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Abstract #4362

Model-Based Deep Learning for Reconstruction of Joint k-q Under-Sampled Diffusion MRI

Merry P. Mani1, Hemant Kumar Aggarwal2, Sanjay Ghosh1, and Mathews Jacob2
1Department of Radiology, University of Iowa, Iowa City, IA, United States, 2Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, United States

We propose a model-based deep learning architecture for the reconstruction of highly accelerated diffusion MRI. We introduce the use of a pre-trained denoiser as the regularizer in a model-based recovery for diffusion weighted data from k-q under-sampled acquisition in a parallel MRI setting. The denoiser is designed based on a general tissue microstructure diffusion signal model with multi-compartmental modeling. A neural network was trained in an unsupervised manner using a convolutional auto-encoder to learn the diffusion MRI signal subspace. To demonstrate the acceleration capabilities of the proposed method, we perform MRI reconstruction experiments on a simulated brain dataset.

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