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

This 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.