VTs with reversed entrance and exits sites; this suggests that a similar isthmus may be supporting both the simulated and actual VT. Comparison of Actual andofVirtual Cycle Lengths Table 2 Comparison ActualVT and Virtual VT Cycle Lengths VT Cycle Lengths, ms Pig #

Actual

Virtual

1

None

234

2

306

214

3

370

390

4

270

226

5

237

236

6

291

255

7

270

None

8

213

286

VT ⫽ ventricular tachycardia.

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Figure 3

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427

Illustration of Arrhythmia Induction

The membrane potential maps illustrate simulated arrhythmia induction. The first beat propagates through both healthy and scar regions (A and B). The second beat 400 ms later also propagates through both healthy and scar regions with the scar region taking longer to repolarize (C and D). The third beat 210 ms later propagates through the normal regions but blocks at the initial contact with the scar (E and F). Re-entry around the scar is now initiated (G and H).

Correlation of VT cycle lengths with LV and infarct sizes. Table 3 shows the correlation coefficients of actual and simulated VT cycles lengths versus the left ventricular, infarct core, gray zone, and combined infarct core and gray zone volumes. The actual VT cycle lengths

Figure 4

were not significantly correlated with any of the volume measurements. However, the simulated VT cycle lengths were significantly correlated with LV volume (r ⫽ 0.79, p ⫽ 0.04) and infarct core volume (r ⫽ 0.90, p ⫽ 0.007).

Simulated and Actual VT Circuits With Good Agreement

The membrane potential maps of simulated ventricular tachycardia (VT) from 4 pigs are shown to have good agreement in infarct exit sites and activation directions when compared with noncontact maps of the actual VT.

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Figure 5

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Simulated and Actual VT Circuits With Reversed Directions

The membrane potential maps of simulated ventricular tachycardia (VT) from 2 pigs are shown to not have good agreement in infarct exit sites and activation directions when compared with noncontact maps of the actual VT. However, the reversal of the VT entrance and exit sites suggests that a similar isthmus may be supporting reentry for both the simulated and actual VT circuits.

Discussion This study demonstrates the feasibility of applying a mathematical model of the action potential to simulate conduction and predict VT circuits in an individualized model of the LV generated in vivo using contrast-enhanced MRI for the purpose of scar characterization. Virtual EPS results were similar to those obtained by actual EPS in 6 of 8 pigs. Virtual EPS may be useful for evaluating VT circuits to better plan approaches to catheter ablation and could be evaluated for its ability to predict future VT events in patients with MI and no history of VT. MRI to predict VT. A combination of structural (24 –28) and electrophysiological (23,29) remodeling resulting from MI contribute to susceptibility to VT. For structural remodeling to provide a sufficient substrate for VT, a combination of infarct core and peri-infarct regions is needed. The infarct core provides the anatomic boundary needed for sustained reentry. Infarct size/mass quantification by MRI have been shown to predict EPS-induced (9) and clinical (10,11,15) VT. The peri-infarct zones are damaged but viable regions of myocardium which contribute to VT through slowing of conduction and unidirectional block due to altered refractory periods. The peri-infarct zones are and MRI Characteristics Correlation Between VT Between Cycle Lengths Correlation VT Cycle Lengths Table 3 and MRI Characteristics Actual VT Cycle Length Volume

Simulated VT Cycle Length

r

p Value

r

p Value

LV

0.25

0.58

0.79

0.04

Infarct core

0.52

0.28

0.90

0.007

Gray zone

0.15

0.78

0.03

0.95

Infarct core ⫹ gray zone

0.2

0.67

0.56

0.19

LV ⫽ left ventricular; MRI ⫽ magnetic resonance imaging; VT ⫽ ventricular tachycardia.

characterized by gray zones in the MRI—intermediate intensities between the darker normal myocardium and the enhanced infarct core. Quantification of MRI gray zones has been used to predict both EPS-induced (14) and clinical (12,13) VT. In addition to the volume of infarct core and gray zones, there are morphological factors of both that appear to play a significant role in the inducibility of VT (30). Channel isthmuses within the infarct are known to facilitate VT and have been targeted for ablation. Perez-David et al. (31) showed that these isthmuses can be detected by MRI and that patients with sustained monomorphic VT were more likely to have MRI-detected isthmuses than patients with similar ejection fractions without VT. The transmurality of the infarct is another important feature because VT circuits likely require both endocardial and epicardial boundaries to support reentry. Boyé et al. (15) showed that relative infarct transmurality measured from the MRI was an independent predictor of clinical VT events. Although the size and morphological characteristics of the infarct mentioned likely all play a critical role in VT, their relative contributions are not easily quantified. For example, a larger infarct volume with a shorter isthmus length may be equally arrhythmogenic as a smaller infarct with a longer channel. The proposed VEPS system takes all of these factors into account in the prediction of VT circuits. Although electrophysiological information cannot be obtained from the MRI, it is known that refractory period gradients across the normal myocardium and peri-infarct zones are needed for the VT induction process. Thus, our model assumes higher refractory periods in the MRI gray zones to allow realistic induction of VT with simulated programmed stimulation. Other computer models of VT. Vigmond et al. (32) demonstrated simulated VT in a computer model created

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using high-resolution MRI of a canine ventricle imaged ex vivo 4 weeks after MI. This model incorporated fiber orientation with diffusion tensor imaging and a simulated His-Purkinje network, while also characterizing nonviable infarct areas and the viable border zones. Pop et al. (33) showed in a pig with induced MI that computer simulation based on ex vivo diffusion tensor MRI could predict the VT circuit obtained with actual EPS. In another study, Sermesant et al. (34) demonstrated the feasibility of an electromechanical model of the infarct ventricle registered to fit clinical data from MRI and x-ray, as well as actual cardiac electrical activity. The present study is the first to demonstrate the possibility of computer simulation of VT with in vivo MRI using clinically available sequences. In contrast to the previously published study using MRI, certain details such as fiber orientation cannot be obtained due to lower spatial resolution. In the development of our model, we therefore focused the model on the key elements of VT that can be obtained by clinical MRI, namely, the infarct core and gray zone characteristics. Even with the omission of the right ventricle, fiber orientation, and the His-Purkinje system, we demonstrated the VT circuits can be predicted in our VEPS system. The simple model also makes simulation practical on commercial workstations rather than requiring a sophisticated computer cluster. Further study is needed to test whether more realistic ventricular modeling is necessary for the prediction of VT induction. Study limitations. A limitation of the study is the small sample size. Spatial resolution of the current images was approximately 2 mm in all 3 dimensions. This may be inadequate to delineate some VT circuits. However, image resolution is likely to improve with time and advances in MRI technology. Defining the gray zone is challenging, and there are no well-accepted criteria for this. Our study found that there was not a consistent threshold value that allowed inducible VT with simulation for all the pigs. Differences in signal-to-noise ratio may in part account for the differences in threshold. Better delineation of this region will also likely improve the performance of VEPS. Further work to develop and evaluate an algorithm for accurate tissue classification from MRI data is an important future study that will require large numbers of subjects. Clearly, further development and testing will be required to establish the utility of VEPS in humans. Potential clinical implications. Computer modeling of the heart has generally been confined to research applications. This pilot study suggests that contrast-enhanced MRI for VEPS could have several potential applications to help identify VT circuits before catheter ablation and potentially as a platform for risk stratification for SCD. The technique is noninvasive, avoiding the risks and time required for the sedation and catheterization needed for standard electrophysiology testing. In addition, VEPS could allow detailed characterization of the induced arrhythmia that would otherwise only be possible with high-resolution mapping.

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Ablation strategies could be planned with this information. Further evaluation and development of this technology will be necessary to assess its utility in the clinical setting. Acknowledgments

The authors thank Kathleen Harris and Brandon Benefield for their assistance with this study. Reprint requests and correspondence: Dr. Jeffrey J. Goldberger, Division of Cardiac Electrophysiology, Northwestern University Feinberg School of Medicine, 251 East Huron, Feinberg Pavilion, Suite 8-503, Chicago, Illinois 60611. E-mail: j-goldberger@ northwestern.edu. REFERENCES

1. State-specific mortality from sudden cardiac death–United States, 1999 by the Centers for Disease Control and Prevention. MMWR Morb Mortal Wkly Rep 2002;51:123– 6. 2. Goldberger JJ, Cain ME, Hohnloser SH, et al. American Heart Association/American College of Cardiology Foundation/Heart Rhythm Society scientific statement on noninvasive risk stratification techniques for identifying patients at risk for sudden cardiac death: a scientific statement from the American Heart Association Council on Clinical Cardiology Committee on Electrocardiography and Arrhythmias and Council on Epidemiology and Prevention. J Am Coll Cardiol 2008;52:1179 –99. 3. El-Sherif N, Scherlag BJ, Lazzara R, Hope RR. Re-entrant ventricular arrhythmias in the late myocardial infarction period. 1. Conduction characteristics in the infarction zone. Circulation 1977;55:686 –702. 4. Kabell G, Scherlag BJ, Hope RR, Lazzara R. Patterns of interectopic activation recorded during pleomorphic ventricular tachycardia after myocardial infarction in the dog. Am J Cardiol 1982;49:56 – 62. 5. Kaltenbrunner W, Cardinal R, Dubuc M, et al. Epicardial and endocardial mapping of ventricular tachycardia in patients with myocardial infarction. Is the origin of the tachycardia always subendocardially localized? Circulation 1991;84:1058 –71. 6. Downar E, Saito J, Doig JC, et al. Endocardial mapping of ventricular tachycardia in the intact human ventricle. III. Evidence of multiuse reentry with spontaneous and induced block in portions of reentrant path complex. J Am Coll Cardiol 1995;25:1591– 600. 7. de Bakker JM, van Capelle FJ, Janse MJ, et al. Reentry as a cause of ventricular tachycardia in patients with chronic ischemic heart disease: electrophysiologic and anatomic correlation. Circulation 1988;77:589–606. 8. Stevenson WG, Khan H, Sager P, et al. Identification of reentry circuit sites during catheter mapping and radiofrequency ablation of ventricular tachycardia late after myocardial infarction. Circulation 1993;88:1647–70. 9. Bello D, Fieno DS, Kim RJ, et al. Infarct morphology identifies patients with substrate for sustained ventricular tachycardia. J Am Coll Cardiol 2005;45:1104 – 8. 10. Bello D, Einhorn A, Kaushal R, et al. Cardiac magnetic resonance imaging: infarct size is an independent predictor of mortality in patients with coronary artery disease. Magn Reson Imaging 2011;29: 50 – 6. 11. Bernhardt P, Stiller S, Kottmair E, et al. Myocardial scar extent evaluated by cardiac magnetic resonance imaging in ICD patients: relationship to spontaneous VT during long-term follow-up. Int J Cardiovasc Imaging 2011;116:1096 –104. 12. Yan AT, Shayne AJ, Brown KA, et al. Characterization of the peri-infarct zone by contrast-enhanced cardiac magnetic resonance imaging is a powerful predictor of post-myocardial infarction mortality. [see comment]. Circulation 2006;114:32–9. 13. Roes SD, Borleffs CJ, van der Geest RJ, et al. Infarct tissue heterogeneity assessed with contrast-enhanced MRI predicts spontaneous ventricular arrhythmia in patients with ischemic cardiomyopathy and implantable cardioverter-defibrillator. Circ Cardiovasc Imaging 2009; 2:183–90. 14. Schmidt A, Azevedo CF, Cheng A, et al. Infarct tissue heterogeneity by magnetic resonance imaging identifies enhanced cardiac arrhythmia

430

15.

16. 17. 18.

19.

20. 21. 22. 23. 24.

25.

Ng et al. Virtual Electrophysiological Study susceptibility in patients with left ventricular dysfunction. Circulation 2007;115:2006 –14. Boyé P, Abdel-Aty H, Zacharzowsky U, et al. Prediction of lifethreatening arrhythmic events in patients with chronic myocardial infarction by contrast-enhanced CMR. J Am Coll Cardiol Img 2011;4:871–9. Kim RJ, Fieno DS, Parrish TB, et al. Relationship of MRI delayed contrast enhancement to irreversible injury, infarct age, and contractile function. Circulation 1999;100:1992–2002. Eldar M, Ohad D, Bor A, Varda-Bloom N, Swanson DK, Battler A. A closed-chest pig model of sustained ventricular tachycardia. Pacing Clin Electrophysiol 1994;17:1603–9. Zuehldorff S. 3D phase sensitive IR prepared spoiled gradient echo technique with free breathing navigators: a tool for quantitative characterization of scar. In: Chung YC, Weale PJ, Jerecic R, editors. Berkeley, CA: Proceedings of the International Society of Magnetic Resonance in Medicine, 2007:683. Schilling RJ, Peters NS, Davies DW. Simultaneous endocardial mapping in the human left ventricle using a noncontact catheter: comparison of contact and reconstructed electrograms during sinus rhythm. Circulation 1998;98:887–98. Fenton F, Karma A. Vortex dynamics in three-dimensional continuous myocardium with fiber rotation: filament instability and fibrillation. Chaos 1998;8:20 – 47. Rush S, Larsen H. A practical algorithm for solving dynamic membrane equations. IEEE Trans Biomed Eng 1978;25:389 –92. Oliver RA, Krassowska W. Reproducing cardiac restitution properties using the Fenton-Karma membrane model. Ann Biomed Eng 2005; 33:907–11. Myerburg RJ, Epstein K, Gaide MS, et al. Cellular electrophysiology in acute and healed experimental myocardial infarction. Ann N Y Acad Sci 1982;382:90 –115. Buxton AE, Lee KL, Fisher JD, Josephson ME, Prystowsky EN, Hafley G. A randomized study of the prevention of sudden death in patients with coronary artery disease. N Engl J Med 1999;341: 1882–90. Bourke J, Young A, Richards D, Uther J. Reduction in incidence of inducible ventricular tachycardia after myocardial infarction by treatment with streptokinase during infarct evolution. J Am Coll Cardiol 1990;16:1703–10.

JACC Vol. 60, No. 5, 2012 July 31, 2012:423–30 26. Bolick D, Hackel D, Reimer K, Ideker R. Quantitative analysis of myocardial infarct structure in patients with ventricular tachycardia. Circulation 1986;74:1266 –79. 27. Jones-Collins B, Patterson R. Quantitative measurement of electrical instability as a function of myocardial infarct size in the dog. Am J Cardiol 1981;48:858 – 63. 28. Wilber DJ, Lynch JJ, Montgomery D, Lucchesi BR. Postinfarction sudden death: significance of inducible ventricular tachycardia and infarct size in a conscious canine model. Am Heart J 1985;109:8 –18. 29. Ohara T, Ohara K, Cao JM, et al. Increase wave break during ventricular fibrillation in the epicardial border zone of hearts with healed myocardial infarction. Circulation 2001;103:1465–72. 30. Ciaccio EJ, Ashikaga H, Kaba RA, et al. Model of reentrant ventricular tachycardia based on infarct border zone geometry predicts reentrant circuit features as determined by activation mapping. Heart Rhythm 2007;4:1034 – 45. 31. Perez-David E, Arenal A, Rubio-Guivernau JL, et al. Noninvasive identification of ventricular tachycardia-related conducting channels using contrast-enhanced magnetic resonance imaging in patients with chronic myocardial infarction: comparison of signal intensity scar mapping and endocardial voltage mapping. J Am Coll Cardiol 2011;57:184 –94. 32. Vigmond E, Vadakkumpadan F, Gurev V, et al. Toward predictive modelling of the electrophysiology of the heart. Exp Physiol 2009;94: 563–77. 33. Pop M, Sermesant M, Mansi T, et al. Correspondence between simple 3-D MRI-based computer models and in-vivo EP measurements in swine with chronic infarctions. IEEE Trans Biomed Eng 2011;58: 3483– 6. 34. Sermesant M, Rhode K, Sanchez-Ortiz GI, et al. Simulation of cardiac pathologies using an electromechanical biventricular model and XMR interventional imaging. Med Image Anal 2005;9:467– 80. Key Words: computer-based model y magnetic resonance imaging y myocardial infarction y ventricular tachycardia. APPENDIX

For supplemental data, please see the online version of this article.