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Abstract #0670

A generalized deep learning framework for multi-landmark intelligent slice placement using standard tri-planar 2D localizers

Dattesh Dayanand Shanbhag1, Chitresh Bhushan2, Andre de Alm Maximo3, Arathi Sreekumari1, Dan W Rettmann4, Dawei Gui5, Anja Kammeier5, Uday Patil1, Rakesh Mullick1, Teck Beng Desmond Yeo2, and Thomas K Foo2

1GE Global Research, Bangalore, India, 2GE Global Research, Niskayuna, NY, United States, 3GE Healthcare, Rio de Janeiro, Brazil, 4GE Healthcare, Rochester, MN, United States, 5GE Healthcare, Waukesha, WI, United States

We demonstrate a deep learning-based MRI scan workflow for intelligent slice placement (ISP) for multiple brain landmarks (MSP, AC-PC, entire visual pathway, pituitary, IAC, hippocampus, TOF-Angiography) based on standard 2D tri-planar localizer images. Unlike prior approaches to automatic plane prescription, this method uses deep learning to determine all necessary planes without the need for explicit delineation of landmark structures and provides visual feedback to the user. For all the landmarks, we demonstrate that the proposed method can achieve landmark slice placement with mean distance error < 1 mm (N = 505) on localizer images itself and is comparable or better to slice placement obtained using higher resolution images. Results indicate excellent feasibility of the method for clinical usage.

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