Abstract #4994

Automated assessment of paraspinal muscles fat composition based on the segmentation of chemical shift encoding-based water/fat-separated images

Friedemann Freitag¹, Thomas Baum¹, Michael Dieckmeyer¹, Jan S. Kirschke², Holger Eggers³, Christian Buerger³, Cristian Lorenz³, and Dimitrios C. Karampinos¹

¹Department of Diagnostic and Interventional Radiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, ²Department of Diagnostic and Interventional Neuroradiology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany, ³Philips Research Laboratories, Hamburg, Germany

Chemical shift encoding-based water-fat MRI derived proton density fat fraction (PDFF) of the paraspinal muscles has been emerging as important surrogate marker in subjects with intervertebral disc disease, osteoporosis, sarcopenia, and neuromuscular disorders. However, measurements of paraspinal muscle PDFF are currently limited in clinical routine due to the required time-consuming manual segmentation procedure. The present study aimed to develop an automatic segmentation algorithm of the paraspinal muscles at the lumbar spine based on water-fat MRI and compared the performance of this algorithm to ground truth data based on manual segmentation.

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