The iterative interaction between experimentation and simulation has long played a central role in the advancement of biological sciences. Among computational models of various biological systems, the heart is the most highly advanced example of a virtual organ, capable of integrating data at multiple scales, from genes all the way through the whole organ [1
]. 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 resynchronization [2
Recent years have witnessed revolutionary advances in imaging, including ex vivo
structural and diffusion tensor (DT) magnetic resonance imaging (MRI), that facilitate acquisition of the intact structure of explanted hearts with hitherto unavailable resolution. Leveraging on these advances, a new generation of whole-heart models with unprecedented detail have emerged [3
]. Such ex vivo
models are being used in basic research to uncover specific mechanisms of heart dysfunction in diseases such as myocardial infarction and heart failure.
Translation of this ex vivo
approach to the in vivo
setting of the clinic can yield far reaching consequences. Ultimately, doctors will be able to use simulation tools to predict outcomes of various electrophysiological interventions, and select the optimal cardiac therapy specific to a patient. To this end however, patient-specific ventricular fiber orientation data are necessary, as these orientations determine directions of electrical propagation and strain distributions in the heart. While ventricular geometry and structural remodeling data can now be routinely acquired in vivo
with imaging techniques such as late gadolinium enhanced cardiac MR [8
], physicians do not currently have access to a method to acquire myocardial orientations data in vivo
Myocardial fiber orientation data necessary for computational studies of cardiac electrophysiology and mechanics have been predominantly acquired using histological techniques; examples include the canine [9
] and swine [10
] ventricular models developed at Auckland University, and the rabbit ventricular model developed at the University of California, San Diego [11
]. While histological sectioning has traditionally been the gold standard for fiber data acquisition, this technique is exceedingly time consuming, prone to tissue deformation errors introduced due to cutting, restricted to 2-D measurements, and not applicable in vivo
. Recently, DTMRI has emerged as a powerful alternative to histology, and DTMRI cardiac fiber data have been acquired in many species [3
]. DTMRI-acquired fiber orientations have been shown to match histological data [13
], and have begun to be used in whole-heart computational electrophysiology and electromechanics [3
]. Unlike histology, DTMRI offers faster data acquisition, 3-D data, and nondestructive imaging. However, there are several drawbacks to the DTMRI technology, most importantly those that limit its application in the in vivo
setting, where the bulk motion of the heart interferes with diffusion measurements.
Attempts to translate the DTMRI technology to the in vivo
setting were first made by Edelman et al.
], but it was later found that their diffusion encoding interacted with myocardial strain, and therefore, a method to correct for strain effects was proposed [17
]. But this correction was achieved at the cost of increased acquisition noise and complexity. Although the strain correction method in combination with interpolation has recently been used to perform 3-D tractography of ventricular fiber orientations [19
], the data prior to interpolation was very sparse, scan time was about 40 min, and breathholding, a difficult task for patients which typically causes inter-slice misalignments, was necessary. Tseng et al.
] and Dou et al.
] have proposed techniques that do not require strain correction, but the former is applicable to only those phases of the cardiac cycle called sweet spots, where the strain effect is negligible, whereas the signal-to-noise ratio of the latter is limited by the gradient intensity available in clinical scanners. Recently, a spin-echo technique to measure cardiac diffusion in vivo
was proposed, and the utility of this method in measuring 2-D in-plane diffusion along short axis slices demonstrated [22
]. Toussaint et al.
] have used this method in combination with curvilinear interpolation to perform 3-D tractography of ventricular fiber orientations, but their acquisition was limited to the equatorial part of LV, scan time was about 1.5 h, and breath-hold correction, a very challenging postprocessing step, was necessary. All in all, despite the above mentioned efforts over almost two decades, in vivo
translation of cardiac DTMRI remains challenging due to technological inadequacies, e.g., in gradient strength, whole-heart imaging, radio-frequency, and navigators [24
Previously, there have been a number of efforts to estimate fiber orientations in the ventricles. Among these, rule-based methods have gained some popularity; they assign fiber orientations to the ventricular walls such that the inclination angles vary smoothly in the transmural direction, as a mathematical function of distance from the endocardium [4
]. Though there have been two preliminary studies comparing fiber orientations derived using rule-based methods with those obtained from DTMRI [26
], how well the idealized rules of myocyte orientation quantitatively correlate with true anatomy in normal and diseased hearts remains largely unknown. A different category of estimation methods utilizes geometric correspondences between two hearts. Panfilov’s group assigned canine heart fiber orientations to the ventricular surfaces of a human heart by computing a landmark-based surface mapping between the human heart and a canine heart [28
]. The intramural fiber orientations of the human heart, however, were derived from the acquired surface directions and a rule-based scheme. Sundar et al.
] presented a technique to estimate fiber orientations via an elastic image transformation of an atlas heart. They tested the methodology using normal and failing canine hearts, and showed that estimated fiber orientations correlate with DTMRI data better than fiber orientations synthesized using a rule-based method. But the elastic mapping employed by them is not guaranteed to be diffeomorphic, and therefore may not preserve the topology of anatomical structures. More importantly, Sundar et al.
did not measure the effect of error in their estimated fiber orientations on the results of simulations of cardiac electrophysiology, or any other functions of heart.
We seek to address the need for a new methodology to acquire myocardial fiber orientations for patient-specific simulations of cardiac function. Importantly, we hypothesize that ventricular fiber orientations of a normal or a failing heart can be estimated, given the geometry of the heart and an atlas, where the atlas is a heart whose geometry and fiber orientations are already available. Should our hypothesis be proved, it would pave the way for the use of estimated fiber orientations in patient-specific models of cardiac function.
In this study, we use state of the art techniques to develop the methodology for estimation of cardiac fiber orientations in vivo, and test the hypothesis in normal and failing ventricles. We quantify the estimation error in each heart, and then assess the effect of this error on simulations of cardiac electrophysiology in the specific heart, by computationally simulating local electrical activation maps as well as pseudo-electrocardiograms (pseudo-ECGs).