title: A novel Bayesian respiratory motion model to estimate and resolve uncertainty in image-guided cardiac interventions.
abstract: 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.
authors.affiliation: Division of Imaging Sciences and Biomedical Engineering, King's College London, King's Health Partners, St. Thomas' Hospital, London SE1 7EH, United Kingdom. devis.peressutti@kcl.ac.uk
authors.forename: Devis
authors.initials: D
authors.lastname: Peressutti
authors.forename: Graeme P
authors.initials: GP
authors.lastname: Penney
authors.forename: R James
authors.initials: RJ
authors.lastname: Housden
authors.forename: Christoph
authors.initials: C
authors.lastname: Kolbitsch
authors.forename: Alberto
authors.initials: A
authors.lastname: Gomez
authors.forename: Erik-Jan
authors.initials: EJ
authors.lastname: Rijkhorst
authors.forename: Dean C
authors.initials: DC
authors.lastname: Barratt
authors.forename: Kawal S
authors.initials: KS
authors.lastname: Rhode
authors.forename: Andrew P
authors.initials: AP
authors.lastname: King
mesh_terms: D016477:Artifacts; D001499:Bayes Theorem; D006348:Cardiac Surgical Procedures / Q000379:methods*; D003198:Computer Simulation; D019560:Echocardiography, Three-Dimensional / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008954:Models, Biological; D009068:Movement; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D015656:Respiratory Mechanics / Q000502:physiology*; D055425:Respiratory-Gated Imaging Techniques / Q000379:methods*; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000379:methods*