A Novel 4D PDE-Based Approach for Accurate Assessment of Myocardium Function Using Cine Cardiac Magnetic Resonance Images. Magnetic Resonance Imaging Evaluation of Left Ventricular Dimentions and Function of Pericardial and Myocardial Disease. Improving Full-Cardiach Cycle Strain Estimation from Tagged CMR by Accurate Modeling of 4D & #D image Appearance Characteriistics. New Automated Markov-Gibbs Random Field Based Framework for Myocardial Wall Viability Quantification on Agent Enhanced Cardiac Magentic Resonance Images. Accurate Automatic Analysis of Cardiac Cine Images. Segmentation of the Left Ventricle from Cardiac MR Images Based on Radial GVF Snake. 4D Deformable Models with Temporal Constraints: Application to 4D Cardiac Image Segmentation. Multistage Hybrid Active Apperance Model Matching: Segmentation of Left and Right Ventricles in Cardiac MR Images. Automatic Detection of Left Ventricle in 4D MR Images. Fully Automated Framework for the Analysis of Myocardial First-Pass Perfusion MR Images. First-Pass Myocardial Perfusion Cardiovascular Magnetic Resonance at 3 Tesla. Quantitative Analysis of First-Pass Contrast-Enhanced Myocardial Perfusion MRI. Unsurpassed Inline Analysis of Cardiac Perfusion MRI. Model-Based Registration for Dynamic Cardiac Perfusion MRI.Improved Semi-Automated Segmentation of Cardiac CT and MR Images.
This will be a comprehensive multi-contributed reference work that will detail the latest developments in spatial, temporal, and functional cardiac imaging. It will include several prominent imaging modalities such as MRI, CT, and PET technologies. There will be special emphasis placed on automated imaging analysis techniques, which are important to biomedical imaging analysis of the cardiovascular system. Novel 4D based approach will be a unique characteristic of this product.
- Inclusion of state-of-the art 4D cardiac image registration and image analysis.
- Many of the contributors will be world-class experts.
- Explores the aspect of automated segmentation of cardiac CT and MR images utilizing both 3D and 4D techniques.
- Provides a novel procedure for improving full-cardiac strain estimation in 3D image appearance characteristics.
- There will be extensive references at the end of each chapter to enhance further study.
Ayman El-Baz is a Professor in the Department of Biogineering at the University of Louisville. His interests include modeling biomedical imaging and developing efficient image analysis techniques for CAD systems. He has authored more than 300 publications, including eight edited books. He has also earned several awards for his work on lung cancer and autism diagnostics.
Jasjit S. Suri is an independent medical imaging entrepreneur who is a member and Fellow of AIMBE, IEEE, and former Chairman of IEEE Denver Section. He has won over 50 awards.