4-D Cardiac MR Image Analysis: Left and Right Ventricular Morphology and Function

• Published in: IEEE transactions on medical imaging Vol. 29; no. 2; pp. 350 - 364
• Main Authors: Honghai Zhang, Wahle, A., Johnson, R.K., Scholz, T.D., Sonka, M.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Active appearance model; Active appearance model (AAM); Active shape model; active shape model (ASM); Adolescent; Adult; Algorithms; Cardiac disease; cardiac magnetic resonance imaging; Cardiovascular diseases; Correlation; disease characterization; Female; Heart; Heart - physiology; Humans; Image analysis; Image motion analysis; Image Processing, Computer-Assisted - methods; Image segmentation; Magnetic Resonance Imaging - methods; Male; Medical imaging; Middle Aged; Models, Cardiovascular; Morphology; Movement - physiology; Phases; Robustness; Segmentation; Sensitivity and Specificity; shape and motion analysis; Tetralogy of Fallot (TOF); Tetralogy of Fallot - physiopathology; Ventricular Function - physiology
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2009.2030799

In this study, a combination of active shape model (ASM) and active appearance model (AAM) was used to segment the left and right ventricles of normal and Tetralogy of Fallot (TOF) hearts on 4-D (3-D+time) MR images. For each ventricle, a 4-D model was first used to achieve robust preliminary segmentation on all cardiac phases simultaneously and a 3-D model was then applied to each phase to improve local accuracy while maintaining the overall robustness of the 4-D segmentation. On 25 normal and 25 TOF hearts, in comparison to the expert traced independent standard, our comprehensive performance assessment showed subvoxel segmentation accuracy, high overlap ratios, good ventricular volume correlations, and small percent volume differences. Following 4-D segmentation, novel quantitative shape and motion features were extracted using shape information, volume-time and dV/dt curves, analyzed and used for disease status classification. Automated discrimination between normal/TOF subjects achieved 90%-100% sensitivity and specificity. The features obtained from TOF hearts show higher variability compared to normal subjects, suggesting their potential use as disease progression indicators. The abnormal shape and motion variations of the TOF hearts were accurately captured by both the segmentation and feature characterization.