The imaging of moving organs such as the heart, in particular, is a real challenge because of its movement. This book presents current and emerging methods developed for the acquisition of images of moving organs in the five main medical imaging modalities: conventional X-rays, computed tomography (CT), magnetic resonance imaging (MRI), nuclear imaging and ultrasound. The availability of dynamic image sequences allows for the qualitative and quantitative assessment of an organ’s dynamics, which is often linked to pathologies.

Table of Contents:
PREFACE xiii ACKNOWLEDGMENTS xv INTRODUCTION xvii PART 1. METHODOLOGICAL BASES 1 CHAPTER 1. EXTRACTION AND SEGMENTATION OF STRUCTURES IN IMAGE SEQUENCES 3 Olivier BERNARD, Patrick CLARYSSE, Thomas DIETENBECK, Denis FRIBOULET, Stéphanie JEHAN-BESSON and Jérome POUSIN 1.1. Problematics 3 1.2. Overview of segmentation methods 3 1.3. Summary of the different classes of deformable models 6 1.3.1. Non-energy approaches 7 1.3.2. Energy-based approaches 8 1.4. Deformable templates 11 1.4.1. Elastic deformable template principle 12 1.4.2. Dynamic elastic deformable template 14 1.4.3. Elastic deformable template and modal analysis 15 1.4.4. The elastic deformable template in practice 15 1.5. Variational active contours 17 1.5.1. Active contour representations 17 1.5.2. Energy functional 21 1.5.3. Obtaining the evolution equation 26 1.5.4. Level set digital implementation 34 1.6. Integration of a priori constraints in the formalism of variational contours 35 1.6.1. Shape a priori 36 1.6.2. Motion a priori 38 1.7. Implementation examples in cardiac imaging 44 1.7.1. Echographic imaging: choice of the data fitting term 44 1.7.2. Example of 3D echocardiography image segmentation 46 1.7.3. Example of 2D echocardiography image segmentation 48 1.8. Conclusion 50 1.9. Bibliography 52 CHAPTER 2. MOTION ESTIMATION AND ANALYSIS 65 Patrick CLARYSSE and Jérome POUSIN 2.1. Problematics 65 2.2. Problem formulation 66 2.3. Transport methods 67 2.3.1. Optical flow 68 2.3.2. Motion estimation seen as an optimal transport problem 70 2.4. Probabilistic approaches 74 2.5. Image registration 76 2.5.1. Transformation 77 2.5.2. Similarity function 78 2.5.3. Optimization 78 2.5.4. Practical considerations 79 2.6. Local methods 79 2.6.1. Block or primitive-matching 79 2.6.2. Least-square estimation 81 2.7. Hybrid methods 81 2.7.1. Power spectrum-based methods 82 2.7.2. Spatiotemporal description 82 2.8. Phase-based methods 84 2.8.1. Fleet and Jepson’s method 85 2.8.2. Analytic and monogenic signal 86 2.8.3. Harmonic phase methods 88 2.9. Registration and motion estimation in a sequence of images 89 2.9.1. Lagrangian description 89 2.9.2. Eulerian description 91 2.9.3. Strategies for the estimation in sequence 91 2.10. Evaluation of motion estimation methods 92 2.11. Conclusion 95 2.12. Bibliography 95 CHAPTER 3. POST-PROCESSING AND ANALYSIS OF DYNAMIC MAGNETIC RESONANCE IMAGES FOR MYOCARDIAL PERFUSION QUANTIFICATION 103 Bruno NEYRAN and Magalie VIALLON 3.1. Introduction 103 3.2. Dynamic measurement of perfusion with contrast agents: reminder about the MRI sequences and the different contrast agents used 107 3.2.1. Brief reminder about cardiac perfusion MRI sequences 107 3.2.2. MRI signal conversion/tracer concentration 107 3.2.3. Different clinical-candidate contrast agents 108 3.3. Motion correction and contour segmentation of the myocardium: important preprocessing prior to quantitative analysis 109 3.3.1. Dynamic image registration 109 3.3.2. Automatic contour extraction 109 3.4. Semi-quantitative perfusion analysis: calculation of relative parameters depending on the injection of the contrast medium 110 3.4.1. Semi-quantitative perfusion parameters 110 3.4.2. Heuristic modeling using a varied gamma function 112 3.4.3. Heuristic modeling with a bi-exponential function 114 3.4.4. Heuristic modeling with the Moate model 115 3.5. Absolute parameters independent of the contrast agent injection (taking account of the arterial input): pharmacokinetic modeling 117 3.5.1. General studies: tracer kinetics theory 118 3.5.2. Identification of the residual function 127 3.5.3. Identification of the discrete residual function 129 3.6. Conclusion 133 3.7. Bibliography 135 CHAPTER 4. TENSOR DECOMPOSITION OF A DYNAMIC SEQUENCE OF IMAGES INTO SIMPLE ELEMENTS  141 Frédérique FROUIN and Claire PELLOT-BARAKAT 4.1. Problematics 141 4.2. Panorama of methods for the quantitative analysis of dynamic image sequences 143 4.2.1. Regions of interest method 143 4.2.2. Parametric imaging methods 144 4.2.3. Movement analysis methods 145 4.2.4. Tensor decomposition of a sequence of images into simple elements 145 4.3. Tensor decomposition methods of an image sequence into simple elements 146 4.3.1. Notations and decomposition principle 146 4.3.2. Orthogonal decomposition of an image sequence 147 4.3.3. Decomposition into simple elements 148 4.4. Specifications for radiotracer or contrast medium monitoring 149 4.4.1. Proposed approach objectives and associated constraints definition 149 4.4.2. Components estimation principle 149 4.4.3. Example of tensor decomposition into simple elements in myocardial perfusion studies 152 4.4.4. Limitations of the proposed approach 153 4.4.5. Clinical applications of the tensor decomposition into simple elements for cardiac imaging 155 4.5. Specifications for the study of cardiac motion 156 4.5.1. Proposed approach objectives and associated constraint definition 156 4.5.2. Tensor decomposition method solution 157 4.5.3. Tensor decomposition model extensions 160 4.5.4. Clinical applications and perspectives 164 4.6. Conclusion 165 4.7. Bibliography 166 PART 2. APPLICATION EXAMPLES 169 CHAPTER 5. EVALUATION OF CARDIAC STRUCTURE SEGMENTATION IN CINE MAGNETIC RESONANCE IMAGING 171 Alain LALANDE, Mireille GARREAU and Frédérique FROUIN 5.1. Context: significance of the automatic segmentation of the cardiac structures 171 5.1.1. Cine MRI in short-axis orientation 171 5.1.2. Left ventricle and right ventricle 172 5.2. Evaluation necessity 175 5.2.1. The place of evaluation 175 5.2.2. Analytic and empirical methods 176 5.3. Empirical evaluation methods 177 5.4. Visual evaluation methods 179 5.5. Supervised methods 180 5.5.1. The definition of a reference 180 5.5.2. Creation of an expert database 183 5.5.3. Evaluation criterion: edge-based approaches 184 5.5.4. Evaluation criteria: region-based approaches 188 5.5.5. Supervised methods for the estimation of a clinical parameter 192 5.5.6. ROC curves 193 5.5.7. Comparison of the supervised methods 194 5.5.8. Limitations of the supervised methods 195 5.6. Non-supervised evaluation methods 198 5.6.1. Unsupervised methods relying on region- or edge-based descriptors 198 5.6.2. Methods using a clinical parameter 202 5.6.3. Estimation methods of a reference segmentation 204 5.6.4. Difficulties in unsupervised methods 205 5.7. Conclusion 205 5.8. IMPEIC and MEDIEVAL working groups 207 5.9. Bibliography 209 CHAPTER 6. PHASE-BASED HEART MOTION ESTIMATION IN MULTIMODALITY CARDIAC IMAGING 217 Martino ALESSANDRINI, Adrian BASARAB, Olivier BERNARD and Philippe DELACHARTRE 6.1. Phase images 218 6.1.1. Multidimensional analytic signals 218 6.1.2. Monogenic signal 219 6.2. Optical flow motion estimation on the phase of the two single-orthant analytic signals and using a deformable mesh: application to cardiac MRI sequences 221 6.2.1. Optical flow method applied to spatial phase images 223 6.2.2. Parametric modeling of local motion 226 6.2.3. Trajectory estimation 228 6.2.4. Results 230 6.2.5. Conclusion 235 6.3. Motion estimation by optical flow from the monogenic phase using a local affine model and multiscale analysis – application to ultrasonic cardiac sequences 236 6.3.1. Affine model 237 6.3.2. Multiscale choice of the window size 238 6.3.3. Iterative refinement of the displacement 238 6.4. Bibliography 244 CHAPTER 7. CARDIAC MOTION ANALYSIS IN TAGGED MRI 247 Patrick CLARYSSE and Pierre CROISILLE 7.1. Motion quantification by the SinMod method 248 7.2. Processing pipeline and features of the software inTag 250 7.2.1. Data and input parameters 251 7.2.2. Motion field estimation 251 7.2.3. LV contour extraction 252 7.2.4. LV motion and deformation analysis 252 7.3. Perspectives 254 7.4. Bibliography 254 CHAPTER 8. LEFT VENTRICLE MOTION ESTIMATION IN COMPUTED TOMOGRAPHY IMAGING 257 Antoine SIMON, Mireille GARREAU, Régis DELAUNAY, Dominique BOULMIER, Erwan DONAL and Christophe LECLERCQ 8.1. Introduction 257 8.1.1. Clinical problem and objectives 257 8.1.2. Technological choice: cardiac CT imaging 258 8.1.3. State of the art and method positioning 259 8.2. Surface matching method 262 8.2.1. Surface segmentation and reconstruction stage 262 8.2.2. Surface–surface matching 263 8.3. Surface–surface approach evaluation 267 8.3.1. Simulated data 267 8.3.2. Real data 270 8.4. Surface–surface approach conclusion 278 8.5. Surface and volume matching method: surface–volume approach 278 8.6. Surface–volume approach evaluation 280 8.6.1. Simulated data 280 8.6.2. Real data 283 8.7. Conclusion 285 8.8. Acknowledgments 287 8.9. Bibliography 287 PART 3 . TOWARD PATIENT-SPECIFIC CARDIOLOGY 293 CHAPTER 9. PERSONALIZATION OF ELECTROMECHANICAL MODELS OF THE CARDIAC VENTRICULAR FUNCTION BY HETEROGENEOUS CLINICAL DATA ASSIMILATION 295 Stephanie MARCHESSEAU, Maxime SERMESANT, Florence BILLET, Hervé DELINGETTE and Nicholas AYACHE 9.1. Introduction 295 9.2. Anatomy and electrophysiology personalization from clinical data 298 9.2.1. Personalization of the heart and the tissue structure anatomy 298 9.2.2. Cardiac electrophysiology personalization 300 9.3. Heart mechanics modeling 302 9.3.1. Modeling of the Bestel–Clément–Sorine electromechanical coupling 302 9.3.2. Blood flow modeling 304 9.3.3. Other boundary conditions 305 9.3.4. Discussion about this model 306 9.4. Image data processing: cardiac kinematics personalization 306 9.4.1. Metrics for the comparison between observed and simulated motion 307 9.4.2. Data time interpolation 307 9.4.3. Deformable models approach 308 9.4.4. Data displacement case 310 9.4.5. Velocity data case 311 9.4.6. Results with cine-MRI data 311 9.4.7. Results from dynamic CT data 312 9.5. Calibration of the mechanical parameters from global data 313 9.5.1. Available data description 314 9.5.2. Unscented transform calibration 315 9.5.3. Calibration results with healthy volunteers 317 9.5.4. Calibration results with pathological cases 317 9.6. Mechanical personalization by variational data assimilation 318 9.6.1. Variational approach on a simplified model 320 9.6.2. Application to synthetic cases 321 9.6.3. Application to clinical cases 322 9.6.4. Sequential approach on full model 322 9.7. Conclusion 323 9.8. Bibliography 324 CONCLUSION 331 APPENDIX 1 335 APPENDIX 2 339 LIST OF AUTHORS 343 INDEX 347