3D ultrasound tracking of the left ventricle using one-step forward prediction and data fusion of collaborative trackers

Tracking the left ventricle (LV) in 3D ultrasound data is a challenging task because of the poor image quality and speed requirements. Many previous algorithms applied standard 2D tracking methods to tackle the 3D problem. However, the performance is limited due to increased data size, landmarks ambiguity, signal drop-out or non-rigid deformation. In this paper we present a robust, fast and accurate 3D LV tracking algorithm. We propose a novel one-step forward prediction to generate the motion prior using motion manifold learning, and introduce two collaborative trackers to achieve both temporal consistency and failure recovery. Compared with tracking by detection and 3D optical flow, our algorithm provides the best results and subvoxel accuracy. The new tracking algorithm is completely automatic and computationally efficient. It requires less than 1.5 seconds to process a 3D volume which contains 4,925,440 voxels.