Navigation for fluoroscopy-guided cryo-balloon ablation procedures of atrial fibrillation

Bourier, Félix; Brost, Alexander; Kleinoeder, Andreas; Kurzendorfer, Tanja; Koch, Martin; Kiraly, Attila P.; Schneider, Hans-Juergen; Hornegger, Joachim; Strobel, Norbert; Kurzidim, Klaus

doi:10.1117/12.911446

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Further intra-procedural support can be provided by visualizing the position and dimensions of the cryo-balloon catheter overlaid onto the fluoroscopic images using overlay guidance systems (OGS) [2,3]. Based on the knowledge of its 3D XR marker position, vector of the catheter shaft, and diameter, several methods for the reconstruction of a 3D model of the cryo-balloon have been introduced with reported submillimeter levels of accuracy for sphere reconstruction [1,4,5]. Also, methods to reconstruct ellipsoids requiring three views [6] or spheroids from even a single image plane [7] have been reported.…”

Section: Introductionmentioning

confidence: 99%

Cryo-balloon catheter localization in X-Ray fluoroscopy using U-net

et al. 2021

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Purpose Automatic identification of interventional devices in X-ray (XR) fluoroscopy offers the potential of improved navigation during transcatheter endovascular procedures. This paper presents a prototype implementation of fully automatic 3D reconstruction of a cryo-balloon catheter during pulmonary vein isolation (PVI) procedures by deep learning approaches. Methods We employ convolutional neural networks (CNN) to automatically identify the cryo-balloon XR marker and catheter shaft in 2D fluoroscopy during PVI. Training data are generated exploiting established semiautomatic techniques, including template-matching and analytical graph building. A first network of U-net architecture uses a single grayscale XR image as input and yields the mask of the XR marker. A second network of the similar architecture is trained using the mask of the XR marker as additional input to the grayscale XR image for the segmentation of the cryo-balloon catheter shaft mask. The structures automatically identified in two 2D images with different angulations are then used to reconstruct the cryo-balloon in 3D. Results Automatic identification of the XR marker was successful in 78% of test cases and in 100% for the catheter shaft. Training of the model for prediction of the XR marker mask was successful with 3426 training samples. Incorporation of the XR marker mask as additional input for the model predicting the catheter shaft allowed to achieve good training result with only 805 training samples. The average prediction time per frame was 14.47 ms for the XR marker and 78.22 ms for the catheter shaft. Localization accuracy for the XR marker yielded on average 1.52 pixels or 0.56 mm. Conclusions In this paper, we report a novel method for automatic detection and 3D reconstruction of the cryo-balloon catheter shaft and marker from 2D fluoroscopic images. Initial evaluation yields promising results thus indicating the high potential of CNNs as alternatives to the current state-of-the-art solutions.

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Section: Introductionmentioning

confidence: 99%

Cryo-balloon catheter localization in X-Ray fluoroscopy using U-net

et al. 2021

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“…In addition, electro-anatomical mapping systems may be used as well [11]. As of now, there is no mapping system that offers support for cryo-balloon ablation procedures, as they do not carry any electrodes or position sensors [12,13]. Recent developments utilize image processing techniques to facilitate cryo-balloon navigation involving augmented fluoroscopy techniques [12,14,15].…”

Section: Motivationmentioning

confidence: 99%

Cryo-balloon catheter localization in fluoroscopic images

et al. 2013

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Abstract. Minimally invasive catheter ablation has become the preferred treatment option for atrial fibrillation. Although the standard ablation procedure involves ablation points set by radio-frequency catheters, cryo-balloon catheters have even been reported to be more advantageous in certain cases. As electro-anatomical mapping systems do not support cryo-balloon ablation procedures, X-ray guidance is needed. However, current methods to provide support for cryo-balloon catheters in fluoroscopically guided ablation procedures rely heavily on manual user interaction. To improve this, we propose a first method for automatic cryo-balloon catheter localization in fluoroscopic images based on a blob detection algorithm. Our method is evaluated on 24 clinical images from 17 patients. The method successfully detected the cryoballoon in 22 out of 24 images, yielding a success rate of 91.6 %. The successful localization achieved an accuracy of 1.00 mm ± 0.44 mm. Even though our methods currently fails in 8.4 % of the images available, it still offers a significant improvement over manual methods. Furthermore, detecting a landmark point along the cryo-balloon catheter can be a very important step for additional post-processing operations.

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Cryo-Balloon Catheter Localization Based on a Support-Vector-Machine Approach

Kurzendorfer

Mewes

Maier

et al. 2016

IEEE Trans. Med. Imaging

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Cryo-balloon catheters have attracted an increasing amount of interest in the medical community as they can reduce patient risk during left atrial pulmonary vein ablation procedures. As cryo-balloon catheters are not equipped with electrodes, they cannot be localized automatically by electro-anatomical mapping systems. As a consequence, X-ray fluoroscopy has remained an important means for guidance during the procedure. Most recently, image guidance methods for fluoroscopy-based procedures have been proposed, but they provide only limited support for cryo-balloon catheters and require significant user interaction. To improve this situation, we propose a novel method for automatic cryo-balloon catheter detection in fluoroscopic images by detecting the cryo-balloon catheter's built-in X-ray marker. Our approach is based on a blob detection algorithm to find possible X-ray marker candidates. Several of these candidates are then excluded using prior knowledge. For the remaining candidates, several catheter specific features are introduced. They are processed using a machine learning approach to arrive at the final X-ray marker position. Our method was evaluated on 75 biplane fluoroscopy images from 40 patients, from two sites, acquired with a biplane angiography system. The method yielded a success rate of 99.0% in plane A and 90.6% in plane B, respectively. The detection achieved an accuracy of 1.00 mm±0.82 mm in plane A and 1.13 mm±0.24 mm in plane B. The localization in 3-D was associated with an average error of 0.36 mm±0.86 mm.

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Navigation for fluoroscopy-guided cryo-balloon ablation procedures of atrial fibrillation

Cited by 3 publications

References 17 publications

Cryo-balloon catheter localization in X-Ray fluoroscopy using U-net

Cryo-balloon catheter localization in X-Ray fluoroscopy using U-net

Cryo-balloon catheter localization in fluoroscopic images

Cryo-Balloon Catheter Localization Based on a Support-Vector-Machine Approach

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