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.
This abstract and the presentation materials are available to members only; a login is required.