title: An inter-landmark approach to 4-D shape extraction and interpretation: application to myocardial motion assessment in MRI.
abstract: This paper presents a novel approach to shape extraction and interpretation in 4-D cardiac magnetic resonance imaging data. Statistical modeling of spatiotemporal interlandmark relationships is performed to enable the decomposition of global shape constraints and subsequently of the image analysis tasks. The introduced descriptors furthermore provide invariance to similarity transformations and thus eliminate pose estimation errors in the presence of image artifacts or geometrical inconsistencies. A set of algorithms are derived to address key technical issues related to constrained boundary tracking, dynamic model relaxation, automatic initialization, and dysfunction localization. The proposed framework is validated with a relatively large dataset of 50 subjects and compared to existing statistical shape modeling methods. The results indicate increased adaptation to spatiotemporal variations and imaging conditions.
authors.affiliation: Institute of Biomedical Engineering, Imperial College London, SW7 2BZ, UK. lekadir@doc.ic.ac.uk
authors.forename: Karim
authors.initials: K
authors.lastname: Lekadir
authors.forename: Niall G
authors.initials: NG
authors.lastname: Keenan
authors.forename: Dudley J
authors.initials: DJ
authors.lastname: Pennell
authors.forename: Guang-Zhong
authors.initials: GZ
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D016477:Artifacts; D006321:Heart / Q000502:physiology; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008955:Models, Cardiovascular; D015233:Models, Statistical; D009038:Motion; D009200:Myocardial Contraction / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods