Meeting Banner
Abstract #4171

Water Fat Identification for Symmetically Acquired Dixon Method on Non-Connected Body Regions

Cong Zhao1, Andrew Liang2

1Siemens ShenZhen Magnetic Resonance, ShenZhen, GuangDong, China; 2School of Engineering and Applied Science, University of Pennsylvania

The work provides a post-processing algorithm classifying water and fat images generated by symetically acquired 2D/3D dixon techniques. It is also extended to work on imaging with non-connected body parts.

Keywords

according achieved addition address adjusted afterwards always angular annotation applied archiving artifacts assuming assumption automatic available background becomes body breast calves causing china classification commercially composing connected connective considered consistent contrasts contribute corner correct corrected correction counts created decomposition denoted described design distinctive divided done echoes engineering enough erode every examined examining exams except existence expanded extended failure feet fixed frequency gaps growing hand happens heart highest histogram identification identify identity implemented include intensity intermingle intermingled intermingling intrinsic known label labeled labeling largest least lowest made major mask minor mixture negative neurology noise object open operations opposed optimal orthopedics partially parts pattern patterns peak peaks people pixel position positioned positive possess problem product property proposed published pure recognition reliability remove respective robust robustness school separation series sets several sign slice soft spectral spectrum steps strengths subsequently subtracted subtracting swapped symmetrically tissue tissues true types unique unstable vectors volume water widely zero