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Abstract #3653

Improving the segmentation over time of 4D flow MRI images, using a medical image registration neural network VoxelMorph

Aaron Christhoper Ponce1,2, Sergio Uribe2,3,4,5, and Julio Sotelo2,5,6
1School of Informatics Engineering, Universidad de Valparaíso, Valparaíso, Chile, 2Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Santiago, Chile, 3Department of Radiology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile, 4Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile, 5Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile, 6School of Biomedical Engineering, Universidad de Valparaíso, Valparaíso, Chile

Synopsis

Keywords: Flow, SegmentationThe segmentation of large vessels in 4D Flow MRI remains a challenge, due to different problems such as random acquisition noise, low spatial and temporal resolution, velocity aliasing, respiratory motion, phase offsets. For that reason the use of a single segmentation has been standardized to represent the geometry throughout all the cardiac phases. However, recent studies have proposed the use of image registration to be able to solve this problem. In this work, an algorithm based on a medical image registration neural network is proposed to improve the segmentation over time for the cardiac phases of the systole period.

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