Identifying and tracking a guide wire in the coronary arteries during angioplasty from X-ray images

Palti-Wasserman, D.; Brukstein, A.M.; Beyar, Rafael

doi:10.1109/10.552245

Cited by 48 publications

(27 citation statements)

References 29 publications

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“…Various methods for catheter tracking have been proposed. They involve filter-based techniques [5] as well as template-matching and learning-based approaches [6]. For electrophysiology procedures, different types of catheters are available.…”

Section: Motivationmentioning

confidence: 99%

Catheter Tracking: Filter-Based vs. Learning-Based

Brost

Wimmer

Liao

et al. 2010

Lecture Notes in Computer Science

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Abstract. Atrial fibrillation is the most common sustained arrhythmia. One important treatment option is radio-frequency catheter ablation (RFCA) of the pulmonary veins attached to the left atrium. RFCA is usually performed under fluoroscopic (X-ray) image guidance. Overlay images computed from pre-operative 3-D volumetric data can be used to add anatomical detail otherwise not visible under X-ray. Unfortunately, current fluoro overlay images are static, i.e., they do not move synchronously with respiratory and cardiac motion. A filter-based catheter tracking approach using simultaneous biplane fluoroscopy was previously presented. It requires localization of a circumferential tracking catheter, though. Unfortunately, the initially proposed method may fail to accommodate catheters of different size. It may also detect wrong structures in the presence of high background clutter. We developed a new learning-based approach to overcome both problems. First, a 3-D model of the catheter is reconstructed. A cascade of boosted classifiers is then used to segment the circumferential mapping catheter. Finally, the 3-D motion at the site of ablation is estimated by tracking the reconstructed model in 3-D from biplane fluoroscopy. We compared our method to the previous approach using 13 clinical data sets and found that the 2-D tracking error improved from 1.0 mm to 0.8 mm. The 3-D tracking error was reduced from 0.8 mm to 0.7 mm.

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Section: Motivationmentioning

confidence: 99%

Catheter Tracking: Filter-Based vs. Learning-Based

Brost

Wimmer

Liao

et al. 2010

Lecture Notes in Computer Science

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“…However, this algorithm failed detecting the microcatheter in some images. PaltiWasserman et al [15] presented an algorithm based on the Hough transform for tracking the guide wire in the coronary arteries during angioplasty for monitoring the myocardial function.…”

Section: Introductionmentioning

confidence: 99%

Aortic valve prosthesis tracking for transapical aortic valve implantation

et al. 2010

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Purpose Transapical aortic valve implantation (TA-AVI) is a new minimally invasive surgical treatment of aortic stenosis for high risk patients. The placement of aortic valve prosthesis (AVP) is performed under 2D Xray fluoroscopic guidance. Difficult clinical complications can arise if the implanted valve is misplaced. Therefore we present a method to track the AVP in 2D X-ray fluoroscopic images in order to improve the accuracy of the TA-AVI. MethodsThe proposed tracking method includes the template matching approach to estimate the position of AVP and a shape model of the prosthesis to extract the corner points of the AVP in each image of sequence. To start the AVP tracking procedure, an initialization step is performed by manually defining the corner points of the prosthesis in the first image of sequence to provide the required algorithm parameters such as the AVP model parameters.Results We evaluated the AVP tracking method on six 2D intra-operative fluoroscopic image sequences. The results of automatic AVP localization agree well with manually-defined AVP positions. The maximum localization errors of tracked prosthesis are less than 1 mm and within the clinical accepted range.Conclusions For assisting the TA-AVI, a method for tracking the AVP in 2D X-ray fluoroscopic image sequences has been developed. Our AVP tracking method is a first step towards automatic optimal placement of the AVP during the TA-AVI.

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“…We assumed that the movement of the tracked prosthesis may be used to determine the motion of the aortic root as similarly proposed in [11]- [12]. Therefore, the coronary ostia can be also tracked even without the contrast agent.…”

Section: ) Avp Trackingmentioning

confidence: 99%

Towards a new image guidance system for assisting transapical minimally invasive aortic valve implantation

Karar

Gessat

Walther

et al. 2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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We propose a new image guidance system for assisting transapical minimally invasive aortic valve implantation. The goal is to define the exact positioning of aortic valve prosthesis, preventing the misplacement of the valve. The proposed system consists of two stand-alone modules. First, preoperative planning software uses DynaCT images with manual anatomical landmarks to calculate the size and optimal position of the prosthesis. Second, an intraoperative system is developed for tracking of the prosthesis and the coronary ostia in 2-D fluoroscopic images. Then the safe area of implantation is defined. The preliminary experimental results of preoperative planning and intraoperative tracking system are promising.Abstract-We propose a new image guidance system for assisting transapical minimally invasive aortic valve implantation. The goal is to define the exact positioning of aortic valve prosthesis, preventing the misplacement of the valve. The proposed system consists of two stand-alone modules. First, preoperative planning software uses DynaCT images with manual anatomical landmarks to calculate the size and optimal position of the prosthesis. Second, an intraoperative system is developed for tracking of the prosthesis and the coronary ostia in 2-D fluoroscopic images. Then the safe area of implantation is defined. The preliminary experimental results of preoperative planning and intraoperative tracking system are promising.

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Identifying and tracking a guide wire in the coronary arteries during angioplasty from X-ray images

Cited by 48 publications

References 29 publications

Catheter Tracking: Filter-Based vs. Learning-Based

Catheter Tracking: Filter-Based vs. Learning-Based

Aortic valve prosthesis tracking for transapical aortic valve implantation

Towards a new image guidance system for assisting transapical minimally invasive aortic valve implantation

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