A subject-specific technique for respiratory motion correction in image-guided cardiac catheterisation procedures

King, Andrew P.; Boubertakh, Redha; Rhode, Kawal; Ma, Yongqiang; Chinchapatnam, Phani; Gao, Gang; Tangcharoen, Tarinee; Ginks, Matthew; Cooklin, Michael; Gill, Jaswinder; Hawkes, David J.; Razavi, Reza; Schaeffter, Tobias

doi:10.1016/j.media.2009.01.003

Cited by 75 publications

(77 citation statements)

References 19 publications

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“…Currently, the 3D roadmap remains static and does not move with the patient's respiratory motion. In some cases, respiratory motion can cause a two-dimensional (2D) registration error of over 14 mm [2], which is a significant compromise in the accuracy of guidance. A number of groups have previously addressed the issue of respiratory motion correction for cardiac interventions.…”

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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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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“…This registration process results in 12 affine parameters for each dynamic image. 2 nd order polynomial curves are fitted in a least-squares sense to each affine parameter as a function of the respiratory surrogate signal (King et al, 2009). Given a value s of the surrogate acquired in the intra-procedure setting, the MRI-derived motion model outputs a twelve parameter affine transformation, denoted byθ(s).…”

Section: Prior Probabilitymentioning

confidence: 99%

“…model-only: a surrogate-driven model estimate (King et al, 2009). Given the surrogate value s, the motion estimate isθ(s).…”

Section: Comparison Of Estimation Techniquesmentioning

confidence: 99%

“…The typical approach is to model the respiratory motion as a direct function of some physically measurable input signal(s), or respiratory surrogate(s). During the intervention, only the surrogate data need be acquired, and the motion model produces a motion estimate based on this data (Manke et al, 2003;Shechter et al, 2005;King et al, 2009;Klinder et al, 2010). Motion models which use this approach are often referred to as surrogate-driven motion models.…”

Section: Introductionmentioning

confidence: 99%

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A novel Bayesian respiratory motion model to estimate and resolve uncertainty in image-guided cardiac interventions

Peressutti

Penney

Housden

et al. 2013

Medical Image Analysis

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(2013). A novel Bayesian respiratory motion model to estimate and resolve uncertainty in image-guided cardiac interventions. Medical Image Analysis, 17(4), 488-502. https://doi.org/10.1016/j.media.2013.01.007Citing this paper Please note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections. General rightsCopyright and moral rights for the publications made accessible in the Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognize and abide by the legal requirements associated with these rights.•Users may download and print one copy of any publication from the Research Portal for the purpose of private study or research.•You may not further distribute the material or use it for any profit-making activity or commercial gain •You may freely distribute the URL identifying the publication in the Research Portal Take down policy If you believe that this document breaches copyright please contact librarypure@kcl.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. The copyright in the published version resides with the publisher.When referring to this paper, please check the page numbers in the published version and cite these.General rights Copyright and moral rights for the publications made accessible in King's Research Portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications in King's Research Portal that users recognise and abide by the legal requirements associated with these rights.'• Users may download and print one copy of any publication from King's Research Portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the King's Research Portal Take down policy If you believe that this document breaches copyright please contact librarypure@kcl.ac.uk providing details, and we will remove access to the work immediately and investigate your claim. Citation to published version:Peressutti, D., Penney, G. P., Housden, R. J., Kolbitsch, C., Gomez, A., Rijkhorst, E-J., Barratt, D. C., Rhode, K. S., & King, A. P. (2013 AbstractIn image-guided cardiac interventions, respiratory motion causes misalignments between the pre-procedure roadmap of the heart used for guidance and the intra-procedure position of the heart, reducing the accuracy of the guidance information and leading to potentially dangerous consequences. We propose a novel technique for...

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“…Manke et al [4] proposed a linear parametric model describing the relation between the variation of the diaphragmatic position and the respiratory-induced motion derived from image-intensity based registration for cardiac-triggered 3D MR imaging. King et al [5] present an affine model which is based on the tracked motion of the diaphragm to compensate for respiratory motion in real-time X-ray images. The main drawback of these approaches is that they require manual landmark selection for diaphragm tracking.…”

Section: Introductionmentioning

confidence: 99%

Automatic Image-Based Cardiac and Respiratory Cycle Synchronization and Gating of Image Sequences

Sundar

Khamene

Yatziv

et al. 2009

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2009

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Abstract. We propose a novel method to detect the current state of the quasi-periodic system from image sequences which in turn will enable us to synchronize/gate the image sequences to obtain images of the organ system at similar configurations. The method uses the cumulated phase shift in the spectral domain of successive image frames as a measure of the net motion of objects in the scene. The proposed method is applicable to 2D and 3D time varying sequences and is not specific to the imaging modality. We demonstrate its effectiveness on X-Ray Angiographic and Cardiac and Liver Ultrasound sequences. Knowledge of the current (cardiac or respiratory) phase of the system, opens up the possibility for a purely image based cardiac and respiratory gating scheme for interventional and radiotherapy procedures.

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A subject-specific technique for respiratory motion correction in image-guided cardiac catheterisation procedures

Cited by 75 publications

References 19 publications

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

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

A novel Bayesian respiratory motion model to estimate and resolve uncertainty in image-guided cardiac interventions

Automatic Image-Based Cardiac and Respiratory Cycle Synchronization and Gating of Image Sequences

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