Flow Network Based Cardiac Motion Tracking Leveraging Learned Feature Matching
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We present a novel cardiac motion tracking method where motion is modeled as flow through a network. The motion is subject to physiologically consistent constraints and solved using linear programming. An additional important contribution of our work is the use of a Siamese neural network to generate edge weights that guide the flow through the network. The Siamese network learns to detect and quantify similarity and dissimilarity between pairs of image patches corresponding to the graph nodes. Despite cardiac motion tracking being an inherently spatiotemporal problem, few methods reliably address it as such. Furthermore, many tracking algorithms depend on tedious feature engineering and metric refining. Our approach provides solutions to both of these problems. We benchmark our method against a few other approaches using a synthetic 4D echocardiography dataset and compare the performance of neural network based feature matching with other features. We also present preliminary results on data from 5 canine cases.
We are immensely thankful of many present and past members of Dr. Albert Sinusas’s lab, who were involved in the image acquisitions. This work was supported in part by the National Institute of Health (NIH) grant number R01HL121226.
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