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Abstract #4247

Deep Learning-Based Needle Tracking Trained on Bloch-Simulated Data and Evaluated on Clinical Real-Time bSSFP Images

Ralf Vogel1,2, Dieter Ritter2, Jonathan Weine2,3, Jonas Faust2,4, Elodie Breton5, Julien Garnon5,6, Afshin Gangi5,6, Andreas Maier1, and Florian Maier2
1Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 2Siemens Healthcare, Erlangen, Germany, 3TU Dortmund, Dortmund, Germany, 4Universität Heidelberg, Heidelberg, Germany, 5ICube UMR7357, University of Strasbourg, CNRS, FMTS, Strasbourg, France, 6Imagerie Interventionnelle, Hôpitaux Universitaires de Strasbourg, Strasbourg, France

Recently, Deep Learning-based methods were used to track the position and orientation of needles in MR images in real-time. Synthetic training data can be generated in large amounts, without data privacy restrictions, and without the need of animal experiments. Therefore, we have simulated the image acquisition using virtual human phantoms containing randomly placed metallic needles in a Bloch simulator. The synthetic images were used to train a U-net to predict the position and orientation of the needle within the susceptibility artifacts of clinical images in less than $$$90\,\text{ms}$$$.

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