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Abstract #2698

Recurrent U-Net Based Temporal Regularization for Dynamic Undersampled Reconstruction in OSSI fMRI

Shouchang Guo1 and Douglas C. Noll2
1Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, United States, 2Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States

Using deep learning for undersampled reconstruction has shown advantages for structural MRI and dynamic MRI, but is not commonly used for fMRI. In this work, we propose a neural network based reconstruction with temporal regularization to exploit the temporal redundancy of oscillating steady-state fMRI images. With a factor of 6 undersampling, the proposed method outperforms other approaches such as cascade of convolutional neural networks with high-resolution and high-quality fMRI results.

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