- Research Article
- Open Access
Acquiring Multiview C-Arm Images to Assist Cardiac Ablation Procedures
© Pascal Fallavollita. 2010
- Received: 31 July 2009
- Accepted: 14 December 2009
- Published: 4 March 2010
CARTO XP is an electroanatomical cardiac mapping system that provides 3D color-coded maps of the electrical activity of the heart; however it is expensive and it can only use a single costly magnetic catheter for each patient intervention. Our approach consists of integrating fluoroscopic and electrical data from the RF catheters into the same image so as to better guide RF ablation, shorten the duration of this procedure, increase its efficacy, and decrease hospital cost when compared to CARTO XP. We propose a method that relies on multi-view C-arm fluoroscopy image acquisition for (1) the 3D reconstruction of the anatomical structure of interest, (2) the robust temporal tracking of the tip-electrode of a mapping catheter between the diastolic and systolic phases and (3) the 2D/3D registration of color coded isochronal maps directly on the 2D fluoroscopy image that would help the clinician guide the ablation procedure much more effectively. The method has been tested on canine experimental data.
- Fluoroscopic Image
- Mapping Catheter
- Temporal Tracking
- Tensor Vote
- Local Activation Time
Other approaches proposed by researchers to guide RF ablation therapy employ multimodal image fusion. These include the visualization of an optically tracked catheter by making use of magnetic resonance imaging (MRI) [2, 3], the combination of MRI and fluoroscopy , ultrasound imaging of the ablation catheter , the combination of ultrasound and preoperative computer tomography (CT) , or preoperative imaging (CT/MRI) for ablation planning [7, 8]. However, all these approaches omit incorporating electrophysiological data that is crucial for the clinician when locating the arrhythmogenic site.
Recently, efforts have been made in attempting to develop a more affordable fluoroscopic navigation system by obtaining local electrical activation times from a roving ablation catheter whose positions are computed from a single image using a monoplane C-arm fluoroscope. However, this single image method yielded inaccurate 3D reconstructions as depth estimations were about 10 mm, thus motivating the need of using multiview geometry for accurate 3D reconstruction. Research objectives were aimed at emulating the format of the CARTO XP system and focusing on treating ventricular tachycardias . With the CARTO XP nonfluoroscopic mapping approach, the patient is positioned over a tripod emitting three electromagnetic waves at unique frequencies. Each beam is registered by one of three specifically tuned coils embedded in the mapping catheter tip to specify location in 3D space, when the catheter tip is considered against a reference catheter. The catheter location and electrograms are recorded and reconstructed in realtime and presented as a 3D geometrical map color coded with the electrophysiological information. The arrhythmia must remain the same during the long point-by-point mapping procedure.
The emphasis of this paper is a continuation of the work in , but with a focus on multiview vision techniques. The long-term objective continues to develop a system similar to the functionality of the CARTO XP technology; albeit with the following advantages: (a) being more affordable by making use of cost-effective catheters which increase the number of available types and shapes of catheters used during the cardiac ablation procedure, (b) use of common monoplane C-arm X-ray fluoroscopes (see Figure 1(b)) compared to expensive new mapping equipment ( 300,000$/system, 5,000$/catheter) that need to be purchased by hospitals, and (c) that 2D/3D registration using only X-ray fluoroscopy is possible in order to superimpose a translucent image of the cardiac activation map directly over 2D C-arm images.
The contributions of this paper are as follows: (i) we provide a complete analysis on two-view 3D reconstruction of the tip-electrode of a mapping catheter inserted in the left ventricle of the heart, (ii) we propose an automatic algorithm that extracts the fluoroscopy image depicting the diastolic image phase, (iii) we automatically filter and track the tip-electrode in a sequence of images using a 2D/2D registration algorithm initialized by clicking once on the tip electrode seen in the diastolic image, and (iv) we provide a detailed report on the feasibility of using our multiview 3D methodology to guide VT catheter ablation by presenting an experimental procedure and results on canine data.
The overall workflow is summarized as follows. Once C-arm data acquisition is achieved, extracting relevant images is necessary in order to minimize motion artifacts before reconstruction in three dimensions. Thus, an image extraction algorithm is first presented allowing us to select images depicting important phases of the cardiac cycle. Second, we describe a single-click algorithm coupled with a new image processing scheme allowing for automatic temporal tracking of the tip electrode. We conclude the section by recalling the mathematics of multiview geometry and propose a fitting technique for 3D reconstruction purposes.
2.1. Fluoroscopic Image Analysis
2.2. Automatic Tracking of Tip-Electrode Using Single-Click Initialization
Locating the tip-electrode of the mapping catheter is crucial since it will come into contact with the arrhythmogenic site for ablation purposes. Therefore, extracting 2D coordinates of the tip electrode is essential. However, this is not an easy task when attempting to develop an automatic algorithm. To our knowledge, the work in  presents the most recent work focusing on this task. The authors present a steerable tensor voting filter to extract the tip-electrode of ablation catheters and report results of approximately 70% accuracy for electrode detection in extremely noisy images. Nevertheless, the algorithm requires defining 3 tensor voting field parameters that require adjustment. The results are not clinically viable and manual selection is still necessary to collect all the tip-electrode coordinates in the C-arm images. At this stage, we thought it best to preserve a smooth workflow by manually selecting only the tip electrode visible in each of the diastole images.
2.2.1. Three-Step Filter to Catalyze Temporal Tracking
At each user-click of the tip-electrode, automatic tracking commences between the diastolic and systolic image frames determined in Section 2.1. Two cropped window sizes are first defined: (i) a template window surrounding the tip electrode center, and a search window around the template. For both cropped windows, we apply the following 3-step preprocessing filter.
A homomorphic filter is used first to denoise the fluoroscopic image . The homomorphic filter decreases the contribution made by the low frequencies and amplifies the contribution of high frequencies. The result is simultaneous dynamic range compression and contrast enhancement. The homomorphic filter is given by
with and . The coefficient c controls the sharpness of the slope at the transition between high and low frequencies; whereas is a constant that controls the shape of the filter and is the distance in pixels from the origin of the filter.
Anisotropic systems are those that exhibit a preferential spreading direction while isotropic systems are those that have no preferences. The Perona-Malik anisotropic diffusion  method was implemented here in order to reduce noise and texture from the image, as well as to preserve and enhance structures. The diffusion equation is given by
where is the input image and is the diffusion coefficient that controls the degree of smoothing at each image pixel. The diffusion coefficient is a monotonically decreasing function of the image gradient magnitude. It allows for locally adaptive diffusion strengths; edges are selectively smoothed or enhanced based on the evaluation of the diffusion function. Although any monotonically decreasing continuous function of the gradient would suffice as a diffusion function, we use the following diffusion coefficient:
is referred to as the diffusion constant or the flow constant. The greatest flow is produced when the image gradient magnitude is close to the value of . Therefore, by choosing to correspond to gradient magnitudes produced by noise, the diffusion process can thus be used to reduce noise in images.
2.2.2. Temporal Tracking via 2D/2D Registration
Our method builds on the following intuition. As we are dealing with the same imaging modality during registration, then the electrode tip carries enough distinctive information for intensity-based registration to hone in the correct transformation (i.e., displacement) between image frames. Also, since the electrode structure is solid then a rigid-based 2D/2D registration scheme should suffice.
We implemented the Normalized Correlation (NC) metric. Pixel values are taken from the search window; their positions are mapped to the template image in order to find a match, or superposition of the two. The correlation is normalized by the autocorrelations of both the images . Let Img1 and Img2 be the search window and template image, respectively. NC computes pixel-wise cross-correlation and normalizes it by the square root of the autocorrelation of the images:
where and are the i th pixels in two images, respectively, and N is the number of pixels considered. The factor is used to optimize the metric when its minimum is reached, say at minus one. Misalignment between the images results in small measurement values. This metric produces a cost function with sharp peaks and well-defined minima. The number of spatial samples used here is empirically set at 50.
The next position of the tip electrode is obtained by calculating optimal transformation that locks both images over each other. The centroid is extracted and serves as 2D coordinates for the tip electrode in the image following the diastolic frame. Here, template and search window images are extracted again and registration is executed. This routine is performed until the systolic image (see Figure 4). At this stage we should have the estimated 2D coordinates of the tip electrodes in all images between diastole and systole.
2.3. C-Arm Fluoroscopy Geometry
The intrinsic matrix of size [ ] contains the pixel coordinates of the image center, also known as the principal point , the scaling factor , which defines the number of pixels per unit distance in image coordinates, and the source-to-image distance SID, also known as the focal length, f of the C-arm (in meters). The extrinsic matrix of size is identified by the transformation needed to align the world coordinate system to the camera coordinate system. This means that a translation vector, , and a rotation matrix, , need to be found in order to align the corresponding axis of the two reference frames. Imaging parameters such as focal length, primary and secondary rotation angles image pixel spacing and size are obtained from the accompanied DICOM image files. This allows us to have a close enough estimate of the C-arm pose for a particular viewing angle.
2.4. Convex (Quick) Hull Algorithm
3.1. Mongrel Dog Experiment
We begin this section by recalling that our primary objective was to develop and test a mapping technique based on multiview measurements of electrode sites and on the reconstruction of the cardiac chamber of interest. Here, the reconstruction uses a full perspective projection model coupled with surface fitting Convex Hull algorithm. Our overall methodology mimics the CARTO XP technology but only requires cost-effective standard catheters and a monoplane C-arm fluoroscope.
3.2. Temporal Tracking Results
The forty datasets were analyzed using our image extraction algorithm as outlined in Section 2.1. Thus, a total of forty diastole and systolic images were extracted from the DICOM datasets, plus additional images between the two cardiac phases. At this point, we manually clicked the tip electrodes of both the reference and mapping catheters in all diastole images and recorded them for the multiview 3D reconstruction method.
Temporal tracking results for both posterior and left lateral datasets.
3.3. Multiview (Biplane) 3D Reconstruction Results
The activation times of the visible nodes are positioned in a matrix, at the 2D coordinate locations of the nodes.
The Delaunay method is used to organize the set of visible points as a set of triangles and cubic interpolation is applied to interpolate the activation times inside each triangle.
The scaled activation time matrix is used to create an RGB color matrix (using a red-to-blue color map), which is finally plotted over the fluoroscopic image with a 30% transparency ratio (30% for the color isochronal map and 70% for the grayscale fluoroscopic image).
From Figure 12, earliest activation times are clearly closest to the stimulation catheter (near the reference catheter). The posterior view also shows that the isochronal map followed the contour of the mapping catheter, which corresponds to the contour of the left ventricular cavity.
3.3.1. Present Method Advantages/Limitations versus the CARTO XP Technology
Since the CARTO XP nonfluoroscopic technology provides contact-based sequential acquisition of endocardial signals and reconstruction of 3D electroanatomical maps, this can be time consuming in terms of acquisition. However, this factor is operator dependent, and in experienced hands final geometry of a cardiac chamber can be obtained within 30 minutes . More important, the instability of the catheter used for timing intracardiac activation and major patient movements relative to the location pad may render the entire map inaccurate for subsequent use, requiring the construction of a whole new map. For established indications, the major limitation for broader use of electroanatomical mapping in catheter ablation procedures at the present time is the associated cost. This is further confounded by the high cost of the mapping catheter. In terms of typical ventricular tachycardia ablation procedures, authors in  indicate total average procedure time of minutes with fluoroscopy time of minutes.
The major drawback of our methodology is the use of fluoroscopy in order to reconstruct an anatomical 3D structure when compared to CARTO XP. Data acquisition took approximately 30–40 minutes, with about an additional 10–15 minutes of manual selection of the tip-electrodes, filtering, and temporal tracking for all image frames between diastole and systole. Also, the sequential measurements of the horizontal and vertical fluoroscopic images were not as accurate as simultaneous measurements with a true biplane fluoroscope as the vertical position of the reference catheter showed a difference of pixels in the twenty posterior and left lateral images. This relatively small difference can be explained by motion artifacts (respiration, heartbeat, subject displacement) and fluoroscope misalignment. However, we provide an added advantage by introducing 2D isochronal activation maps superimposed over the corresponding fluoroscopic images in the diastolic phase. These isochronal maps accurately depicted the progression of the electrical activation away from the pacing catheter. Furthermore, our entire methodology is cost efficient and this is important as the availability of expensive systems such as CARTO XP is still limited in developing countries .
We introduced a multiview method of a fluoroscopic navigation system to guide RF catheter ablation of cardiac arrhythmias. This initial prototype allowed us to: ( ) localize in 3D the mapping catheter tip-electrode from two-view fluoroscopic images; ( ) automatically extract important images of the cardiac phase, ( ) measure the local activation times on the electrograms recorded with the catheter at multiple sites, and ( ) superimpose over the fluoroscopic images, isochronal maps depicting the electrical activation sequence from which the cardiologist could precisely localize the arrhythmogenic site with respect to the RF catheter. As a plus, we present a single-click tracking algorithm that could estimate the locations of the tip-electrode in images following diastole. Our overall methodology strikes a balance between efficiency and robustness of a common multiview vision problem applied to medicine. Nevertheless, the true effectiveness of our method will be determined on patient data validation and experimentation.
The author would like to thank Dr. Pierre Savard, from École Polytechnique, Canada, for providing the image datasets and valuable suggestions.
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