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Abstract #3719

Fetal brain T1-weighted imaging with a deep learning constrained compressed SENSE reconstruction

Jing Wang1, Zhuo Wang1, Yi Zhu2, and Ke Jiang2
1Radiology, the First Hospital of Jilin University, Changchun, China, 2Philips Healthcare, Beijing, China


The quality of commonly used fetal T1-weighed inversion recovery(IR) images is relatively poor. Compressed SENSE(CS) technique allows shortening of scan time, but the overall image quality has not been significantly improved. In this study, Compressed-SENSE Artificial intelligence(CS-AI) framework was applied to reduce the scan time and increase the spatial resolution. This study aims at acquiring high-resolution fetal brain T1-weighted image with reduced scan time and compare the image quality among images reconstructed with CS-AI, CS and conventional SENSE.

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