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Abstract #3562

Intelligent Knee MRI slice placement by adapting a generalized deep learning framework

Chitresh Bhushan1, Dattesh D Shanbhag2, Andre de Alm Maximo3, Arathi Sreekumari2, Dawei Gui4, Uday Patil2, Brandon Pascual4, Rakesh Mullick2, Teck Beng Desmond Yeo1, and Thomas Foo1
1GE Global Research, Niskayuna, NY, United States, 2GE Healthcare, Bangalore, India, 3GE Healthcare, Rio de Janeiro, Brazil, 4GE Healthcare, Waukesha, WI, United States

We demonstrate a deep learning-based workflow for intelligent slice placement (ISP) in MR knee imaging: meniscus plane, femoral condyle plane, tibial plane, sagittal plane and ACL plane, based on standard 2D tri-planar localizer images. We leveraged a previously described generalized architecture for ISP planning in brain, with only the training data and plane definitions adapted for knee. The mean absolute distance error between GT plane and predicted plane was < 0.5 mm for all planes except tibial plane (~ 1 mm). The results indicate the generalization of deep-learning ISP framework and its suitability for ISP in any anatomy of interest.

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