Abstract #2007

Iterative Motion-Compensated Reconstruction with Convolutional Neural Network (iMoCo-Net) for Ultrashort Echo Time (UTE) Proton Lung MRI

Fei Tan¹, Ke Wang², Michael Lustig^1,2, and Peder E. Z. Larson^1,3

¹UC Berkeley-UCSF Graduate Program in Bioengineering, University of California Berkeley and University of California, San Francisco, San Francisco, CA, United States, ²Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States, ³Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States

Synopsis

Keywords: Motion Correction, Motion CorrectionThis abstract explores the feasibility of machine learning-based motion-compensated reconstruction for free-breathing UTE lung MRI. Specifically, we used respiratory motion-resolved Non-uniform Fourier Transform (NuFFT) reconstruction as input, iterative motion-compensated (iMoCo) reconstruction as target, and a 2D U-Net convolutional neural network. Test results demonstrate a sharper diaphragm and a higher apparent SNR compared to the averaged input. In conclusion, iMoCo-Net accelerates the reconstruction of 3D radial UTE data substantially, shortening the required time from hours to minutes.

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