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.

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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><tbody><tr><td>LDDMM</td><td>Johns Hopkins University</td><td></td><td>&lt;a href=&quot;http://cis.jhu.edu/software/lddmm-volume/index.php&quot; target=&quot;_blank&quot;&gt;http://cis.jhu.edu/software/lddmm-volume/index.php&lt;/a&gt;</td></tr><tr><td>MATLAB</td><td>Mathworks, Inc.</td><td>R2011b</td><td> &lt;a href=&quot;http://www.mathworks.com/products/matlab/&quot; target=&quot;_blank&quot;&gt;http://www.mathworks.com/products/matlab/&lt;/a&gt;</td></tr><tr><td>ImageJ</td><td>National Institutes of Health</td><td></td><td> &lt;a href=&quot;http://rsbweb.nih.gov/ij/&quot; target=&quot;_blank&quot;&gt;http://rsbweb.nih.gov/ij/&lt;/a&gt;</td></tr><tr><td>Tarantula</td><td>CAE Software Solutions</td><td></td><td>&lt;a href=&quot;http://www.meshing.at/Spiderhome/Tarantula.html&quot; target=&quot;_blank&quot;&gt;http://www.meshing.at/Spiderhome/Tarantula.html&lt;/a&gt;</td></tr><tr><td>CARP</td><td>CardioSolv</td><td></td><td>&lt;a href=&quot;http://cardiosolv.com/&quot; target=&quot;_blank&quot;&gt;http://cardiosolv.com/&lt;/a&gt;</td></tr><tr><td>Canine images</td><td>Johns Hopkins University</td><td></td><td> &lt;a href=&quot;http://www.ccbm.jhu.edu/research/DTMRIDS.php&quot; target=&quot;_blank&quot;&gt;http://www.ccbm.jhu.edu/research/DTMRIDS.php&lt;/a&gt;</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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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 Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

Atrial fibrillation (AF) is a complex cardiac arrhythmia with high morbidity and mortality.1,2 It is the most common sustained cardiac rhythm disturbance seen in clinical practice and its prevalence is expected to increase in the coming years.3 Increased intra-atrial pressure and dilatation have been long recognized to lead to AF,1,4 which highlights the relevance of using animal models and stretch to study AF dynamics. Understanding the mechanisms underlying AF requires visualization of the cardiac electrical waves with high spatial and temporal resolution. While high-temporal resolution can be achieved by conventional electrical mapping traditionally used in human electrophysiological studies, the small number of intra-atrial electrodes that can be used simultaneously limits the spatial resolution and precludes any detailed tracking of the electrical waves during the arrhythmia. The introduction of optical mapping in the early 90's enabled wide-field characterization of fibrillatory activity together with sub-millimeter spatial resolution in animal models5,6 and led to the identification of rapidly spinning electrical wave patterns (rotors) as the sources of the fibrillatory activity that may occur in the ventricles or the atria.7-9 Using combined time- and frequency-domain analyses of optical mapping it is possible to demonstrate discrete sites of high frequency periodic activity during AF, along with frequency gradients between left and right atrium. The region with fastest rotors activates at the highest frequency and drives the overall arrhythmia.10,11 The waves emanating from such rotor interact with either functional or anatomic obstacles in their path, resulting in the phenomenon of fibrillatory conduction.12 Mapping the endocardial surface of the posterior left atrium (PLA) allows the tracking of AF wave dynamics in the region with the highest rotor frequency. Importantly, the PLA is the region where intracavitary catheter-based ablative procedures are most successful terminating AF in patients,13 which underscores the relevance of studying AF dynamics from the interior of the left atrium. Here we describe a sheep model of acute stretch-induced AF, which resembles some of the characteristics of human paroxysmal AF. Epicardial mapping on the left atrium is complemented with endocardial mapping of the PLA using a dual-channel rigid borescope c-mounted to a CCD camera, which represents the most direct approach to visualize the patterns of activation in the most relevant region for AF maintenance.

This report provides a detailed description of the methodology and results of simultaneous endocardial and epicardial optical mapping of electrical excitation in the intact left atrium of a Langendorff-perfused sheep heart during stretch-induced atrial fibrillation.

 Atrial fibrillation (AF) is a complex cardiac arrhythmia with high morbidity and mortality.1,2 It is the most common sustained cardiac rhythm disturbance ...

Medicine

Anatomical Reconstructions of the Human Cardiac Venous System using Contrast-computed Tomography of Perfusion-fixed Specimens

A detailed understanding of the complexity and relative variability within the human cardiac venous system is crucial for the development of cardiac devices that require access to these vessels. For example, cardiac venous anatomy is known to be one of the key limitations for the proper delivery of cardiac resynchronization therapy (CRT)1 Therefore, the development of a database of anatomical parameters for human cardiac venous systems can aid in the design of CRT delivery devices to overcome such a limitation. In this research project, the anatomical parameters were obtained from 3D reconstructions of the venous system using contrast-computed tomography (CT) imaging and modeling software (Materialise, Leuven, Belgium). The following parameters were assessed for each vein: arc length, tortuousity, branching angle, distance to the coronary sinus ostium, and vessel diameter.
	CRT is a potential treatment for patients with electromechanical dyssynchrony. Approximately 10-20% of heart failure patients may benefit from CRT2. Electromechanical dyssynchrony implies that parts of the myocardium activate and contract earlier or later than the normal conduction pathway of the heart. In CRT, dyssynchronous areas of the myocardium are treated with electrical stimulation. CRT pacing typically involves pacing leads that stimulate the right atrium (RA), right ventricle (RV), and left ventricle (LV) to produce more resynchronized rhythms. The LV lead is typically implanted within a cardiac vein, with the aim to overlay it within the site of latest myocardial activation. 
	We believe that the models obtained and the analyses thereof will promote the anatomical education for patients, students, clinicians, and medical device designers. The methodologies employed here can also be utilized to study other anatomical features of our human heart specimens, such as the coronary arteries. To further encourage the educational value of this research, we have shared the venous models on our free access website: <a href="http://www.vhlab.umn.edu/atlas" target="_blank">www.vhlab.umn.edu/atlas</a>.

The objective of this research is to recreate and then access the anatomy of the human cardiac venous system using 3D reconstructions generated from contrast-computed tomography scans.

A detailed  understanding of the complexity and relative variability within the human  cardiac venous system is crucial for the development of cardiac ...

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Scientific efforts in the field of computational modeling of cardiovascular diseases have largely focused on heart failure with reduced ejection fraction (HFrEF), broadly overlooking heart failure with preserved ejection fraction (HFpEF), which has more recently become a dominant form of heart failure worldwide. Motivated by the paucity of HFpEF in silico representations, two distinct computational models are presented in this paper to simulate the hemodynamics of HFpEF resulting from left ventricular pressure overload. First, an object-oriented lumped-parameter model was developed using a numerical solver. This model is based on a zero-dimensional (0D) Windkessel-like network, which depends on the geometrical and mechanical properties of the constitutive elements and offers the advantage of low computational costs. Second, a finite element analysis (FEA) software package was utilized for the implementation of a multidimensional simulation. The FEA model combines three-dimensional (3D) multiphysics models of the electro-mechanical cardiac response, structural deformations, and fluid cavity-based hemodynamics and utilizes a simplified lumped-parameter model to define the flow exchange profiles among different fluid cavities. Through each approach, both the acute and chronic hemodynamic changes in the left ventricle and proximal vasculature resulting from pressure overload were successfully simulated. Specifically, pressure overload was modeled by reducing the orifice area of the aortic valve, while chronic remodeling was simulated by reducing the compliance of the left ventricular wall. Consistent with the scientific and clinical literature of HFpEF, results from both models show (i) an acute elevation of transaortic pressure gradient between the left ventricle and the aorta and a reduction in the stroke volume and (ii) a chronic decrease in the end-diastolic left ventricular volume, indicative of diastolic dysfunction. Finally, the FEA model demonstrates that stress in the HFpEF myocardium is remarkably higher than in the healthy heart tissue throughout the cardiac cycle.

This work introduces two computational models of heart failure with preserved ejection fraction based on a lumped-parameter approach and finite element analysis. These models are used to evaluate the changes in the hemodynamics of the left ventricle and related vasculature induced by pressure overload and diminished ventricular compliance.

Scientific efforts in the field of computational modeling of cardiovascular diseases have largely focused on heart failure with reduced ejection fraction ...

Engineering

Creating a Structurally Realistic Finite Element Geometric Model of a Cardiomyocyte to Study the Role of Cellular Architecture in Cardiomyocyte Systems Biology

With the advent of three-dimensional (3D) imaging technologies such as electron tomography, serial-block-face scanning electron microscopy and confocal microscopy, the scientific community has unprecedented access to large datasets at sub-micrometer resolution that characterize the architectural remodeling that accompanies changes in cardiomyocyte function in health and disease. However, these datasets have been under-utilized for&#160;investigating the role of cellular architecture remodeling in cardiomyocyte function. The purpose of this protocol is to outline how to create an accurate finite element model of a cardiomyocyte using high resolution electron microscopy and confocal microscopy images. A detailed and accurate model of cellular architecture has significant potential to provide new insights into cardiomyocyte biology, more than experiments alone can garner. The power of this method lies in its ability to computationally fuse information from two disparate imaging modalities of cardiomyocyte ultrastructure to develop one unified and detailed model of the cardiomyocyte. This protocol outlines steps to integrate electron tomography and confocal microscopy images of adult male Wistar (name for a specific breed of albino rat) rat cardiomyocytes to develop a half-sarcomere finite element model of the cardiomyocyte. The procedure generates a 3D finite element model that contains an accurate, high-resolution depiction (on the order of ~35 nm) of the distribution of mitochondria, myofibrils and ryanodine receptor clusters that release the necessary calcium for cardiomyocyte contraction from the sarcoplasmic reticular network (SR) into the myofibril and cytosolic compartment. The model generated here as an illustration does not incorporate details of the transverse-tubule architecture or the sarcoplasmic reticular network and is therefore a minimal model of the cardiomyocyte. Nevertheless, the model can already be applied in simulation-based investigations into the role of cell structure in calcium signaling and mitochondrial bioenergetics, which is illustrated and discussed using two case studies that are presented following the detailed protocol.

This protocol outlines a novel method to create a spatially detailed finite element model of the intracellular architecture of cardiomyocytes from electron microscopy and confocal microscopy images. The power of this spatially detailed model is demonstrated using case studies in calcium signaling and bioenergetics.

With the advent of three-dimensional (3D) imaging technologies such as electron tomography, serial-block-face scanning electron microscopy and confocal ...

 IVUS-Based Computational Method to Quantify Coronary Mechanical Properties

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Quantifying the mechanical properties of coronary arterial walls could provide meaningful information for the diagnosis, management, and treatment of coronary artery diseases. Since patient-specific coronary samples are not available for patients requiring continuous monitoring, direct experimental testing of vessel material properties becomes impossible. Current coronary models typically use material parameters from available literature, leading to significant mechanical stress/strain calculation errors. Here, we would introduce a finite element model-based updating approach (FEMBUA) to quantify patient-specific in vivo material properties of coronary arteries based on medical images. In vivo cine intravascular ultrasound (IVUS) and virtual histology (VH)-IVUS images of coronary arteries were acquired from a patient with coronary artery disease. Cine IVUS images showing the vascular movement over one cardiac cycle were segmented, and two IVUS frames with maximum and minimum lumen circumferences were selected to represent the coronary geometry under systolic and diastolic pressure conditions, respectively. VH-IVUS image was also segmented to obtain the vessel contours, and a layer thickness of 0.05 cm was added to the VH-IVUS contours to reconstruct the coronary geometry. A computational finite element model was created with an anisotropic Mooney-Rivlin material model used to describe the vessel's mechanical properties and pulsatile blood pressure conditions prescribed to the coronary luminal surface to make it contract and expand. Then, an iterative updating approach was employed to determine the material parameters of the anisotropic Mooney-Rivlin model by matching minimum and maximum lumen circumferences from the computational finite element model with those from cine IVUS images. This image-based finite element model-based updating approach could be successfully extended to determine the material properties of arterial walls in various vascular beds and holds the potential for risk assessment of cardiovascular diseases.

In vivo cine intravascular ultrasound images show the coronary cross-sectional movement corresponding to different pressure loading conditions. Based on a finite element model, an iterative scheme was employed to determine the patient-specific mechanical properties of coronary arteries in vivo by matching coronary motion from the computational model and medical images.

Quantifying the mechanical properties of coronary arterial walls could provide meaningful information for the diagnosis, management, and treatment of ...

Preclinical Cardiac Electrophysiology Assessment by Dual Voltage and Calcium Optical Mapping of Human Organotypic Cardiac Slices

Human cardiac slice preparations have recently been developed as a platform for human physiology studies and therapy testing to bridge the gap between animal and clinical trials. Numerous animal and cell models have been used to examine the effects of drugs, yet these responses often differ in humans. Human cardiac slices offer an advantage for drug testing in that they are directly derived from viable human hearts. In addition to having preserved multicellular structures, cell-cell coupling, and extracellular matrix environments, human cardiac tissue slices can be used to directly test the effect of innumerable drugs on adult human cardiac physiology. What distinguishes this model from other heart preparations, such as whole hearts or wedges, is that slices can be subjected to longer-term culture. As such, cardiac slices allow for studying the acute as well as chronic effects of drugs. Furthermore, the ability to collect several hundred to a thousand slices from a single heart makes this a high-throughput model to test several drugs at varying concentrations and combinations with other drugs at the same time. Slices can be prepared from any given region of the heart. In this protocol, we describe the preparation of left ventricular slices by isolating tissue cubes from the left ventricular free wall and sectioning them into slices using a high precision vibrating microtome. These slices can then either be subjected to acute experiments to measure baseline cardiac electrophysiological function or cultured for chronic drug studies. This protocol also describes dual optical mapping of cardiac slices for simultaneous recordings of transmembrane potentials and intracellular calcium dynamics to determine the effects of the drugs being investigated.

This protocol describes the procedure for sectioning and culturing human cardiac slices for preclinical drug testing and details the use of optical mapping for recording transmembrane voltage and intracellular calcium signals simultaneously from these slices.

Human cardiac slice preparations have recently been developed as a platform for human physiology studies and therapy testing to bridge the gap between ...

Preparation of Mesh-Shaped Engineered Cardiac Tissues Derived from Human iPS Cells for In Vivo Myocardial Repair

The current protocol describes methods to generate scalable, mesh-shaped engineered cardiac tissues (ECTs) composed of cardiovascular cells derived from human induced pluripotent stem cells (hiPSCs), which are developed towards the goal of clinical use. HiPSC-derived cardiomyocytes, endothelial cells, and vascular mural cells are mixed with gel matrix and then poured into a polydimethylsiloxane (PDMS) tissue mold with rectangular internal staggered posts. By culture day 14 ECTs mature into a 1.5 cm x 1.5 cm mesh structure with 0.5 mm diameter myofiber bundles. Cardiomyocytes align to the long-axis of each bundle and spontaneously beat synchronously. This approach can be scaled up to a larger (3.0 cm x 3.0 cm) mesh ECT while preserving construct maturation and function. Thus, mesh-shaped ECTs generated from hiPSC-derived cardiac cells may be feasible for cardiac regeneration paradigms.

The present protocol generates mesh-shaped engineered cardiac tissues containing cardiovascular cells derived from human induced pluripotent stem cells to allow the investigation of cell implantation therapy for heart diseases.

The current protocol describes methods to generate scalable, mesh-shaped engineered cardiac tissues (ECTs) composed of cardiovascular cells derived from ...

Fabrication of 3D Cardiac Microtissue Arrays using Human iPSC-Derived Cardiomyocytes, Cardiac Fibroblasts, and Endothelial Cells

Generation of human cardiomyocytes (CMs), cardiac fibroblasts (CFs), and endothelial cells (ECs) from induced pluripotent stem cells (iPSCs) has provided a unique opportunity to study the complex interplay among different cardiovascular cell types that drives tissue development and disease. In the area of cardiac tissue models, several sophisticated three-dimensional (3D) approaches use induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) to mimic physiological relevance and native tissue environment with a combination of extracellular matrices and crosslinkers. However, these systems are complex to fabricate without microfabrication expertise and require several weeks to self-assemble. Most importantly, many of these systems lack vascular cells and cardiac fibroblasts that make up over 60% of the nonmyocytes in the human heart. Here we describe the derivation of all three cardiac cell types from iPSCs to fabricate cardiac microtissues. This facile replica molding technique allows cardiac microtissue culture in standard multi-well cell culture plates for several weeks. The platform allows user-defined control over microtissue sizes based on initial seeding density and requires less than 3 days for self-assembly to achieve observable cardiac microtissue contractions. Furthermore, the cardiac microtissues can be easily digested while maintaining high cell viability for single-cell interrogation with the use of flow cytometry and single-cell RNA sequencing (scRNA-seq). We envision that this in vitro model of cardiac microtissues will help accelerate validation studies in drug discovery and disease modeling.

Here, we describe an easy-to-use methodology to generate 3D self-assembled cardiac microtissue arrays composed of pre-differentiated human-induced pluripotent stem cell-derived cardiomyocytes, cardiac fibroblasts, and endothelial cells. This user-friendly and low cell requiring technique to generate cardiac microtissues can be implemented for disease modeling and early stages of drug development.

Generation of human cardiomyocytes (CMs), cardiac fibroblasts (CFs), and endothelial cells (ECs) from induced pluripotent stem cells (iPSCs) has provided ...

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

Three dimensional&#160;models can be a valuable tool for surgeons as they develop surgical plans and medical fellows as they learn about complex cases. In particular, 3D models can play an important role in the field of cardiology, where complex congenital heart diseases occur. While many 3D printers can provide anatomically correct and detailed models, existing 3D printing materials fail to replicate myocardial tissue properties and can be extremely costly. This protocol aims to develop a process for the creation of patient-specific models of complex congenital heart defects using a low-cost silicone that more closely matches cardiac muscle properties. With improved model fidelity, actual surgical procedural training could occur in advance of the procedure. Successful creation of cardiac models begins with the segmentation of radiologic images to generate a virtual blood pool (blood that fills the chambers of the heart) and myocardial tissue mold. The blood pool and myocardial mold are 3D printed in acrylonitrile butadiene styrene (ABS), a plastic dissolvable in acetone. The mold is assembled around the blood pool, creating a negative space simulating the myocardium. Silicone with a shore hardness of 2A is poured into the negative space and allowed to cure. The myocardial mold is removed, and the remaining silicone/blood pool model is submerged in acetone. The described process results in a physical model in which all cardiac features, including intra-cardiac defects, are represented with more realistic tissue properties and are more closely approximated than a direct 3D printing approach. The successful surgical correction of a model with a ventricular septal defect (VSD) using a GORE-TEX patch (standard surgical intervention for defect) demonstrates the utility of the method.

Patient-specific models improve surgeon and fellow confidence when developing or learning surgical plans. Three-dimensional (3D) printers generate adequate detail for surgical preparation, but fail to replicate tissue haptic fidelity. A protocol is presented detailing the creation of patient-specific, silicone cardiac models, combining 3D printing precision with simulated silicone tissue.

Three dimensional&#160;models can be a valuable tool for surgeons as they develop surgical plans and medical fellows as they learn about complex cases. In ...

In Silico Clinical Trials for Cardiovascular Disease

The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique characteristic of the platform is the integration of patient-specific biological, genetic, and clinical imaging data. The platform allows the testing and optimization of medical treatment to maximize positive therapeutic outcomes. Thus, adverse effects and drug interactions can be avoided, sudden cardiac death can be prevented, and the time between the commencement of drug treatment and the desired result can be shortened. This article presents a parametric model of the left ventricle automatically generated from patient-specific ultrasound images by applying an electromechanical model of the heart. Drug effects were prescribed through specific boundary conditions for inlet and outlet flow, ECG measurements, and calcium function for heart muscle properties. Genetic data from patients were incorporated through the material property of the ventricle wall. Apical view analysis involves segmenting the left ventricle using a previously trained U-net framework and calculating the bordering rectangle based on the length of the left ventricle in the diastolic and systolic cycle. M-mode view analysis includes bordering of the characteristic areas of the left ventricle in the M-mode view. After extracting the dimensions of the left ventricle, a finite elements mesh was generated based on mesh options, and a finite element analysis simulation was run with user-provided inlet and outlet velocities. Users can directly visualize on the platform various simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses.

In Silico Clinical Trials for Cardiovascular Disease

This protocol demonstrates the workflow of the SILICOFCM platform for automatically generating a parametric model of the left ventricle from patient-specific ultrasound images by applying a multi-scale electromechanical model of the heart. This platform enables in silico clinical trials intended to reduce real clinical trials and maximize positive therapeutic outcomes.

The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique ...

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

Summary

Explore More Videos

Chapters in this video

High-Resolution Endocardial and Epicardial Optical Mapping in a Sheep Model of Stretch-Induced Atrial Fibrillation

Anatomical Reconstructions of the Human Cardiac Venous System using Contrast-computed Tomography of Perfusion-fixed Specimens

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Creating a Structurally Realistic Finite Element Geometric Model of a Cardiomyocyte to Study the Role of Cellular Architecture in Cardiomyocyte Systems Biology

Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery

Preclinical Cardiac Electrophysiology Assessment by Dual Voltage and Calcium Optical Mapping of Human Organotypic Cardiac Slices

Preparation of Mesh-Shaped Engineered Cardiac Tissues Derived from Human iPS Cells for In Vivo Myocardial Repair

Fabrication of 3D Cardiac Microtissue Arrays using Human iPSC-Derived Cardiomyocytes, Cardiac Fibroblasts, and Endothelial Cells

Creation of Patient-Specific Silicone Cardiac Models with Applications in Pre-surgical Plans and Hands-on Training

In Silico Clinical Trials for Cardiovascular Disease