3D/2D model-to-image registration by imitation learning for cardiac procedures View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-05-12

AUTHORS

Daniel Toth, Shun Miao, Tanja Kurzendorfer, Christopher A. Rinaldi, Rui Liao, Tommaso Mansi, Kawal Rhode, Peter Mountney

ABSTRACT

PURPOSE: In cardiac interventions, such as cardiac resynchronization therapy (CRT), image guidance can be enhanced by involving preoperative models. Multimodality 3D/2D registration for image guidance, however, remains a significant research challenge for fundamentally different image data, i.e., MR to X-ray. Registration methods must account for differences in intensity, contrast levels, resolution, dimensionality, field of view. Furthermore, same anatomical structures may not be visible in both modalities. Current approaches have focused on developing modality-specific solutions for individual clinical use cases, by introducing constraints, or identifying cross-modality information manually. Machine learning approaches have the potential to create more general registration platforms. However, training image to image methods would require large multimodal datasets and ground truth for each target application. METHODS: This paper proposes a model-to-image registration approach instead, because it is common in image-guided interventions to create anatomical models for diagnosis, planning or guidance prior to procedures. An imitation learning-based method, trained on 702 datasets, is used to register preoperative models to intraoperative X-ray images. RESULTS: Accuracy is demonstrated on cardiac models and artificial X-rays generated from CTs. The registration error was [Formula: see text] on 1000 test cases, superior to that of manual ([Formula: see text]) and gradient-based ([Formula: see text]) registration. High robustness is shown in 19 clinical CRT cases. CONCLUSION: Besides the proposed methods feasibility in a clinical environment, evaluation has shown good accuracy and high robustness indicating that it could be applied in image-guided interventions. More... »

PAGES

1141-1149

References to SciGraph publications

  • 2008-01-11. Fusion of three-dimensional X-ray angiography and three-dimensional echocardiography in INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
  • 2017-01-24. Registration with Adjacent Anatomical Structures for Cardiac Resynchronization Therapy Guidance in STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. IMAGING AND MODELLING CHALLENGES
  • 2012. Automatic Segmentation of the Myocardium in Cine MR Images Using Deformable Registration in STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. IMAGING AND MODELLING CHALLENGES
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11548-018-1774-y

    DOI

    http://dx.doi.org/10.1007/s11548-018-1774-y

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1103962975

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/29754382


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