2014 Project Week:AblationSuccessRatePredictionUsingJointImageAndShapeAnalysis

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Key Investigators

  • Yi Gao, LiangJia Zhu, Josh Cates, Rob MacLeod, Sylvain Bouix, Ron Kikinis, Allen Tannenbaum


Among the AFib patients underwent RF ablation, the relative high AFib recurrence rate is a concern. We combine both the image and shape information for the purpose of predicting the RF ablation success rate.

Approach, Plan

The fibrosis distributions on the left atrium wall is imaged using the dynamic contrast enhanced MRI. Distributed on different anatomical structures, they are considered as "mass" defined on different domains. Under the framework of the optimal mass transport (OMT), the masses are transported to a common domain where the statistical analysis can then be applied. The significant different regions are then characterized by the low-p-value regions.


  • C++ code ready
  • Six months
    • Discuss with DBP collaborators and start writing the manuscript
    • Started constructing the CLI extension
    • Documentation
  • Beyond
    • Continue work on the image/shape analysis and success rate prediction

Delivery Mechanism

This work will be delivered to the NA-MIC Kit as a commandline extension.


  • Utah DBP
  • Y. Gao, A. Tannenabum, S. Bouix; "A Framework for Joint Image-and-Shape Analysis"; SPIE Medical Imaging. 2014
  • Y. Gao, Y. Rathi, S. Bouix, A. Tannenbaum; Filtering in the Diffeomorphism Group and the Registration of Point Sets; IEEE Transactions on Image Processing 21 (10), 4383--4396
  • Y. Gao and S. Bouix, Synthesis of realistic subcortical anatomy with known surface deformations; in MICCAI Workshop on Mesh Processing in Medical Image Analysis, 2012, pp. 80–88.