Abstract #4512

Efficient Fetal Brain Segmentation according to the Point Spread Function of MRI

Yunzhi Xu¹, Jiaxin Li¹, Xue Feng², Kun Qing³, Dan Wu¹, and Li Zhao¹

¹Zhejiang University, Hangzhou, China, ²Biomedical Engineering, University of Virginia, Virginia, VA, United States, ³Department of Radiation Oncology, City of Hope National Center, Los Angeles, CA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Fetus, Fetal Brain MRI，Point Spread FunctionHigh apparent resolution of fetal MRIs is provided by slice-to-volume reconstruction pipelines widely. However, the physical resolution of the fetal brain is lower than that. Therefore, we hypothesize that fetal brain segmentation can be performed based on downsampled fetal brain MRI according to its point spread function. In this work, 150 adult brain and 80 fetal brain MRIs were used to validate hypothesize. Using downsampled fetal data with factor of 4, a highly efficient segmentation model achieved similar segmentation accuracies compared to original data, which demonstrated that segmentation models can be developed based on PSF.

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