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Abstract #1763

Deep Learning Based Joint MR Image Reconstruction and Under-sampling Pattern Optimization

Vihang Agarwal1, Yue Cao1, and James Balter1
1Radiation Oncology, University of Michigan, Ann Arbor, MI, United States

Accelerating MRI acquisition by under-sampling measurements in k-space and learning an image reconstruction model with high image quality is necessary to expand its clinical utilization. In this work, we explore joint optimization of under-sampling patterns and image reconstruction neural networks for aggressive sub-sampling of images that require very long acquisition times (e.g., FLAIR T2 weighted images). We propose Attention Residual Non-Local Networks (ARNL-Net) trained with an uncertainty based L1 loss function for producing high quality images. Initial experiments demonstrate the practicability of this method, with reconstructions demonstrating superior fidelity to fully sampled images as compared to random under-sampling schemes.

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