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Abstract #3603

ODE-based Deep Network for MRI Reconstruction

Ali Pour Yazdanpanah1,2, Onur Afacan1,2, and Simon K. Warfield1,2
1Harvard Medical School, Boston, MA, United States, 2Boston Children's Hospital, Boston, MA, United States

Fast data acquisition in MRI is vastly in demand and scan time depends on the number of acquired k-space samples. The data-driven methods based on deep networks have resulted in promising improvements, compared to the conventional methods. The connection between deep network and Ordinary Differential Equation (ODE) has been studied recently. Here, we propose an ODE-based deep network for MRI reconstruction to enable the rapid acquisition of MR images with improved image quality. Our results with undersampled data demonstrate that our method can deliver higher quality images in comparison to the reconstruction methods based on the UNet and Residual networks.

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