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

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)488-502
Number of pages15
JournalMedical Image Analysis
Issue number4
PublishedMay 2013


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

    MedIA_postprint_1.pdf, 2.59 MB, application/pdf

    Uploaded date:21 Jul 2015

    Version:Accepted author manuscript

    NOTICE: this is the author’s version of a work that was accepted for publication in Medical Image Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication.

King's Authors


In 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 motion-correcting the pre-procedural information that combines a probabilistic MRI-derived affine motion model with intra-procedure real-time 3D echocardiography (echo) images in a Bayesian framework. The probabilistic model incorporates a measure of confidence in its motion estimates which enables resolution of the potentially conflicting information supplied by the model and the echo data. Unlike models proposed so far, our method allows the final motion estimate to deviate from the model-produced estimate according to the information provided by the echo images, so adapting to the complex variability of respiratory motion. The proposed method is evaluated using gold-standard MRI-derived motion fields and simulated 3D echo data for nine volunteers and real 3D live echo images for four volunteers. The Bayesian method is compared to 5 other motion estimation techniques and results show mean/max improvements in estimation accuracy of 10.6%/18.9% for simulated echo images and 20.8%/41.5% for real 3D live echo data, over the best comparative estimation method.

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