Abstract #0839

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

Sayeri Lala¹, Nalini Singh^2,3, Borjan Gagoski^4,5, Esra Turk⁴, P. Ellen Grant^4,5, Polina Golland^1,3, and Elfar Adalsteinsson^1,2,6

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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