With the increasing versatility of CMR, further understanding of intrinsic contractility of the myocardium can be achieved by performing subject-specific modeling by integrating structural and functional information available. The recent introduction of the virtual tagging framework allows for visualization of the localized deformation of the myocardium based on phase contrast myocardial velocity mapping. The purpose of this study is to examine the use of a non-linear, Kernel-Partial Least Squares Regression (K-PLSR) predictive motion modeling scheme for the virtual tagging framework. The method allows for the derivation of a compact non-linear deformation model such that the entire deformation field can be predicted by a limited number of control points. When applied to virtual tagging, the technique can be used to predictively guide the mesh refinement based on the motion of the coarse grid, thus greatly reducing the search space and increasing the convergence speed of the algorithm. The effectiveness and numerical accuracy of the proposed technique are assessed with both numerically simulated data sets and in vivo phase contrast CMR velocity mapping from a group of 7 subjects. The technique presented has a distinct advantage over the conventional mesh refinement scheme and brings CMR myocardial contractility analysis closer to routine clinical practice.

The increase in the use of robotic assisted surgery has provided benefits to both the patient and the surgeon. There are possibilities for training and surgical planning and guidance within this framework but work is needed for this to reach a patient-specific level. Medical images obtained prior to surgery are used clinically to plan operations and there has been much research in their use to build models, giving further knowledge of tissue and organ structures. The incorporation of this data into the surgical environment is a recent development, assisting the surgeon with orientation information, but motion due to respiration or changes to morphology due to intervention begets a need for the incorporation of real-time imaging into the modelling. The current work on pre-operative and intra-operative imaging and modelling methods for robotic assisted surgery has to be reviewed and then the Image Constrained Biomechanical Modelling has to be introduced, whereby patient-specific biomechanical modelling is based on the underlying medical images, reducing the computational complexity involved with traditional biomechani-cal modelling. This article concludes with a discussion of the possible and promising future directions of this work.

Abstract. Diagnosis and treatment of coronary artery disease requires a full understanding of the intrinsic contractile mechanics of the heart. MR myocardial velocity imaging is a promising technique for revealing intramural cardiac motion but its ability to depict 3D strain tensor distribution is constrained by anisotropic voxel coverage of velocity imaging due to limited imaging slices and the achievable SNR in patient studies. This paper introduces a novel Kriging estimator for simultaneously improving the tracking and dense inter-slice estimation of the myocardial velocity data. A harmonic embedding technique is employed to determine point correspondence between left ventricle models between subjects, allowing for a statistical shape model to be reconstructed. The use of different semivariograms is investigated for optimal deformation reconstruction. Results from in vivo data demonstrate a marked improvement in tracking myocardial deformation, thus enhancing the potential clinical value of MR myocardial velocity imaging.