Motion compensated coronary interventional navigation by means of diaphragm tracking and elastic motion models

Timinger, Holger; Krueger, Sascha; Dietmayer, Klaus; Borgert, J.

doi:10.1088/0031-9155/50/3/007

Cited by 29 publications

(20 citation statements)

References 21 publications

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“…For cardiac interventions including PCI, the organ motion is mainly affected by respiratory and cardiac motion. Many previous works often built a motion model parameterized by a cardiac signal derived from ECG and a respiratory signal obtained from diaphragm tracking ( [37,42,13]) or automatic PCAbased surrogate ( [15]). Some other works model only the respiratory motion in cardiac-gated images ( [35,21,30]).…”

Section: Dynamic Coronary Roadmappingmentioning

confidence: 99%

Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering

Smal

Daemen

et al. 2020

Medical Image Analysis

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Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified with X-ray opaque contrast agents. Interventional cardiologists typically navigate instruments using non-contrast-enhanced fluoroscopic images, since higher use of contrast agents increases the risk of kidney failure. When using fluoroscopic images, the interventional cardiologist needs to rely on a mental anatomical reconstruction. This paper reports on the development of a novel dynamic coronary roadmapping approach for improving visual feedback and reducing contrast use during PCI. The approach compensates cardiac and respiratory induced vessel motion by ECG alignment and catheter tip tracking in X-ray fluoroscopy, respectively. In particular, for accurate and robust tracking of the catheter tip, we proposed a new deep learning based Bayesian filtering method that integrates the detection outcome of a convolutional neural network and the motion estimation between frames using a particle filtering framework. The proposed roadmapping and tracking approaches were validated on clinical X-ray images, achieving accurate performance on both catheter tip tracking and dynamic coronary roadmapping experiments. In addition, our approach runs in real-time on a computer with a single GPU and has the potential to be integrated into the clinical workflow of PCI procedures, providing cardiologists with visual guidance during interventions without the need of extra use of contrast agent.

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Section: Dynamic Coronary Roadmappingmentioning

confidence: 99%

Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering

Smal

Daemen

et al. 2020

Medical Image Analysis

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“…A number of groups have previously addressed the issue of respiratory motion correction for cardiac interventions. Motion-compensated navigation for coronary interventions based on magnetic tracking was suggested in [3], but it required additional special hardware. Several image-based approaches have been developed that use only information from the Xray fluoroscopic images themselves.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Respiratory Motion Correction for Cardiac Electrophysiology Procedures Using Image-Based Coronary Sinus Catheter Tracking

King

Gogin

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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Abstract. X-ray fluoroscopically guided cardiac electrophysiological procedures are routinely carried out for diagnosis and treatment of cardiac arrhythmias. X-ray images have poor soft tissue contrast and, for this reason, overlay of static 3D roadmaps derived from pre-procedural volumetric data can be used to add anatomical information. However, the registration between the 3D roadmap and the 2D X-ray data can be compromised by patient respiratory motion. We propose a novel method to correct for respiratory motion using real-time image-based coronary sinus (CS) catheter tracking. The first step of the proposed technique is to use a blob detection method to detect all possible catheter electrodes in the Xray data. We then compute a cost function to select one CS catheter from all catheter-like objects. For correcting respiratory motion, we apply a low pass filter to the 2D motion of the CS catheter and update the 3D roadmap using this filtered motion. We tested our CS catheter tracking method on 1048 fluoroscopy frames from 15 patients and achieved a success rate of 99.3% and an average 2D tracking error of 0.4 mm ± 0.2 mm. We also validated our respiratory motion correction strategy by computing the 2D target registration error (TRE) at the pulmonary veins and achieved a TRE of 1.6 mm ± 0.9 mm.

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“…The results for the compensation of data1 and data2 can be compared to a compensation using a free form deformation model as described in [6,7] using radial basis functions [8] for interpolation. This compensation resulted in a residual displacement of 1.04 mm F0.91 mm for data1 and 1.99 mm F 1.09 mm for data2.…”

Section: Resultsmentioning

confidence: 99%

Respiratory motion compensation with tracked internal and external sensors during CT guided procedures

Borgert

Krüger

Timinger

et al. 2005

International Congress Series

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Motion compensated coronary interventional navigation by means of diaphragm tracking and elastic motion models

Cited by 29 publications

References 21 publications

Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering

Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering

Real-Time Respiratory Motion Correction for Cardiac Electrophysiology Procedures Using Image-Based Coronary Sinus Catheter Tracking

Respiratory motion compensation with tracked internal and external sensors during CT guided procedures

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