4D Blood Flow Reconstruction Over the Entire Ventricle From Wall Motion and Blood Velocity Derived From Ultrasound Data.

We demonstrate a new method to recover 4D blood flow over the entire ventricle from partial blood velocity measurements using multiple 3D+t colour Doppler images and ventricular wall motion estimated using 3D+t BMode images. We apply our approach to realistic simulated data to ascertain the ability of the method to deal with incomplete data, as typically happens in clinical practice. Experiments using synthetic data show that the use of wall motion improves velocity reconstruction, shows more accurate flow patterns and improves mean accuracy particularly when coverage of the ventricle is poor. The method was applied to patient data from 6 congenital cases, producing results consistent with the simulations. The use of wall motion produced more plausible flow patterns and reduced the reconstruction error in all patients.

Temporal sparse free-form deformations.

FFD represent a widely used model for the non-rigid registration of medical images. The balance between robustness to noise and accuracy in modelling localised motion is typically controlled by the control point grid spacing and the amount of regularisation. More recently, TFFD have been proposed which extend the FFD approach in order to recover smooth motion from temporal image sequences. In this paper, we revisit the classic FFD approach and propose a sparse representation using the principles of compressed sensing. The sparse representation can model both global and local motion accurately and robustly. We view the registration as a deformation reconstruction problem. The deformation is reconstructed from a pair of images (or image sequences) with a sparsity constraint applied to the parametric space. Specifically, we introduce sparsity into the deformation via L1 regularisation, and apply a bending energy regularisation between neighbouring control points within each level to encourage a grouped sparse solution. We further extend the sparsity constraint to the temporal domain and propose a TSFFD which can capture fine local details such as motion discontinuities in both space and time without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate deformations in dynamic 2D and 3D image sequences. Compared to the classic FFD and TFFD approach, a significant increase in registration accuracy can be observed in natural images as well as in cardiac images.