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Abstract #4772

A Deep Learning Accelerated MRI Reconstruction Model's Dependence on Training Data Distribution

Dimitrios Karkalousos1, Kai Lønning2, Serge Dumoulin3, Jan-Jakob Sonke4, and Matthan W.A. Caan5

1Spinoza Centre for Neuroimaging, Netherlands, Netherlands, 2Department of Radiation Oncology, the Netherlands Cancer Institute & Spinoza Centre for Neuroimaging, Amsterdam, Netherlands, 3Spinoza Centre for Neuroimaging, Amsterdam, Netherlands, 4Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, Netherlands, 5Academic Medical Center, Amsterdam, Netherlands

Recurrent Inference Machines (RIM) are deep learning inverse problem solvers that have been shown to generalize well to anatomical structures and contrast settings it was not exposed to during training. This makes RIMs ideal for accelerated MRI reconstruction, where the variation in acquisition settings is high. Using T1- and T2*-weighted brain scans and T2-weighted knee scans, we compare the RIM's performance when trained on only a single type of data against the case where all three data types are present in the training set. We present results that show an overall model robustness, but also indicate a slight preference for training on all three types of data.

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