With increasing resolutions and number of acquisitions, medical imaging more and more requires computer support for interpretation as currently not all imaging data is fully used. In our work, we show how multi-channel images can be used for robust air masking and reliable muscle tissue detection in the human lower limb. We exploit additional channels that are usually discarded in clinical routine. We use the common mDixon acquisition protocol for MR imaging. A series of thresholding, morphological, and connectivity operations is used for processing. We demonstrate our fully automated approach on four subjects and present a comparison with manual labeling. We discuss how this work is used for advanced and intuitive visualization, the quantification of tissue types, pose estimation, initialization of further segmentation methods, and how it could be used in clinical environments.
['Matthias Becker', 'Nadia Magnenat-Thalmann']
Muscle Tissue Labeling of Human Lower Limb in Multi-Channel mDixon MR Imaging: Concepts and Applications