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 every 10th slice in the image. Obtain the landmark points for the remaining slices by linearly interpolating the manually identified points, using MATLAB.Reconstruct the fiber orientations of the atlas heart by computing the primary eigenvectors of the DTs in the DTMRI image (Figure 2C).</li>
 <li>Acquire an image of the geometry of the patient heart in diastole using in vivo cardiac CT or MRI. Reconstruct the patient heart geometry from the image similarly to the way the atlas was built (Figure 3A &amp; Figure 3B). The patient image should be re-sampled prior to reconstruction such that the in-plane resolution is 1 mm2. Similarly, the number of slices for which landmarks are manually picked, and the interval of out-of-plane interpolation must be adjusted so that the segmented patient heart image has a slice thickness of 1 mm.</li>
 <li>Deform the atlas ventricular image to match the patient geometry image in two steps. In the first step, perform an affine transformation based on a set of thirteen landmarks points: the left ventricular (LV) apex, the two right ventricular (RV) insertion points at the base, the two RV insertion points midway between base and apex, and four sets of two points that evenly divide RV and LV epicardial contours at base, and midway between base and apex (Figure 4A &amp; Figure 4B). In the second step, deform the affine-transformed atlas ventricles further to match the patient geometry, using large deformation diffeomorphic metric mapping (LDDMM) (Figure 4C).</li>
 <li>Morph the DTMRI image of the atlas by re-positioning of image voxels and re-orientating the DTs according to the transformation matrix of the affine matching and the deformation field of the LDDMM transformation. Perform the re-orientation of the DTs using the preservation of principal directions (PPD) method.</li>
 <li>Obtain the estimate of the patient fiber orientations from the morphed atlas DTMRI image by computing the primary eigenvector of the DTs (Figure 5).</li>
</ol>
2. Measurement of Estimation Error
<ol>
 <li>Acquire ex vivo structural MR and DTMR images of six normal and three failing canine hearts, at a resolution of 312.5&times;312.5&times;800 &mu;m3. Here, heart failure should be generated in the canines via radiofrequency ablation of the left bundle-branch followed by 3 weeks of tachypacing at 210 min-1.</li>
 <li>Segment the ventricles from the canine hearts similarly to the human atlas heart, as described in &sect;1.1. Denote ventricles segmented from normal canine hearts as hearts 1 through 6, and those segmented from failing canine hearts as hearts 7 through 9 (Figure 6).</li>
 <li>Obtain five different estimates of ventricular fiber orientations of heart 1 by using each of hearts 2 to 6 as an atlas (Figure 7).</li>
 <li>Estimate fiber orientations for each of the failing ventricles using heart 1 as the atlas (Figure 8).</li>
 <li>Foreach data point in each set of estimated fiber orientations, compute the estimation error as|&theta;c-&theta;a| , where &theta;c and &theta;a are the inclination angles of estimated and acquired fiber orientations at that point, respectively.</li>
 <li>For each data point in each set of estimated fiber orientations, compute the acute angle betweenestimated and acquired fiber directions in three-dimensions (3D) by means of thevector dot product.</li>
</ol>
3. Measurement of the Effects of Estimation Error on Simulations
<ol>
 <li>From heart 1, construct six models, one with the DTMRI-acquired fiber orientations of heart 1 (referred to as model 1), and five with the five estimated fiber orientations datasets (models 2 to 6).For each of the three failing heart geometries, construct two ventricular models, one with the DTMRI-acquired fiber orientations and the other with the estimated fiber orientations. Here the spatial resolution of the models, computed in terms of the average edge length of the meshes, should be about 600 &mu;m. Denote the heart failure models with DTMRI-acquired fibers as models 7 to 9, and those with estimated fibers as models 10 to 12.In the models, use monodomain representation to describe the cardiac tissue, with governing equations:</li>
 </ol>
<img src="/files/ftp_upload/50125/50125eq1.jpg" alt="Equation 1" fo:content-width="2.18in" fo:src="/files/ftp_upload/50125/50125eq1highres.jpg" /> 
 where &sigma;bis the bulk conductivity tensor which is calculated from the bidomain conductivity tensors as described by Potse et al30; Vm is the transmembrane potential; Cm is the membrane specific capacitance; and Iion is the density of the transmembrane current, which in turn depends on Vm and a set of state variables &mu; describing the dynamics of ionic fluxes across the membrane.For Cm , use a value of 1 &mu; F/cm2. For &sigma;i in normal canine heart models, use longitudinal and transverse conductivity values of 0.34 S/m and 0.06 S/m, respectively. Represent llon by the Greenstein-Winslow ionic models of the canine ventricular myocyte. Decrease the electrical conductivities in canine heart failure ventricular models by 30% (Figure 9).
<ol start="2">
 <li>Using the software package CARP (CardioSolv, LLC), simulate sinus rhythm with all models. Induce reentrant ventricular tachycardia (VT) in the six failing models using an S1-S2 pacing protocol. Choose the timing between S1 and S2 to obtain sustained VT activity for 2 sec after S2 delivery. If VT is not induced for any S1-S2 timing, decrease the conductivities by up to 70% until VT was induced (Figure 10).</li>
 <li>For each simulation, calculate pseudo-ECGs by taking the difference of extracellular potentials between two points in an isotropic bath surrounding the hearts. Place the two points near the base of the heart separated by 18 cm, such that the line connecting them is perpendicular to the base-apex plane of the septum as illustrated in Figure 10. For each simulation with estimated fiber orientations, compute the MAD metric as</li>
</ol>
<img src="/files/ftp_upload/50125/50125eq2.jpg" alt="Equation 2" fo:content-width="2.77in" fo:src="/files/ftp_upload/50125/50125eq2highres.jpg" /> 
where X is the ECG waveform of the simulation with estimated fiber orientations, Y is the ECG waveform of thecorresponding simulation with acquired fiber orientations, X is the mean value of X,Y is the mean value of Y, and n is the length of X and Y.

<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. E., Lee, K. L., DiCarlo, L., Gold, M. R., Greer, G. S., Prystowsky, E. N., O'Toole, M. F., Tang, A., Fisher, J. D., Coromilas, J., Talajic, M., Hafley, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Electrophysiologic+testing+to+identify+patients+with+coronary+artery+disease+who+are+at+risk+for+sudden+death.+Multicenter+Unsustained+Tachycardia+Trial+Investigators.">Electrophysiologic testing to identify patients with coronary artery disease who are at risk for sudden death. Multicenter Unsustained Tachycardia Trial Investigators.</a> The New England Journal of Medicine. 342 (26), 1937-1945 (2000).</li><li>Wei, D., Okazaki, O., Harumi, K., Harasawa, E., Hosaka, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparative+simulation+of+excitation+and+body+surface+electrocardiogram+with+isotropic+and+anisotropic+computer+heart+models.">Comparative simulation of excitation and body surface electrocardiogram with isotropic and anisotropic computer heart models.</a> IEEE Transactions on Biomedical Engineering. 42 (4), 343-357 (1995).</li><li>Leon, L. J., Horacek, B. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computer+model+of+excitation+and+recovery+in+the+anisotropic+myocardium.+II.+Excitation+in+the+simplified+left+ventricle.">Computer model of excitation and recovery in the anisotropic myocardium. II. Excitation in the simplified left ventricle.</a> Journal of Electrocardiology. 24 (1), 17-31 (1991).</li><li>Rohmer, D., Sitek, A., Gullberg, G. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reconstruction+and+Visualization+of+Fiber+and+Laminar+Structure+in+the+Normal+Human+Heart+from+Ex+Vivo+Diffusion+Tensor+Magnetic+Resonance+Imaging+(DTMRI)+Data.">Reconstruction and Visualization of Fiber and Laminar Structure in the Normal Human Heart from Ex Vivo Diffusion Tensor Magnetic Resonance Imaging (DTMRI) Data.</a> Investigative Radiology. 42 (11), 777-789 (2007).</li><li>Daubert, J. P., Zareba, W., Hall, W. J., Schuger, C., Corsello, A., Leon, A. R., Andrews, M. L., McNitt, S., Huang, D. T., Moss, A. J., Investigators, M. I. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predictive+value+of+ventricular+arrhythmia+inducibility+for+subsequent+ventricular+tachycardia+or+ventricular+fibrillation+in+Multicenter+Automatic+Defibrillator+Implantation+Trial+(MADIT)+II+patients.">Predictive value of ventricular arrhythmia inducibility for subsequent ventricular tachycardia or ventricular fibrillation in Multicenter Automatic Defibrillator Implantation Trial (MADIT) II patients.</a> Journal of Americal College of Cardiology. 47 (1), 98-107 (2006).</li><li>Sosnovik, D. E., Wang, R., Dai, G., Reese, T. G., Wedeen, V. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diffusion+MR+tractography+of+the+heart.">Diffusion MR tractography of the heart.</a> Journal of Cardiovascular Magnetic Resonance. 11 (1), 47-61 (2009).</li><li>Sundar, H., Shen, D., Biros, G., Litt, H., Davatzikos, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimating+myocardial+fiber+orientations+by+template+warping.">Estimating myocardial fiber orientations by template warping.</a> Proc. IEEE International Symposium on Biomedical Imaging. , 73-76 (2006).</li><li>Beg, M. F., Helm, P. A., McVeigh, E., Miller, M. I., Winslow, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+Cardiac+Anatomy+Using+MRI.">Computational Cardiac Anatomy Using MRI.</a> Magnetic Resonance in Medicine. 52 (5), 1167-1174 (2004).</li><li>Alexander, D. C., Pierpaoli, C., Basser, P. J., Gee, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatial+Transformations+of+Diffusion+Tensor+Magnetic+Resonance+Images.">Spatial Transformations of Diffusion Tensor Magnetic Resonance Images.</a> IEEE Transactions on Medical Imaging. 20, 1131-1139 (2001).</li><li>Helm, P. A., Younes, L., Beg, M. F., Ennis, D. B., Leclercq, C., Faris, O. P., McVeigh, E., Kass, D., Miller, M. I., Winslow, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evidence+of+Structural+Remodeling+in+the+Dyssynchronous+Failing+Heart.">Evidence of Structural Remodeling in the Dyssynchronous Failing Heart.</a> Circulation Research. 98, 125-132 (2006).</li><li>Helm, P., Beg, M. F., Miller, M., Winslow, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+and+mapping+cardiac+fiber+and+laminar+architecture+using+diffusion+tensor+MR+imaging.">Measuring and mapping cardiac fiber and laminar architecture using diffusion tensor MR imaging.</a> Annals of the New York Academy of Sciences. 1047, 296-307 (2005).</li><li>Helm, P. A., Tseng, H. -. J., Younes, L., McVeigh, E. R., Winslow, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ex+vivo+3D+diffusion+tensor+imaging+and+quantification+of+cardiac+laminar+structure.">Ex vivo 3D diffusion tensor imaging and quantification of cardiac laminar structure.</a> Magnetic Resonance in Imaging. 54, 850-859 (2005).</li><li>Scollan, D. F., Holmes, A., Winslow, R., Forder, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Histological+validation+of+myocardial+microstructure+obtained+from+diffusion+tensor+magnetic+resonance+imaging.">Histological validation of myocardial microstructure obtained from diffusion tensor magnetic resonance imaging.</a> American Journal of Physiology - Heart and Circulatory Physiology. 275 (6), H2308-H2318 (1998).</li><li>Lombaert, H., Peyrat, J., Croisille, P., Rapacchi, S., Fanton, L., Cheriet, F., Clarysse, P., Magnin, I., Delingette, H., Ayache, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Human+Atlas+of+the+Cardiac+Fiber+Architecture:+Study+on+a+Healthy+Population.">Human Atlas of the Cardiac Fiber Architecture: Study on a Healthy Population.</a> IEEE Transactions on Medical Imaging. 31 (7), 1436-1447 (2012).</li><li>Streeter, D. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Gross morphology and fiber geometry of the heart. , (1979).</li><li>Vadakkumpadan, F., Rantner, L. J., Tice, B., Boyle, P., Prassl, A. J., Vigmond, E., Plank, G., 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+Models+of+Cardiac+Structure+with+Applications+in+Arrhythmia+and+Defibrillation+Studies.">Image-Based Models of Cardiac Structure with Applications in Arrhythmia and Defibrillation Studies.</a> Journal of Electrocardiology. 42, 151.e1-151.e10 (2009).</li><li>Plank, G., Zhou, L., Greenstein, J. L., Plank, G., Zhou, L., Greenstein, J. L., Cortassa, S., Winslow, R. L., O'Rourke, B., Trayanova, N. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=From+mitochondrial+ion+channels+to+arrhythmias+in+the+heart:+computational+techniques+to+bridge+the+spatio-temporal+scales.">From mitochondrial ion channels to arrhythmias in the heart: computational techniques to bridge the spatio-temporal scales.</a> Philosophical Transactions Series A, Mathematical, Physical, and Engineering Sciences. 366 (1879), 3381-3409 (2008).</li><li>Roberts, D. E., Scher, A. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effect+of+tissue+anisotropy+on+extracellular+potential+fields+in+canine+myocardium+in+situ.">Effect of tissue anisotropy on extracellular potential fields in canine myocardium in situ.</a> Circulation Research. 50, 342-351 (1982).</li><li>Greenstein, J., Wu, R., Po, S., Tomaselli, G. F., Winslow, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Role+of+the+Calcium-Independent+Transient+Outward+Current+I(to1)+in+Shaping+Action+Potential+Morphology+and+Duration.">Role of the Calcium-Independent Transient Outward Current I(to1) in Shaping Action Potential Morphology and Duration.</a> Circulation Research. 87, 1026-1033 (2000).</li><li>Winslow, R., Rice, J., Jafri, S., Marb&#225;n, E., O'Rourke, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanisms+of+altered+excitation-contraction+coupling+in+canine+tachycardia-induced+heart+failure,+II:+model+studies.">Mechanisms of altered excitation-contraction coupling in canine tachycardia-induced heart failure, II: model studies.</a> Circulation Research. 84 (5), 571-586 (1999).</li><li>Akar, F., Nass, R., Hahn, S., Cingolani, E., Shah, M., Hesketh, G., DiSilvestre, D., Tunin, R., Kass, D., Tomaselli, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+Changes+in+Conduction+Velocity+and+Gap+Junction+Properties+During+Development+of+Pacing-Induced+Heart+Failure.">Dynamic Changes in Conduction Velocity and Gap Junction Properties During Development of Pacing-Induced Heart Failure.</a> American Journal of Physiology - Heart and Circulatory Physiology. 293 (2), H1223-H1230 (2007).</li><li>Gurev, V., Constantino, J., Rice, J. J., 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=Distribution+of+Electromechanical+Delay+in+the+Ventricles:Insights+from+a+3D+Electromechanical+Model+of+the+Heart.">Distribution of Electromechanical Delay in the Ventricles:Insights from a 3D Electromechanical Model of the Heart.</a> Biophysical Journal. 99 (3), 745-754 .</li><li>Ten Tusscher, K. H. W. J., Hren, R., Panfilov, A. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Organization+of+Ventricular+Fibrillation+in+the+Human+Heart.">Organization of Ventricular Fibrillation in the Human Heart.</a> Circulation Research. 100 (12), e87-e101 (2007).</li><li>Gima, K., Rudy, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ionic+Current+Basis+of+Electrocardiographic+Waveforms.">Ionic Current Basis of Electrocardiographic Waveforms.</a> Circulation Research. 90, 889-896 (2002).</li><li>Gerstenfeld, E., Dixit, S., Callans, D., Rajawat, Y., Rho, R., Marchlinski, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+comparison+of+spontaneous+and+paced+12-lead+electrocardiogram+during+right+ventricular+outflow+tract+ventricular+tachycardia.">Quantitative comparison of spontaneous and paced 12-lead electrocardiogram during right ventricular outflow tract ventricular tachycardia.</a> Journal of Americal College of Cardiology. 41 (11), 2046-2053 (2003).</li><li>Potse, M., Dube, B., Richer, J., Vinet, A., Gulrajani, R. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparison+of+monodomain+and+bidomain+reaction-diffusion+models+for+action+potential+propagation+in+the+human+heart.">A comparison of monodomain and bidomain reaction-diffusion models for action potential propagation in the human heart.</a> IEEE Transactions on Biomedical Engineering. 53 (12), 2425-2435 (2006).</li><li>Peyrat, J. -. M., Sermesant, M., Pennec, X., Delingette, H., Chenyang, X., McVeigh, E. R., Ayache, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Computational+Framework+for+the+Statistical+Analysis+of+Cardiac+Diffusion+Tensors:+Application+to+a+Small+Database+of+Canine+Hearts.">A Computational Framework for the Statistical Analysis of Cardiac Diffusion Tensors: Application to a Small Database of Canine Hearts.</a> IEEE Transactions on Medical Imaging. 26, 1500-1514 (2007).</li><li>Chen, J., Song, S. -. K., Liu, W., McLean, M., Allen, S. J., Tan, J., Wickline, S. A., Yu, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Remodeling+of+cardiac+fiber+structure+after+infarction+in+rats+quantified+with+diffusion+tensor+MRI.">Remodeling of cardiac fiber structure after infarction in rats quantified with diffusion tensor MRI.</a> American Journal of Physiology - Heart and Circulatory Physiology. 285 (3), H946-H954 (2003).</li><li>Stecker, E. C., Chugh, S. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+sudden+cardiac+death:+next+steps+in+pursuit+of+effective+methodology.">Prediction of sudden cardiac death: next steps in pursuit of effective methodology.</a> Journal of Interventional Cardiac Electrophysiolog. 31 (2), 101-107 (2011).</li></ol>

No conflicts of interest declared.

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 ...

patient-specific-modeling-heart-estimation-ventricular-fiber

Research

JoVE Journal

Bioengineering

13.6K Views.  Johns Hopkins University. 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.

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

High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging

Many biological and clinical studies require the longitudinal study and analysis of morphology and function with cellular level resolution. Traditionally, multiple experiments are run in parallel, with individual samples removed from the study at sequential time points for evaluation by light microscopy. Several intravital techniques have been developed, with confocal, multiphoton, and second harmonic microscopy all demonstrating their ability to be used for imaging in situ 1. With these systems, however, the required infrastructure is complex and expensive, involving scanning laser systems and complex light sources. Here we present a protocol for the design and assembly of a high-resolution microendoscope which can be built in a day using off-the-shelf components for under US$5,000. The platform offers flexibility in terms of image resolution, field-of-view, and operating wavelength, and we describe how these parameters can be easily modified to meet the specific needs of the end user.
We and others have explored the use of the high-resolution microendoscope (HRME) in in vitro cell culture 2-5, in excised 6 and living animal tissues 2,5, and in human tissues in vivo 2,7. Users have reported the use of several different fluorescent contrast agents, including proflavine 2-4, benzoporphyrin-derivative monoacid ring A (BPD-MA) 5, and fluoroscein 6,7, all of which have received full, or investigational approval from the FDA for use in human subjects. High-resolution microendoscopy, in the form described here, may appeal to a wide range of researchers working in the basic and clinical sciences. The technique offers an effective and economical approach which complements traditional benchtop microscopy, by enabling the user to perform high-resolution, longitudinal imaging in situ.

High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging

In many biological and clinical situations it is advantageous to study cellular processes as they evolve in their native microenvironment. Here we describe the assembly and use of a low-cost fiber-optic microscope which can provide real time imaging in cell culture, animal studies, and clinical patient studies.

Many biological and clinical studies require the longitudinal study and analysis of morphology and function with cellular level resolution.  ...

Multiparametric Optical Mapping of the Langendorff-perfused Rabbit Heart

Optical imaging and fluorescent probes have significantly advanced research methodology in the field of cardiac electrophysiology in ways that could not have been accomplished by other approaches1. With the use of the calcium- and voltage-sensitive dyes, optical mapping allows measurement of transmembrane action potentials and calcium transients with high spatial resolution without the physical contact with the tissue. This makes measurements of the cardiac electrical activity possible under many conditions where the use of electrodes is inconvenient or impossible1. For example, optical recordings provide accurate morphological changes of membrane potential during and immediately after stimulation and defibrillation, while conventional electrode techniques suffer from stimulus-induced artifacts during and after stimuli due to electrode polarization1. 
The Langendorff-perfused rabbit heart is one of the most studied models of human heart physiology and pathophysiology. Many types of arrhythmias observed clinically could be recapitulated in the rabbit heart model. It was shown that wave patterns in the rabbit heart during ventricular arrhythmias, determined by effective size of the heart and the wavelength of reentry, are very similar to that in the human heart2. It was also shown that critical aspects of excitation-contraction (EC) coupling in rabbit myocardium, such as the relative contribution of sarcoplasmic reticulum (SR), is very similar to human EC coupling3. Here we present the basic procedures of optical mapping experiments in Langendorff-perfused rabbit hearts, including the Langendorff perfusion system setup, the optical mapping systems setup, the isolation and cannulation of the heart, perfusion and dye-staining of the heart, excitation-contraction uncoupling, and collection of optical signals. These methods could be also applied to the heart from species other than rabbit with adjustments to flow rates, optics, solutions, etc.
Two optical mapping systems are described. The panoramic mapping system is used to map the entire epicardium of the rabbit heart4-7. This system provides a global view of the evolution of reentrant circuits during arrhythmogenesis and defibrillation, and has been used to study the mechanisms of arrhythmias and antiarrhythmia therapy8,9. The dual mapping system is used to map the action potential (AP) and calcium transient (CaT) simultaneously from the same field of view10-13. This approach has enhanced our understanding of the important role of calcium in the electrical alternans and the induction of arrhythmia14-16.

This article describes the basic procedures for conducting optical mapping experiments in the Langendorff-perfused rabbit heart using the panoramic imaging system, and the dual (voltage and calcium) imaging modality.

Optical imaging and fluorescent probes have significantly advanced research methodology in the field of cardiac electrophysiology in ways that could not ...

Targeting Neuronal Fiber Tracts for Deep Brain Stimulation Therapy Using Interactive, Patient-Specific Models

Deep brain stimulation (DBS), which involves insertion of an electrode to deliver stimulation to a localized brain region, is an established therapy for movement disorders and is being applied to a growing number of disorders. Computational modeling has been successfully used to predict the clinical effects of DBS; however, there is a need for novel modeling techniques to keep pace with the growing complexity of DBS devices. These models also need to generate predictions quickly and accurately. The goal of this project is to develop an image processing pipeline to incorporate structural magnetic resonance imaging (MRI) and diffusion weighted imaging (DWI) into an interactive, patient specific model to simulate the effects of DBS. A virtual DBS lead can be placed inside of the patient model, along with active contacts and stimulation settings, where changes in lead position or orientation generate a new finite element mesh and solution of the bioelectric field problem in near real-time, a timespan of approximately 10 seconds. This system also enables the simulation of multiple leads in close proximity to allow for current steering by varying anodes and cathodes on different leads. The techniques presented in this paper reduce the burden of generating and using computational models while providing meaningful feedback about the effects of electrode position, electrode design, and stimulation configurations to researchers or clinicians who may not be modeling experts.

The goal of this project is to develop an interactive, patient-specific modeling pipeline to simulate the effects of deep brain stimulation in near real-time and provide meaningful feedback as to how these devices influence neural activity in the brain.

Deep brain stimulation (DBS), which involves insertion of an electrode to deliver stimulation to a localized brain region, is an established therapy for ...

Light-sheet Fluorescence Microscopy for the Study of the Murine Heart

Light-sheet fluorescence microscopy has been widely used for rapid image acquisition with a high axial resolution from micrometer to millimeter scale. Traditional light-sheet techniques involve the use of a single illumination beam directed orthogonally at sample tissue. Images of large samples that are produced using a single illumination beam contain stripes or artifacts and suffer from a reduced resolution due to the scattering and absorption of light by the tissue. This study uses a dual-sided illumination beam and a simplified CLARITY optical clearing technique for the murine heart. These techniques allow for deeper imaging by removing lipids from the heart and produce a large field of imaging, greater than 10 x 10 x 10 mm3. As a result, this strategy enables us to quantify the ventricular dimensions, track the cardiac lineage, and localize the spatial distribution of cardiac-specific proteins and ion-channels from the post-natal to adult mouse hearts with sufficient contrast and resolution.

This study uses a dual-sided illumination light-sheet fluorescence microscopy (LSFM) technique combined with optical clearing to study the murine heart.

Light-sheet fluorescence microscopy has been widely used for rapid image acquisition with a high axial resolution from micrometer to millimeter scale. ...

Biaxial Mechanical Characterizations of Atrioventricular Heart Valves

Extensive biaxial mechanical testing of the atrioventricular heart valve leaflets can be utilized to derive&#160;optimal parameters used in constitutive models, which provide a mathematical representation of the mechanical function of those structures. This presented biaxial mechanical testing protocol involves (i) tissue acquisition, (ii) the preparation of tissue specimens, (iii) biaxial mechanical testing, and (iv) postprocessing of the acquired data. First, tissue acquisition requires obtaining porcine or ovine hearts from a local Food and Drug Administration-approved abattoir for later dissection to retrieve the valve leaflets. Second, tissue preparation requires using tissue specimen cutters on the leaflet tissue to extract a clear zone for testing. Third, biaxial mechanical testing of the leaflet specimen requires the use of a commercial biaxial mechanical tester, which consists of force-controlled, displacement-controlled, and stress-relaxation testing protocols to characterize the leaflet tissue&#39;s mechanical properties. Finally, post-processing requires the use of data image correlation techniques and force and displacement readings to summarize the tissue&#39;s mechanical behaviors in response to external loading. In general, results from biaxial testing demonstrate that the leaflet tissues yield a nonlinear, anisotropic mechanical response. The presented biaxial testing procedure is advantageous to other methods since the method presented here allows for a more comprehensive characterization of the valve leaflet tissue under one unified testing scheme, as opposed to separate testing protocols on different tissue specimens. The proposed testing method has its limitations in that shear stress is potentially present in the tissue sample. However, any potential shear is presumed negligible.

This protocol involves characterizations of atrioventricular valve leaflets with force-controlled, displacement-controlled, and stress-relaxation biaxial mechanical testing procedures. Results acquired with this protocol can be used for constitutive model development to simulate the mechanical behavior of functioning valves under a finite element simulation framework.

Extensive biaxial mechanical testing of the atrioventricular heart valve leaflets can be utilized to derive&#160;optimal parameters used in constitutive ...

Optical Imaging of Isolated Murine Ventricular Myocytes

The ability to isolate adult cardiac myocytes has permitted researchers to study a variety of cardiac pathologies at the single cell level. While advances in calcium sensitive dyes have permitted the robust optical recording of single cell calcium dynamics, recording of robust transmembrane optical voltage signals has remained difficult. Arguably, this is because of the low single to noise ratio, phototoxicity, and photobleaching of traditional potentiometric dyes. Therefore, single cell voltage measurements have long been confined to the patch clamp technique which while the gold standard, is technically demanding and low throughput. However, with the development of novel potentiometric dyes, large, fast optical responses to changes in voltage can be obtained with little to no phototoxicity and photobleaching. This protocol describes in detail how to isolate adult murine myocytes which can be used for cellular shortening, calcium, and optical voltage measurements. Specifically, the protocol describes how to use a ratiometric calcium dye, a single-excitation calcium dye, and a single excitation voltage dye. This approach can be used to assess the cardiotoxicity and arrhythmogenicity of various chemical agents. While phototoxicity is still an issue at the single cell level, methodology is discussed on how to reduce it.

We present the methodology for the isolation of murine myocytes and how to obtain voltage or calcium traces simultaneously with sarcomere shortening traces using fluorescence photometry with simultaneous digital cell geometry measurements.

The ability to isolate adult cardiac myocytes has permitted researchers to study a variety of cardiac pathologies at the single cell level. While advances ...

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography

Three-dimensional (3D) quantification of the left ventricle (LV) provides significant added value in terms of diagnostic accuracy and precise risk stratification in various cardiac disorders. Recently, 3D echocardiography became available in routine cardiology practice; however, high-quality image acquisition and subsequent analysis have a steep learning curve. The present article aims to guide the reader through a detailed 3D protocol by presenting tips and tricks and also by highlighting the potential pitfalls to facilitate the widespread but technically sound use of this important technique concerning the LV. First and foremost, we show the acquisition of a high-quality 3D dataset with optimal spatial and temporal resolution. Then, we present the analytical steps toward a detailed quantification of the LV by using one of the most widely applied built-in software. We will quantify LV volumes, sphericity, mass and also systolic function by measuring ejection fraction and myocardial deformation (longitudinal and circumferential strain). We will discuss and provide clinical examples about the essential scenarios where the transition from a conventional echocardiographic approach to a 3D-based quantification is highly recommended.

In this article, we provide a step-by-step acquisition and analysis protocol for the volumetric assessment and speckle-tracking analysis of the left ventricle by 3D echocardiography, particularly focusing on practical aspects that maximize the feasibility of this technique.

Three-dimensional (3D) quantification of the left ventricle (LV) provides significant added value in terms of diagnostic accuracy and precise risk ...

Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

Phantoms are essential tools for clinical training, surgical planning and the development of novel medical devices. However, it is challenging to create anatomically accurate head phantoms with realistic brain imaging properties because standard fabrication methods are not optimized to replicate any patient-specific anatomical detail and 3D printing materials are not optimized for imaging properties. In order to test and validate a novel navigation system for use during brain tumor surgery, an anatomically accurate phantom with realistic imaging and mechanical properties was required. Therefore, a phantom was developed using real patient data as input and 3D printing of molds to fabricate a patient-specific head phantom comprising the skull, brain and tumor with both ultrasound and X-ray contrast. The phantom also had mechanical properties that allowed the phantom tissue to be manipulated in a similar manner to how human brain tissue is handled during surgery. The phantom was successfully tested during a surgical simulation in a virtual operating room.
The phantom fabrication method uses commercially available materials and is easy to reproduce. The 3D printing files can be readily shared, and the technique can be adapted to encompass many different types of tumor.

This protocol describes the fabrication of a patient specific skull, brain and tumor phantom. It uses 3D printing to create molds, and polyvinyl alcohol (PVA-c) is used as the tissue mimicking material.

Phantoms are essential tools for clinical training, surgical planning and the development of novel medical devices. However, it is challenging to create ...

Evaluating Regional Pulmonary Deposition using Patient-Specific 3D Printed Lung Models

Development of targeted therapies for pulmonary diseases is limited by the availability of preclinical testing methods with the ability to predict regional aerosol delivery. Leveraging 3D printing to generate patient-specific lung models, we outline the design of a high-throughput, in vitro experimental setup for quantifying lobular pulmonary deposition. This system is made with a combination of commercially available and 3D printed components and allows the flow rate through each lobe of the lung to be independently controlled. Delivery of fluorescent aerosols to each lobe is measured using fluorescence microscopy. This protocol has the potential to promote the growth of personalized medicine for respiratory diseases through its ability to model a wide range of patient demographics and disease states. Both the geometry of the 3D printed lung model and the air flow profile setting can be easily modulated to reflect clinical data for patients with varying age, race, and gender. Clinically relevant drug delivery devices, such as the endotracheal tube shown here, can be incorporated into the testing setup to more accurately predict a device&#8217;s capacity to target therapeutic delivery to a diseased region of the lung. The versatility of this experimental setup allows it to be customized to reflect a multitude of inhalation conditions, enhancing the rigor of preclinical therapeutic testing.

We present a high-throughput, in vitro method for quantifying regional pulmonary deposition at the lobe level using CT scan-derived, 3D printed lung models with tunable air flow profiles.

Development of targeted therapies for pulmonary diseases is limited by the availability of preclinical testing methods with the ability to predict ...

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 result in pathology, and how myocardial diseases may be treated. Cardiovascular magnetic resonance imaging (CMR) has become an indispensable tool for a comprehensive in vivo assessment of cardiac anatomy and function. This protocol shows detailed measurements of mouse heart left ventricular function, myocardial strain, and hemodynamic forces using 7-Tesla CMR. First, animal preparation and positioning in the scanner are demonstrated. Survey scans are performed for planning imaging slices in various short- and long-axis views. A series of prospective ECG-triggered short-axis (SA) movies (or CINE images) are acquired covering the heart from apex to base, capturing end-systolic and end-diastolic phases. Subsequently, single-slice, retrospectively gated CINE images are acquired in a midventricular SA view, and in 2-, 3-, and 4-chamber views, to be reconstructed into high-temporal resolution CINE images using custom-built and open-source software. CINE images are subsequently analyzed using dedicated CMR image analysis software.
Delineating endomyocardial and epicardial borders in SA end-systolic and end-diastolic CINE images allows for the calculation of end-systolic and end-diastolic volumes, ejection fraction, and cardiac output. The midventricular SA CINE images are delineated for all cardiac time frames to extract a detailed volume-time curve. Its time derivative allows for the calculation of the diastolic function as the ratio of the early filling and atrial contraction waves. Finally, left ventricular endocardial walls in the 2-, 3-, and 4-chamber views are delineated using feature-tracking, from which longitudinal myocardial strain parameters and left ventricular hemodynamic forces are calculated. In conclusion, this protocol provides detailed in vivo quantification of the mouse cardiac parameters, which can be used to study temporal alterations in cardiac function in various mouse models of heart disease.

This study describes a comprehensive cardiovascular magnetic resonance imaging (CMR) protocol to quantify the left ventricular functional parameters of the mouse heart. The protocol describes the acquisition, post-processing, and analysis of the CMR images as well as assessment of different cardiac functional parameters.

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