Author + information
- Received January 3, 2012
- Revision received February 23, 2012
- Accepted March 12, 2012
- Published online July 31, 2012.
- Jason Ng, PhD⁎,
- Jason T. Jacobson, MD⁎,
- Justin K. Ng, MS⁎,
- David Gordon, MD, PhD⁎,
- Daniel C. Lee, MD⁎,
- James C. Carr, MD† and
- Jeffrey J. Goldberger, MD⁎,⁎ ()
- ↵⁎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
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.
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 cardioverter-defibrillators can significantly reduce mortality. Although current techniques and algorithms identify populations that are at 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) and spontaneous VT (11). The surrounding border zones, also known as gray zones, are thought to be a heterogeneous mix of viable and nonviable myocardium and have been shown to correlate with post-MI mortality (12), clinical VT (13), and VT inducibility (14). Infarct transmurality measured from MRI has also been shown to be a predictor of ventricular arrhythmias (15). Because of the strong correlation observed between the infarct morphologies observed on 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.
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 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 64-electrode 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.
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 tissue-conduction 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 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:(1)The ordinary differential equations describing the Fenton-Karma 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 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.
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.
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.
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.
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 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).
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 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 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.
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. 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.
The authors thank Kathleen Harris and Brandon Benefield for their assistance with this study.
For supplemental data, please see the online version of this article.
Virtual Electrophysiological Study in a 3-Dimensional Cardiac MRI Model of Porcine Myocardial Infarction
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.
- Abbreviations and Acronyms
- electrophysiological study
- left ventricle
- myocardial infarction
- magnetic resonance imaging
- sudden cardiac death
- virtual electrophysiological study
- ventricular tachycardia
- Received January 3, 2012.
- Revision received February 23, 2012.
- Accepted March 12, 2012.
- American College of Cardiology Foundation
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