Abstract #3383

A Machine Learning Approach for Mitigating Artifacts in Fetal Imaging due to an Undersampled HASTE Sequence

Sayeri Lala¹, Borjan Gagoski², Jeffrey N. Stout³, Bo Zhao⁴, Berkin Bilgic⁴, Ellen P. Grant², Polina Golland⁵, and Elfar Adalsteinsson⁶

¹Electrical Engineering and Computer Science, MIT, Cambridge, MA, United States, ²Boston Children’s Hospital, Boston, MA, United States, ³IMES, MIT, Harvard-MIT Health Sciences and Technology, Cambridge, MA, United States, ⁴Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital, Boston, MA, United States, ⁵Electrical Engineering and Computer Science, MIT CSAIL, Cambridge, MA, United States, ⁶Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, MIT, Cambridge, MA, United States

This work investigates using deep learning to mitigate artifacts in fetal images resulting from accelerated acquisitions. We applied an existing deep learning framework to reconstruct undersampled HASTE images of the fetus. The deep learning architecture is a cascade of two convolutional neural networks combined with data consistency layers. Training and evaluation were performed on coil-combined and reconstructed HASTE images with retrospective undersampling. The datasets derived from imaging of ten pregnant subjects, GA 19-37 weeks, yielding 3994 HASTE slices. This approach mitigates artifacts from incoherent aliasing with residual reconstruction errors in high spatial frequency features in the phase encoding direction.

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