Most deep-learning-based reconstructions methods need predefined sampling patterns and precomputed coil sensitivities for supervised training, limiting their later use in applications under different conditions. Furthermore, only the magnitude images are always stored in DICOM format in the Picture Archiving and Communication System (PACS) of a typical radiology department. That means that raw k-space data is usually not available. This work focuses on how to extract prior knowledge from magnitude images (DICOM) and how to apply the extracted prior to reconstruct images from k-space multi-channel data sampled with different schemes.
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