The advent of high resolution structural and functional imaging of biological tissues has created the possibility of combining imaging techniques in a novel way to probe the relationship between structure and function in many tissues. Optical coherence tomography (OCT) is a rapidly developing noncontact imaging modality that can generate three-dimensional (3D) images in real time with micron-scale spatial resolution.1, 2 Optical coherence tomography is similar to ultrasound; however, instead of generating time-gated images based on echoes of sound waves, OCT images backscattered light with gating based on the interference of light coming from the tissue with light in a reference path3 and is capable of imaging a depth of in cardiac tissue.4
The spatial resolution of OCT is anywhere from , and it can provide very detailed morphological information in intact tissues and organs. Historically, this level of detail has been limited to histology; however, as a nondestructive imaging modality, OCT produces images of tissue structures that are free from the artifacts induced by histological sectioning. Therefore, OCT bridges the gap between the resolution of nondestructive imaging techniques, such as ultrasound and magnetic resonance imaging, and histology or confocal microscopy performed on tissue sections.5 Because of its resolution and real-time imaging, the applications of OCT have expanded across many fields of medicine, such as ophthalmology,6 nephrology,7 and cardiology.8
To date, the clinical applications for OCT in cardiology relate to coronary artery imaging. However in cardiac research, OCT has also been used to study the mechanical function of the embryonic mouse heart,9 and the growth mechanics of the developing chick heart,10 demonstrating that OCT can be used to investigate the relationship between cardiac structure and mechanical function. Also, two previous preliminary studies have shown that OCT can be used to recognize components of the cardiac conduction system,4, 11 which demonstrates that OCT can visualize distinct anatomical structures within the myocardium.
Optical mapping of voltage-sensitive dyes is widely used to investigate the electrophysiological function of cardiac tissue.12 The fluorescent signal that is recorded with optical mapping represents the summed electrical activity from a volume of tissue, with a depth of penetration of .13, 14 However, discerning components of optical signals originating in tissue layers below the surface is difficult, particularly in structurally heterogeneous regions such as the cardiac conduction system. Detailed knowledge of cardiac structure is also important in optical mapping of arrhythmias because structural heterogeneities influence arrhythmia induction, stabilization, and termination.15, 16 Therefore, developing a method to correlate the 3D structure of cardiac tissue with functional mapping data is necessary, and in this study, we coupled optical imaging of action potentials with OCT to provide direct structural-functional correlations in several cardiac preparations. Our data indicate that the detailed structural imaging of OCT significantly enhances the interpretation of optical data.
All animal studies were approved by the Institutional Animal Care and Use Committee of Washington University. New Zealand white rabbits ( , 2.5 to 3 mos. old, ) were anesthetized with of pentobarbital and 1000 intravenous units of heparin intravenously, after which a midsternal thoracotomy was performed and the heart removed. The heart was Langendorff perfused with oxygenated ( , ) Tyrode’s solution at and received of di-4-ANEPPS (Molecular Probes, Eugene, Oregon) over . The heart was dissected in cold Tyrode’s solution to create a preparation containing the atrioventricular (AV) junction, sinoatrial node, or the right ventricular free wall. Each type of preparation was pinned to silicone with the endocardium as flat as possible. The preparations were superfused at with perfusate containing of the excitation-contraction uncoupler 2,3-butanedione monoxime (Sigma, St. Louis, Missouri) to prevent motion artifacts.
A photodiode array (C4675, Hamamatsu, Hamamatsu City, Japan) was used with an optical mapping system that has been described previously.17 Briefly, a ring of light emitting diodes (LED ring) with a wavelength centered at illuminated the tissue to excite di-4-ANEPPS. Fluorescent emission passed through a lens (1:1.4, AF Nikkor, Nikon, Tokyo, Japan) and a dichroic mirror, was long pass filtered at , and focused on the photodiode array. Optical signals were amplified (Innovative Technologies Network, Gaithersburg, Maryland), sampled at (PCI 6031E, National Instruments, Austin, Texas), and then lowpass filtered at . The focusing lens could be adjusted to change the spatial resolution from per photodiode, depending on the preparation. Preparations were digitally photographed and optical mapping fields of view were marked with stainless steel pins in the tissue. Activation times were defined as the time of the maximum first derivative of the fluorescent signal, , and activation maps were constructed from the activation times of each photodiode. Conduction velocity vector fields were calculated from activation maps using a polynomial fitting method developed by Bayly 18
Following optical mapping experiments, the tissue and silicone disk were placed in 3.7% formaldehyde diluted in phosphate buffered saline (Sigma-Aldrich, St. Louis, Missouri) overnight at . The following day, samples were transferred to a 20% sucrose solution and kept at for two days to dehydrate the tissue. Optical coherence tomography imaging was then performed with samples submerged in sucrose solution at room temperature on the second day.
A microscope-coupled OCT system was used to image the samples.19 A super luminescent diode light source (Covega SLD 1021, Jessup, Maryland) with resolution was used. The axial and lateral resolution of the system was approximately (in air). Individual OCT images were averaged 3 to 6 times depending on the tissue sample at the time of acquisition.
The OCT volumes were acquired as data sets that were in the , , and dimensions, respectively, corresponding to a tissue volume of . For each of the preparations imaged with OCT, the tissue surface was never completely horizontal across the entire imaging field of view. Depending on the tissue, surface variations ranged vertically from across the horizontal field of view [Fig. 1a ]. Because of the surface variation, slicing the 3D data set in the -plane created artifacts in the resulting images such as shadows and holes, as shown in Fig. 1b. To generate OCT images of the tissue parallel to the surface, we defined a new coordinate system in the dimension , where corresponded to the tissue surface [Fig. 1c].
To define the tissue surface, each scan was filtered with a two-dimensional Weiner filter (MATLAB, The Math Works, Natick, Massachusetts) to remove noise before the scans were combined to generate a 3D data set. Next, an intensity threshold was defined, and each column of the 3D data set was searched in the dimension for the pixel where the threshold was crossed. This point in each column corresponded to the tissue surface. All surface points were combined in three dimensions to generate the surface of the tissue. Once the surface was defined, images parallel to the surface were created by stepping through the data set below the surface by one pixel at a time in the dimension [Fig. 1d].
Fiber Orientation Determination
To calculate fiber orientation from two-dimensional (2D) OCT images,20 a modification of an algorithm described in Karlon 21 was used. First, preprocessing of the data is necessary because of the low contrast nature of OCT images. A Wiener filter was used to remove noise and a 2D Butterworth high pass filter was used to remove the low frequency background intensity (MATLAB).
The and intensity gradients ( and , respectively) were computed by convolving the horizontal and vertical Sobel filters with the 2D OCT images. For each pixel, the magnitude and angle of the gradient, and , were calculated, the dominant local direction of the fibers was computed . Vectors that were outliers were removed manually. Because the angle of the fibers is perpendicular to the angle of the gradients, the final dominant angle for the subspace of the image was determined as the maximum of the distribution shifted by .
OCT of Myocardium
The optical properties of cardiac fibrous tissue and fat create optical contrast within the myocardium in OCT images. Figure 2a illustrates the rabbit atrioventricular junction (AVJ), and corresponding OCT volumes are shown in Fig. 2b. Comparing corresponding histology and OCT images in Figs. 2c and 2d, and Figs. 2e and 2f, it is clear that the fibrous tissue (which appears blue in Masson’s trichrome histology) and adipose tissue surrounding the conduction system in the rabbit AVJ produce less backscatter in OCT images than the myocardium, providing the necessary contrast to visualize the AVJ conduction system with OCT, free of sectioning artifacts usually present in histology.
Optical Mapping Coupled with OCT
Cardiac conduction is faster in the direction of fiber orientation than transverse to fibers, due to factors such as cell orientation, size, geometry, and gap junctional distribution.22 Therefore, by coupling optical mapping with OCT, we expected to find a strong correlation between conduction velocity and fiber orientation. To validate the coupling of OCT and optical mapping, we initially used a 2D system so that optical signals originating below the surface of the tissue did not confound our interpretation of the data. For this purpose, we used the rabbit sinoatrial node (SAN, ) because the rabbit SAN is thick and several centimeters wide23 and, therefore, is essentially two dimensional with one layer of tissue contributing to optical signals [Fig. 3a ]. Figure 3b displays an activation map recorded during spontaneous SAN activity. Activation began in the middle of the SAN and spread anisotropically, which is indicated by conduction velocity vectors. Velocity vectors were binned in intervals in Fig. 3c, which indicated that conduction spread most rapidly at at an angle of . Conduction was slowest orthogonally at .
A OCT image of the SAN below the surface is shown in Fig. 3d with the leading pacemaker location indicated, and fiber orientation of the tissue is shown in Fig. 3e. A histogram of the fiber vectors is shown in Fig. 3c, which indicates that the dominant fiber angle in the tissue is . Therefore, the direction of maximal conduction velocity was aligned with the fiber orientation of the tissue. We found the same result in all studied SAN preparations .
OCT Can Resolve Multilayer Conduction Seen in Optical Mapping
As mentioned previously, optical mapping data consists of fluorescence that originates not only in the tissue surface, but also from tissue layers below the surface. In tissue with multiple layers of conduction such as the AVJ (Fig. 2), optical action potentials (OAPs) contain multiple components originating in different layers. The upstroke of individual OAP components can be used to create activation maps and conduction velocity vectors to determine the direction of wave front propagation. As shown in Fig. 3, conduction velocity vectors and fiber orientation vectors are strongly correlated. Therefore, we hypothesized that fiber orientation vectors obtained with OCT and conduction velocity vectors constructed from OAP components recorded from multilayer tissue could be used to identify which layer of tissue was the origin of individual optical signal components.
Bimodal imaging in the AVJ was performed to reconstruct the origin of OAP components. Figure 4a shows an activation map of retrograde conduction through the AVJ that was induced by pacing the His bundle. Figure 4b displays the OCT volume from this preparation. As can be seen in Figs. 4c, 4d, 4e, components of the activation map can be assigned to different layers of tissue in the OCT volume based on conduction velocity and fiber orientation vectors. In Fig. 4c, conduction spread from right to left at . An example OAP in Fig. 4c exhibits two distinct upstrokes. The portion of the activation map shown in Fig. 4c was constructed from any OAP that contained the initial upstroke. In the deep tissue layers seen in OCT ( below the surface), the fibers of the conduction system were oriented approximately parallel to the conduction velocity vectors [Fig. 4c].
In Fig. 4d, conduction propagated very slowly with OAPs showing no distinct upstroke during this period. Instead, the small initial depolarization of the OAP in Fig. 4d is consistent with electrotonic activation in this region. The OCT data from this region shows that below the tissue surface is the interface between the conduction system and the atrial myocardium, where conduction spread transverse to fiber orientation. Finally, the activation map in Fig. 4e, constructed from OAPs that only contained one upstroke that corresponded in time to the second upstroke seen in Fig. 4c, demonstrates that activation spread rapidly in all directions. Optical coherence tomography of the superficial layer of atrial tissue, below the surface, revealed that fiber orientation in the atrial tissue matched the initial direction of rapid conduction. Accounting for the 3D conduction of activation from the conduction system below the surface to the atrial layer above, conduction spread approximately between the two layers transversely to fiber orientation. Similar 3D reconstructions of conduction were reproduced in all studied AVJ preparations .
Structural Heterogeneities Contributing to Arrhythmia
Optical coherence tomography and optical mapping can also be used to investigate structural heterogeneities contributing to arrhythmias. Previous studies have shown that heterogeneities such as disrupted cell-cell coupling and scar tissue can provide the substrate to anchor reentrant arrhythmias,24, 25 converting paroxysmal arrhythmia to sustained monomorphic tachycardia. Here, we explored the role of microscopic structural heterogeneities in arrhythmia maintenance in the rabbit isolated right ventricle [Fig. 5a ]. Reentrant arrhythmias were induced in four preparations, and reentry tended to anchor at only one or two locations. These reentry core locations were imaged with OCT. In Fig. 5a, a preparation with two reentry cores is shown, with a phase map of reentry shown in Fig. 5b. Surface OCT imaging of each core is shown in Figs. 5c and 5d. The endocardium in Fig. 5c is highly trabeculated, and we postulate that this contributed to reentry anchoring at this location.
The reentry core of Fig. 5d shows little or no endocardial structural heterogeneity. However, OCT imaging sectioned in the dimension indicated two distinct myocardial sheets merged in this area. Figures 5e, 5f, 5g show OCT images and fiber orientation vectors from superficial to deeper layers: 60, 200, and below the surface, respectively. The dominant fiber angle changed from approximately in the superficial layer to below it [Fig. 5h]. Although this area appears quite smooth on the endocardium, the complicated fiber structure revealed with OCT may have played a role in anchoring reentrant arrhythmias in this region.
Imaging Tissue Damage
Functional studies of cardiac tissue often induce damage, either intentionally in the form of ablations, or unintentionally due to pacing. In Fig. 6a , an AVJ preparation was paced sequentially at each circle from an electrode that roamed in a grid pattern throughout the AVJ. A surface image of the OCT data set [Fig. 6b] clearly indicated that pacing at each location produced a small amount of tissue damage. As shown in Fig. 6c, a section perpendicular to the surface through one of the areas of tissue damage showed tissue swelling and decreased backscattering surrounding the area of injury, indicating that edema surrounded the injury. In addition, it is clear that the area of injury was confined to the superficial atrial layer; the His bundle below the surface was unaffected.
Here, we have illustrated several applications of bimodal biophotonic imaging combining OCT with optical mapping. Using OCT to reconstruct fiber angles from optically mapped preparations provides a precise understanding of the activation patterns recorded and facilitates the interpretation of optical mapping data recorded from preparations with multiple layers of conduction. Optical coherence tomography can also be used to investigate the role of microscopic heterogeneities in arrhythmia maintenance. Finally, we illustrated that OCT is a sensitive technique to investigate tissue damage that results from functional studies. Therefore using OCT to image larger tissue lesions, such as ablations, is certainly possible and could monitor the depth of ablated tissue.
In optical mapping data, the contribution of tissue layers below the surface to the recorded fluorescence is a decaying exponential function, with tissue up to in depth contributing to fluorescence.13 Here, we recorded OCT data from the atrial and ventricular myocardium, as well as the cardiac conduction system. We consistently recorded increased depth penetration with OCT in superventricular tissue as compared with the ventricular myocardium. With our system, we could resolve structures below the surface of superventricular tissue. However in ventricular tissue, our depth penetration was limited to . Because the depth of penetration of OCT and optical mapping are similar, the two techniques complement each other well with structural and functional data from overlapping tissue volumes.
Histology remains the gold standard for investigating the microscopic structure of biological tissue. However, histological methods are associated with artifacts related to the trauma of sectioning, the plane of section, and tissue processing following sectioning. Here, we introduced a new method of sectioning an OCT volume to create images parallel to the tissue surface, producing images within the myocardium in planes that were physiologically relevant. It is important to note, however, that although this method of sectioning OCT data is valid for smooth surfaces, it is not applicable to trabeculated regions because when there are substantial anatomic variations in the surface, our sectioning method juxtaposes pixels that were not related to each other in the actual tissue.
This form of “virtual histology” can be combined with traditional histology and immunohistochemistry to accurately determine molecular and structural characteristics of particular locations within the intact structure of tissue. When combined with optical mapping, the functional characteristics of such locations can be characterized as well. For example, the data in Fig. 4d indicate that slow conduction occurred at the interface of the atrial myocardium and the AV conduction system, transverse to fiber orientation. This data raises the question of why conduction slowed in this region. Was conduction slow simply because it was transverse to fiber orientation, or did the molecular characteristics of this region play a role? Perhaps when excitation left the densely packed myocardium of the conduction system and entered the loosely packed atrial myocardium surrounding the conduction system, only a few myocytes were depolarized at a time producing what looked like electrotonic activation in the optical signals. After excitation meandered through the loosely packed myocardium, it excited the well-defined superficial atrial myocardium and spread quickly across the atrial septum.
Applying an integrated multimodal imaging with optical mapping, OCT, and immunohistochemistry can enhance our understanding of the mechanisms responsible for slow AV nodal conduction by pinpointing the precise location of slow conduction, determining its intact structure, and then investigating the molecular characteristics of the slowly conducting regions. We have also shown that a similar comprehensive understanding of structural contributions to arrhythmia maintenance, as well as the relationship between structure and function in the sinoatrial node can be investigated using this method. More broadly, this approach can bridge the gap between structural and functional studies in other areas of research, such as neuroscience.
Funding for this project was provided by NIH (R01-HL67322), Medtronic, and a Whitaker Foundation Graduate Fellowship.