Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization
ACM Transactions on Mathematical Software (TOMS)
Computing Large Deformation Metric Mappings via Geodesic Flows of Diffeomorphisms
International Journal of Computer Vision
Non-parametric diffeomorphic image registration with the demons algorithm
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Diffeomorphic registration using b-splines
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Atlas construction and image analysis using statistical cardiac models
STACOM'10/CESC'10 Proceedings of the First international conference on Statistical atlases and computational models of the heart, and international conference on Cardiac electrophysiological simulation challenge
Volumetric myocardial mechanics from 3D+t ultrasound data with multi-model tracking
STACOM'10/CESC'10 Proceedings of the First international conference on Statistical atlases and computational models of the heart, and international conference on Cardiac electrophysiological simulation challenge
International Journal of Computer Vision
Estimation of smooth growth trajectories with controlled acceleration from time series shape data
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
STACOM'11 Proceedings of the Second international conference on Statistical Atlases and Computational Models of the Heart: imaging and modelling challenges
| Hi-index | 0.00 |
This paper proposes a new registration method for the in vivo quantification of cardiac deformation from a sequence of possibly noisy images. Our algorithm has been applied to 3D ultrasound (3D-US) images, which currently give a reasonable spatial and time resolution, but suffer from significant acquisition noise. Therefore, this modality requires the design of a robust strategy to quantify motion and deformation with the clinical aim of better quantifying cardiac function e.g. in heart failure. In the proposed method, referred to as Large Diffeomorphic Free Form Deformation (LDFFD), the displacement field at each time step is computed from a smooth non-stationary velocity field, thus imposing a coupling between the transformations at successive time steps. Our contribution is to extend this framework to the estimation of motion and deformation in an image sequence. Similarity is captured for the entire image sequence using an extension of the pairwise mutual information metric. The LDFFD algorithm is applied here to recover longitudinal strain curves from healthy and Left-Bundle Branch Block (LBBB) subjects. Strain curves for the healthy subjects are in accordance with the literature. For the LBBB patient, strain quantified before and after Cardiac Resynchronization Therapy show a clear improvement of cardiac function in this subject, in accordance with clinical observations.