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Abstract #3787

Super-resolution Reconstruction of Fetal Brain MRI Through Learning of Multi-view Interpolation Weights

Shijie Huang1, Kai Zhang1, Zifeng Lian1, Geng Chen2, and Dinggang Shen1,3,4
1School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China, 2National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China, 3Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China, 4Shanghai Clinical Research and Trial Center, Shanghai, China

Synopsis

Keywords: Prenatal, Brain, Fetal; Brain; Super-resolution Reconstruction

Motivation: Super-resolution reconstruction of isotropic fetal brain MR images is critical for prenatal examinations, but is hindered by fetal motion and misalignment of thick-slice scans.

Goal(s): This study aims to quantify misalignment between slices and volumes while predicting isotropic high-resolution volumetric images.

Approach: Our approach learns an implicit function to quantify misalignment between MR slices and volumes by obtaining interpolation weights from latent codes of multi-view motion-corrupted thick-slice stacks. The established model then interpolates the isotropic high-resolution image by utilizing predicted weights.

Results: Our proposed method effectively mitigates the adverse effects of motion corruption in fetal brain MRI while substantially reducing reconstruction time.

Impact: Our end-to-end fetal brain super-resolution approach bypasses traditional two-step iterative optimization paradigm, and substantially reduces reconstruction time. It eliminates the need for slice-to-volume registration, and shifts the focus of implicit neural representation from addressing appearance estimation issues to motion estimation.

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Keywords