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Abstract #0675

Consistency in human and machine-learning based scan-planes for clinical knee MRI planning

Chitresh Bhushan1, Dattesh D. Shanbhag2, Andre Maximo3, Uday Patil2, Radhika Madhavan1, Matthew Frick4, Kimberly K. Amrami4, Desmond Teck Beng yeo1, and Thomas Foo1
1GE Research, Niskayuna, NY, United States, 2GE Healthcare, Bengaluru, India, 3GE Healthcare, Rio de Janeiro, Brazil, 4Mayo Clinic, Rochester, MN, United States

We evaluate the consistency and clinical applicability of our automated deep-learning based intelligent slice placement (ISP) approach for knee scan planning. We use 146 clinical knee exams that were retrospectively selected to have anatomically consistent scan planning along with manual-marking from in-house radiologist to access the variability across MR technicians. The results indicate that our automated ISP approach has better consistency than the variability seen across MR technicians for coronal and sagittal knee scan planning, indicating promising clinical applicability of our automated ISP approach.

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