Name: patient-specific modeling of the heart: estimation of ventricular fiber orientations
Uploaded: 2013-01-08T13:00:00
Description: Watch this Scientific Journal Video about Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations at JoVE.com

We thank Drs. Raimond Winslow, Elliot McVeigh, and Patrick Helm at Johns Hopkins University for providing the ex vivo datasets online.This research was supported by National Institutes of Health grant R01-HL082729, and National Science Foundation grant CBET-0933029.

1. Fiber Orientations Estimation
<ol>
 <li>Acquire structural MRI and DTMRI images of a normal adult human heart in diastole, at a resolution of 1 mm3. Using ImageJ, extract the ventricular myocardium from the atlas structural image by fitting, for each short-axis slice, closed splines through a set of landmark points placed along the epicardial and endocardial boundaries in the slice (Figure 2A &amp; Figure 2B). Perform the placement of landmark points manually for...

<ol>
	<li>Trayanova, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Whole+Heart+Modeling:+Applications+to+Cardiac+Electrophysiology+and+Electromechanics.">Whole Heart Modeling: Applications to Cardiac Electrophysiology and Electromechanics.</a> Circulation Research. 108, 113-128 .</li><li>Vadakkumpadan, F., Arevalo, H., Ceritoglu, C., Miller, M., Trayanova, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Image-Based+Estimation+of+Ventricular+Fiber+Orientations+for+Personalized+Modeling+of+Cardiac+Electrophysiology.">Image-Based Estimation of Ventricular Fiber Orientations for Personalized Modeling of Cardiac Electrophysiology.</a> IEEE Transactions on Medical Imaging. 31 (5), 1051-1060 .</li><li>Vadakkumpadan, F., Gurev, V., Constantino, J., Arevalo, H., Trayanova, N., Kerckhoffs, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+of+Whole-Heart+Electrophysiology+and+Mechanics:+Towards+Patient-Specific+Simulations.">Modeling of Whole-Heart Electrophysiology and Mechanics: Towards Patient-Specific Simulations.</a> Patient-Specific Modeling of the Cardiovascular System: Technology-Driven Personalized Medicine. , 145-165 (2010).</li><li>Buxton, A. 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This research demonstrates quantitatively that, in the absence of DTMRI, myocardial fiber orientations of normal and failing ventricles can be estimated from in-vivo images of their geometries for use in simulations of cardiac electrophysiology. The proposed methodology is demonstrated with in vivo CT data, but it is equally applicable to in vivo MR images of ventricular geometry, addressing the lack of ability to directly acquire patient fiber orientations. It is thus an important step ...

The computational approach is becoming central to the advancement of the understanding of the function of the heart in health and disease. State-of-the-art whole-heart models of electrophysiology and electromechanics are currently being used to study a wide range of phenomena, such as normal ventricular propagation, arrhythmia, defibrillation, electromechanical coupling, and cardiac resynchronization1. However, for the computational approach to be directly applicable in the clinical environment, it is imperative that the models be patient-specific, i.e. the models must be based on the specific architecture and electrophysiological or electromechanical properties of the patient's diseased heart. Simulation with such models will aid physicians to arrive at highly personalized decisions for electrophysiological interventions as well as prophylaxis, thereby dramatically improving cardiac health care2-4 .
Creation of realistic cardiac models requires the acquisition of the geometry and fiber structure of a patient heart. Fiber orientations determine directions of electrical propagation and strain distributions in the heart, and therefore acquiring them is essential to cardiac modeling5, 6.With recent advances in medical imaging, it is now feasible to obtain the geometry of a patient heart, includingstructural remodeling such as infarction, in vivo with high resolution using magnetic resonance imaging (MRI) and computed tomography (CT) technologies. However, there is no practical method for acquiring fiber structure of a patient heart in vivo. Diffusion tensor (DT) MRI7, 8, the only technique to acquire fiber orientations of the intact heart, is not widely available in vivo due to certain limitations 9. A brief description of the previous efforts to translate DTMRI to the clinical setting can be found elsewhere 2. Though methodologies such as rule-based assignment of fiber orientations offer alternatives to DTMRI, these methodologies have certain serious limitations 2, 10. Thus difficulties in acquiring cardiac fiber structure in vivo presently impede the application of electrophysiological and electromechanical cardiac simulations in clinical setting. The objective of this research was to directly address this need.
We hypothesized that ventricular fiber orientations of a heart can be accurately predicted given the geometry of the heart and an atlas, where the atlas is a heart whose geometry and fiber orientations are available. Accordingly, we used state of the art techniques to develop a methodology for estimation of cardiac fiber orientations in vivo, and tested the hypothesis in normal and failing canine ventricles2. The central idea of our fiber estimation methodology is to exploit similarities in fiber orientations, relative to geometry, between different hearts in order to approximate the fiber structure of a (target) heart for which only the geometry information is available. At the heart of our estimation methodology is the registration of the atlas geometry with the target geometry using large deformation diffeomorphic metric mapping (LDDMM) 11, and the morphing of atlas fiber orientations using preservation of principal components (PPD) 2, 12.The diffeomorphicproperty of LDDMM guarantees that the atlas does not "foldover" itself during deformation, thereby preserving the integrityof anatomical structures. Figure 1 illustrates the processing pipeline of our methodology. The protocol text section &sect;1 describes the various components of the pipeline by demonstrating how the estimation can be performed for an example patient. The numbers inside some of the blocks in Figure 1 refer to the corresponding subsections under section &sect;1 of the protocol text.
We evaluated the performance of the proposed methodology by quantifying the estimation error, and measuring the effect of this error onsimulations of cardiac electrophysiology, by computationally simulating local electrical activation maps as well as pseudo-electrocardiograms (pseudo-ECGs). Due to the unavailability of human hearts, the performance evaluation was conducted using canine hearts available from previous studies 13-15 . The estimation error was calculated by means of inclination angles 16, followingthe tradition of histology, where angular measurements are performedon tissue sections that are cut parallel to the epicardialsurface. Since the anglebetween the fiber direction and epicardial tangent plane is generallysmall 17, 18, the information loss in describing a fiberdirection entirely using its inclination angle is insignificant. For the computational simulations, image-based models were built as reported previously 19, 20, and cardiac tissue in the models was represented based on established mathematical techniques and experimental data 21-25 . Sinus rhythm was simulated by replicating activation originating from the Purkinje network26, and ventricular tachycardia, by an S1-S2 pacing protocol 27. Pseudo-ECGs were computed 28 and compared using the mean absolute deviation (MAD) metric 29.

<table border="1">
<tbody>
 <tr>
 <td>LDDMM</td>
 <td>Johns Hopkins University</td>
 <td></td>
 <td><a href="http://cis.jhu.edu/software/lddmm-volume/index.php" target="_blank">http://cis.jhu.edu/software/lddmm-volume/index.php</a></td>
 </tr>
 <tr>
 <td>MATLAB</td>
 <td>Mathworks, Inc.</td>
 <td>R2011b</td>
 <td> <a href="http://www.mathworks.com/products/matlab/" target="_blank">http://www.mathworks.com/products/matlab/</a></td>
 </tr>
 <tr>
 <td>ImageJ</td>
 <td>National Institutes of Health</td>
 <td></td>
 <td> <a href="http://rsbweb.nih.gov/ij/" target="_blank">http://rsbweb.nih.gov/ij/</a></td>
 </tr>
 <tr>
 <td>Tarantula</td>
 <td>CAE Software Solutions</td>
 <td></td>
 <td><a href="http://www.meshing.at/Spiderhome/Tarantula.html" target="_blank">http://www.meshing.at/Spiderhome/Tarantula.html</a></td>
 </tr>
 <tr>
 <td>CARP</td>
 <td>CardioSolv</td>
 <td></td>
 <td><a href="http://cardiosolv.com/" target="_blank">http://cardiosolv.com/</a></td>
 </tr>
 <tr>
 <td>Canine images</td>
 <td>Johns Hopkins University</td>
 <td></td>
 <td> <a href="http://www.ccbm.jhu.edu/research/DTMRIDS.php" target="_blank">http://www.ccbm.jhu.edu/research/DTMRIDS.php</a></td>
 </tr>
 </tbody>
</table>

patient-specific modeling of the heart: estimation of ventricular fiber orientations

Patient-specific simulations of heart (dys)function aimed at personalizing cardiac therapy are hampered by the absence of in vivo imaging technology for clinically acquiring myocardial fiber orientations. The objective of this project was to develop a methodology to estimate cardiac fiber orientations from in vivo images of patient heart geometries. An accurate representation of ventricular geometry and fiber orientations was reconstructed, respectively, from high-resolution ex vivo structural magnetic resonance (MR) and diffusion tensor (DT) MR images of a normal human heart, referred to as the atlas. Ventricular geometry of a patient heart was extracted, via semiautomatic segmentation, from an in vivo computed tomography (CT) image. Using image transformation algorithms, the atlas ventricular geometry was deformed to match that of the patient. Finally, the deformation field was applied to the atlas fiber orientations to obtain an estimate of patient fiber orientations. The accuracy of the fiber estimates was assessed using six normal and three failing canine hearts. The mean absolute difference between inclination angles of acquired and estimated fiber orientations was 15.4 &deg;. Computational simulations of ventricular activation maps and pseudo-ECGs in sinus rhythm and ventricular tachycardia indicated that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.The new insights obtained from the project will pave the way for the development of patient-specific models of the heart that can aid physicians in personalized diagnosis and decisions regarding electrophysiological interventions.

Figure 11, A-C displays streamlined visualizations of estimated as well as DTMRI-derived fiber orientations in normal and failing hearts. Qualitative examination shows that estimated fiber orientations align well with DTMRI-derived ones. Panel D illustrates, overlaid on the geometry of heart 1, the distribution of error in normal hearts' inclination angles, averaged across all five estimates. Panel E shows the mean distribution of error in failing hearts' inclination angles, overlaid on the geomet...

Watch this Scientific Journal Video about Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations at JoVE.com

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

A methodology to estimate ventricular fiber orientations from in vivo images of patient heart geometries for personalized modeling is described. Validation of the methodology performed using normal and failing canine hearts demonstrate that that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.

Patient-specific  simulations of heart (dys)function aimed at personalizing cardiac therapy are hampered  by the absence of in vivo imaging technology for ...

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Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

Mouse models have contributed significantly to understanding genetic and physiological factors involved in healthy cardiac function, how perturbations ...