Merk, Denis R. MD, PhD*†; Karar, Mohamed Esmail MSc‡; Chalopin, Claire PhD‡; Holzhey, David MD, PhD†; Falk, Volkmar MD, PhD§; Mohr, Friedrich W. MD, PhD†; Burgert, Oliver PhD‡
From the *Department of Cardiothoracic Surgery, School of Medicine, Stanford University, Stanford, CA USA; †Department of Cardiothoracic Surgery, Heart Center Leipzig, University of Leipzig, Leipzig, Germany; ‡Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Leipzig, Germany; and §Division of Heart and Vascular Surgery, University Hospital Zurich, Zurich, Switzerland.
Accepted for publication June 30, 2011.
Supported by German Academic Exchange Service (DAAD) in cooperation with the Egyptian Supreme Council of Universities (ESCU) under scholarship no. A0690520. The Innovation Center Computer Assisted Surgery (ICCAS) at Faculty of Medicine, University of Leipzig, is funded by German Federal Ministry of Education and Research (BMBF) and Saxon Ministry of Science and Fine Arts (SMWK) in the scope of the initiative “Unternehmen Region” with grants 03 ZIK 031 and 03 ZIK 032.
Disclosure: The authors declare no conflict of interest.
Address correspondence and reprint requests to Mohamed Esmail Karar, MSc, Innovation Center Computer Assisted Surgery (ICCAS), University of Leipzig, Semmelweisstrasse 14, D-04103 Leipzig, Germany. E-mail: email@example.com.
Aortic valve stenosis is the most frequently acquired valvular heart disease and is responsible for approximately 70% of all valve surgery.1 Between 2% to 4% of the population older than 65 years in the United States and 43% of all patients with valvular diseases in Europe suffer from degenerative aortic stenoses.2,3
Aortic valve replacement via sternotomy and cardiopulmonary bypass (CPB) is the gold standard in treating aortic valve pathologies.2,4 However, nearly 30% of elderly patients with severe and symptomatic aortic stenosis are not suitable candidates for standard surgical replacement of the aortic valve due to their comorbidities and are therefore high surgical risk.5 Consequently, a new approach such as minimally invasive transcatheter aortic valve implantation (TAVI) presents a good treatment solution for high-risk patients.6–9
Compared with the standard treatment of aortic valve stenosis, the TAVI limits the surgical access to either a minithoracotomy (transapical TAVI) or a transfemoral TAVI approach. Another advantage of the TAVI is the possibility to perform beating heart surgery without any CPB support and therefore decrease the side effects of CPB.10
The main advantage of using the transapical TAVI technique is the direct access to the aortic valve, eliminating the need of a large peripheral vascular access as needed for the transfemoral approach and therefore being advantageous for patients with peripheral vascular disease, small tortuous vasculature, a history of major vascular complications, and/or previous vascular interventions.2 In transapical TAVI, a stented aortic valve prosthesis (AVP) is temporarily crimped upon a balloon catheter and inserted via a left lateral minithoracotomy through the apex into the aortic root. After the valve is considered to be positioned correctly, the balloon-expandable prosthesis is deployed under rapid ventricular pacing to its final diameter, fixing the prosthesis in the aorta and pressing the natural calcified valve in the aortic annulus (Fig. 1a).
Once the AVP has been deployed, it cannot be repositioned. Hence, exact valve positioning is crucial. A prosthesis positioned too high can obstruct the coronaries. On the other hand, a valve positioned too low can result in damaged mitral valve leaflets and/or leads to severe paravalvular aortic regurgitation.
Other complications occurring during TAVI can affect the conducting system resulting in atrioventricular block (10%–30%) or an insufficient valve with regurgitation or a paravalvular leakage (2%–35%). Further complications include coronary ostia occlusion (0.5%–1%), cardiac tamponade (1%–9%), or acute aortic dissections (0%–4%).11,12 AVP malpositioning during implantation is rare and occurs with an incidence of 1.2% to 5.3% when using the transapical technique.12,13 The 30-day mortality for transapical TAVI in Europe is between 5% to 11%.12,14,15
The placement of the AVP is typically carried out under live 2D x-ray fluoroscopy guidance.10,16 However, tracking of the stenotic aortic valve features in intraoperative fluoroscopic images is a challenging task, because the aortic root including the anatomic landmarks is only visible under fluoroscopy guidance with contrast agent. On the other side, an excessive usage of contrast agent can lead to renal insufficiency especially in the high-risk elderly patient population scheduled for TAVI.
Only a few studies deal with intraoperative image-guided TAVI procedure. A guidance system for assisting the TAVI has been proposed, including a planning system and a tracking system for the crimped bioprosthesis.17–19 Another system used interventional angiography and a fluoroscopy C-arm system to overlay a static 3D rendered volume onto live 2D fluoroscopic images.20 Other systems use robotic systems with magnetic resonance imaging (MRI) guidance for image-guided therapy.21,22
The motivation of this study is to automatically find the exact position for the prosthesis without additional usage of contrast agent under 2D fluoroscopy guidance. Tracking and continuously visualizing the moving anatomic aortic valve landmarks, such as the coronary ostia and the lowest points of the cusps, are the key features for this automation process.
Image registration is a key technology that enables new techniques for image-guided minimally invasive interventions.23 It is a process that geometrically aligns two images acquired from the same patient at different times or with different imaging modalities. Real-time alignment of 2D fluoroscopic images at different acquisition times are used to determine the landmark position in each fluoroscopic image of live sequences.
To increase the overall safety for TAVI, we present a new method of intraoperative tracking of aortic valve landmarks by defining the boundaries of the target area during valve implantation between the coronary ostia and the lowest points of two aortic valve cusps. Based on real-time image registration during implantation, this new technique assists the positioning of the prosthesis under live 2D fluoroscopy guidance as shown in Figure 1b.
We retrospectively picked 10 datasets of 2D fluoroscopic images for patients receiving a regular transapical TAVI using Edwards SAPIEN prosthesis (Edwards Lifesciences Inc., Irvine, CA USA). The SAPIEN prosthesis is one of the commercially available transcatheter prostheses used and approved for the transapical TAVI.14
Figure 2 shows the flowchart of the developed image-based tracking method of four valve landmarks in live 2D fluoroscopic images. The four necessary anatomic landmarks are the coronary ostia and the lowest points of two aortic cusps. The initial positions of the valve landmarks are manually defined on a contrast image. In this study, the contrast image is automatically detected when the aortic root roadmap is clearly visible in one fluoroscopic image of the sequence because of contrast agent injection. Because the contrast-enhanced aortic root including stenotic valve features shows dark pixels in fluoroscopic images, a histogram analysis of these fluoroscopic images was used to capture the required contrast image (Fig. 3b).24
Once the positions of the valve landmarks have been initialized, automatic image-based tracking procedure of the landmarks is started as follows: first, each input image of the sequence and the contrast image have to be preprocessed to reduce the image noise by using median filter.
Second, a rigid intensity-based image registration framework between the input image as a fixed image and the contrast image as a moving image is applied, based on the Insight Segmentation and Registration Toolkit (ITK).25 The image registration framework includes four components:
* A mutual information metric by Mattes et al26 has been used to define how well the two images match each other based on similar intensity features between the input image and the predefined contrast image (Fig. 3). These matched objects are the transesophageal echocardiography probe, guide wires, the catheter, the rib spreader, and the valve delivery system.
* A linear interpolation allows intensity estimation of the moving image in a nongrid position after mapping the fixed image space onto the moving image space.
* A transformation maps the fixed image onto the moving image by resolving the translational misalignment between the two images to overlap the same objects in both images.
* A regular step gradient descent optimizer is applied with four iterations to explore the optimal values of these translational parameters in x-y coordinates that allow to update the registered image transformation in real time.
Third, the initial positions of the valve landmarks in the contrast image are updated according to the final transform parameters in current processed image. Finally, the new position of the valve landmarks is overlaid onto each image of the sequence to visualize the boundaries of the target area for the valve implantation in the 2D fluoroscopic images during the intervention.
Evaluation of the registration-based tracking procedure is a challenging task, because of the lack of contrast agent in most fluoroscopic images of the sequence. Moreover, there is no ground truth image data for the aortic root including the valve landmarks. Therefore, all fluoroscopic images have been visually assessed and qualitatively inspected to validate the quality score of distance localization errors of the target.27 The quality scores of the tracked valve landmarks are estimated as follows: (a) good is defined as an excellent alignment to a minimal misalignment between the input sequences and the registered contrast image with a difference of less than 2 mm; (b) moderate is defined when a relatively high misalignment between the two images occurs between 2.0 and 5.0 mm; and (c) insufficient is defined as a significant misalignment of more than 5.0 mm between both images.
The developed tracking method of aortic valve landmarks was tested and evaluated on different fluoroscopic image sequences of 10 patients during routine transapical TAVI. The dimensions of fluoroscopic images are 1024 × 1024 pixels, with a pixel size of approximately 0.2 mm. All fluoroscopic images sequences were acquired during routine angiographic and fluoroscopic x-ray imaging with a floor mounted C-arm system (Artis zeego, Siemens AG, Healthcare Sector, Forchheim, Germany).
Figure 4 shows a screenshot of the developed fluoroscopic guidance software for assisting the TAVI. The image-registration tracking algorithm has been implemented using C++ programming language being suitable for real-time image processing with a frame rate of 10 to 15 frames per second.
In Figure 5, two different sets of tested fluoroscopic images are presented showing successful results of valve landmark tracking, based on the intensity-based image registration. The contrast images including the manual localization of aortic valve landmarks are shown in Figures 5a, b. To visualize the matching accuracy between the contrast image (moving image) and the intensity features of each live fluoroscopic image (fixed image), checkerboard images are shown as alternating blocks from the contrast image and the input fluoroscopic image in the presence of contrast agent (Figs. 5c, d) and without contrast agent as shown in Figures 5e, f.
Significant errors of valve landmark tracking appeared in the image sequence (seq. 7) among all the tested datasets (Fig. 6). Although the contrast image has been successfully automatically detected as shown in Figure 6a, checkerboard registration visualizes the visible discrepancies between the input fluoroscopic images and the detected contrast image.
The registration-based tracking algorithm has been qualitatively evaluated for all tested images of the fluoroscopic sequences as a percentage of the total number of images per sequence as shown in Figure 7. In image sequences 6, 7, and 10, insufficiently registered landmarks were observed. The seq. 7 showed the maximum percentage of images with an insufficient score of 25% of all images per sequence, due to severe image noise and a small number of similar intensity features between the contrast image and each image of the sequence. The moderate score of landmarks tracking did not exceed 40% in seq. 5 among all the tested image sequences. However, most images of the 10 fluoroscopic sequences showed successful tracking of landmarks with a good score between 60% and 95% of all fluoroscopic images, such as sequences 2, 3, and 8.
The positioning of the AVP is the most critical step of the transapical TAVI procedure under 2D fluoroscopy guidance. Avoiding usage of additional contrast agent is a key factor to solve the current limitations of single-plane x-ray fluoroscopy for the image guidance of TAVI interventions.
We demonstrated in this study that 2D fluoroscopic image registration is potentially a feasible method for intraoperative tracking of the aortic valve landmarks to assist the position of the AVP without further contrast agent injections. Moreover, the main advantage of image-based tracking methods over optical and electromagnetic tracking systems is that image-based guidance systems do not require expensive instrumentation or sophisticated imaging technologies. Therefore, our image register tracking-based algorithm provides an appropriate solution for real-time tracking of anatomic valve landmarks in live 2D fluoroscopic images.
The qualitative evaluation of the registration performance showed that the quality of registration based on landmark algorithm tracking is good as long as the alignment errors are less than 2.0 mm as seen in most tested sequences. Three of 10 fluoroscopic image sequences showed misalignment in the range of 1% to 25% insufficient registered images per sequence. Severe image noise and the small number of similar intensity features such as transesophageal echocardiography probe and guide wires affected the image registration accuracy for seq. 7 (Fig. 6). However, the alignment of fluoroscopic images is still valid and optimized by using the capabilities of image registration framework.
We have developed a new sensorless tracking method of stenotic aortic valve landmarks for assisting the transapical TAVI under 2D fluoroscopy guidance. This method can provide a helpful tool for the surgeon by automatically defining the desired position of the AVP. In addition, the developed method is used at low levels of contrast agent injections for controlling the final placement of the prosthesis to reduce long-term negative effects, such as renal failure, in patients. Therefore, the accuracy of valve implantation will increase; surgery time can be significantly reduced while increasing the overall safety of the surgical procedure.
To minimize the user interaction and to increase the accuracy of TAVI guidance, we are currently integrating the 3D information of the aortic root model including the valve landmarks from intraoperative C-arm computed tomographic images28 with live 2D fluoroscopic images.
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This is a report of an innovative new technique for sensorless tracking of stenotic valve landmarks using 2D fluoroscopic imaging to assist in the accurate placement of a transapical transcatheter aortic valve. This is a timely report as transcatheter aortic valve implantation (TAVI) is frequently performed in Europe and has just been approved for inoperable patients in the U.S. Mispositioning during TAVI can have disastrous and, in rare instances, fatal consequences. The advantages of this technique include that it does not require other expensive instrumentation, such as MRI guidance, or additional usage of contrast agents. The disadvantage is that there was insufficient image tracking in 3 of 10 images, and the percentage of good tracking ranged from under 50% to 95%. Further work will be needed to define the utility of this new tracking methodology.
© 2011 Lippincott Williams & Wilkins, Inc.