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Abstract #3639

Quantitative and Volumetric Assessment of a Deep Cascade Network for MR Reconstruction Under Different Acceleration Factors

Wallace Souza Loos1, Roberto Souza1, Mariana Bento1, Robert Marc Lebel1,2, and Richard Frayne1
1University of Calgary, Calgary, AB, Canada, 2General Electric Healthcare, Calgary, AB, Canada

Magnetic resonance (MR) imaging still has a high acquisition time due to inherent sequential procedure required to fill k-space. Deep-cascade networks have been used to reconstruct MR images from an under-sampled k-space in order to reduce acquisition time. In this work we investigate a deep-cascade to reconstruct MR images of the brain. We trained the network with 14 different acceleration factors (R). Relevant brain structures were preserved until R = 7x. For R ≥ 8x, MR images presented noticeable blurring artifact. The quality of the segmentation of the brain structures were similar to the reference MR image until R=9.

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