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Abstract #0839

A Deep Learning Approach for Image Quality Assessment of Fetal Brain MRI

Sayeri Lala1, Nalini Singh2,3, Borjan Gagoski4,5, Esra Turk4, P. Ellen Grant4,5, Polina Golland1,3, and Elfar Adalsteinsson1,2,6

1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, 2Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, 3Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology, Cambridge, MA, United States, 4Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, MA, United States, 5Harvard Medical School, Boston, MA, United States, 6Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States

Fetal MRI plays a critical role in diagnosing brain abnormalities but routinely suffers from artifacts resulting in nondiagnostic images. We aim to automatically identify nondiagnostic images during acquisition so they can be immediately flagged for reacquisition. As a first step, we trained a neural network to classify T2-weighted single-shot fast spin-echo (HASTE) images as diagnostic or nondiagnostic. With novel data, the average Area Under Receiver Operator Characteristic Curve was 0.84 (σ = 0.04). The neural network learned relevant criteria, identifying high contrast boundaries between areas like cerebral spinal fluid and cortical plate as relevant to determining image quality.

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