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Abstract #1510

All you need are DICOM images

Guanxiong Luo1, Moritz Blumenthal1, Xiaoqing Wang1,2, and Martin Uecker1,2,3,4
1University Medical Center Göttingen, Göttingen, Germany, 2German Centre for Cardiovascular Research (DZHK), Partner Site Göttingen, Göttingen, Germany, 3Institute of Medical Imaging, Graz University of Technology, Graz, Austria, 4Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells'' (MBExC), University of Göttingen, Göttingen, Germany


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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