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Abstract #0250

Task-Based UltraFast MRI: Simultaneous Image Reconstruction and Tissue Segmentation

Francesco Caliva1, Adam Noworolski2, Andrew Leynes1,3, Claudia Iriondo1,3, Sharmila Majumdar1, Peder Larson1, and Valentina Pedoia1
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States, 2EECS, University of California, Berkeley, Berkeley, CA, United States, 3Department of Bioengineering, University of California, Berkeley, Berkeley, CA, United States

We propose a novel task based deep learning framework for simultaneous MRI reconstruction and segmentation. On a dataset of retrospectively undersampled knee-DESS volumes we demonstrate that irrespective of ultra-high acceleration factors (i.e. 48×) a multitask 3D encoder-decoder is capable of reconstructing with high fidelity the knee MRI, accurately segment cartilaginous and meniscal tissues and reliably provide cartilage thickness. Our multitask solution outperforms two other methods: a compressed sensing reconstruction step, followed by a deep learning-based tissue segmentation. The other method comprises a cascade of two convolutional neural networks that sequentially perform image reconstruction and segmentation.

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