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Abstract #1051

Machine Enhanced Reconstruction Learning and Interpretation Networks (MERLIN)

Kerstin Hammernik1,2 and Thomas Küstner3
1Lab for AI in Medicine, Technical University of Munich, Munich, Germany, 2Department of Computing, Imperial College London, London, United Kingdom, 3Medical Image And Data Analysis (MIDAS.lab), Department of Interventional and Diagnostic Radiology, University Hospital of Tuebingen, Tuebingen, Germany


Machine Learning (ML) methods have evolved tremendously during the last decade. A number of frameworks support the development of new ML methods. However, support for high-dimensional and complex-valued data processing is often limited. Therefore, we propose a Machine Enhanced Reconstruction Learning and Interpretation Networks (MERLIN) framework that seamlessly integrates with existing ML solutions such as Tensorflow/Keras and Pytorch and complements them by high-dimensional, complex-valued and MR-specific operators and layers. Furthermore, standard data processing pipelines in Python are provided in the context of MRI reconstruction.

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