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Abstract #1362

Learning-based automatic field-of-view positioning for fetal-brain MRI

Malte Hoffmann1,2, Daniel C Moyer3, Lawrence Zhang3, Polina Golland3, Borjan Gagoski1,4, P Ellen Grant1,4, and André JW van der Kouwe1,2
1Department of Radiology, Harvard Medical School, Boston, MA, United States, 2Department of Radiology, Massachusetts General Hospital, Boston, MA, United States, 3Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States, 4Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States

Unique challenges of fetal-brain MRI include successful acquisition of standard sagittal, coronal and axial views of the brain, as motion precludes acquisition of coherent orthogonal slice stacks. Technologists repeat scans numerous times by manually rotating slice prescriptions but inaccuracies in slice placement and intervening motion limit success. We propose a system to automatically prescribe slices based on the fetal-head pose as estimated by a neural network from a fast scout. The target sequence receives the head pose and acquires slices accordingly. We demonstrate automatic acquisition of standard anatomical views in-vivo.

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