Practical use of Block-Matching in 3D Speckle Tracking
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In this thesis, optimizations for speckle tracking are integrated into an existing framework for real-time tracking of deformable subdivision surfaces. This is employed in the segmentation of the the left ventricle (LV) in 3D echocardiography. The main purpose of the project was to optimize the efficiency of material point tracking, this leading to a more robust LV myocardial deformation field estimation. Block-matching is the most time consuming part of speckle tracking, and the corresponding algorithms used in this thesis are optimized based on a Single Instruction Multiple Data (SIMD) model, in order to achieve data level parallelism. The SIMD model is implemented by using Streaming SIMD Extensions (SSE) to improve the processing time for the computation of the sum of absolute differences, one possible metric for block matching purposes. Furthermore, a study is conducted to optimize parameters associated with speckle tracking in regards to both accuracy and computation time. This is tested by using simulated data sets of infarcted ventricles in 3D echocardiography. More specifically, the tests examine how the size of kernel blocks and search windows affect the accuracy and processing time of the tracking. It also compares the performance of kernel blocks specified in cartesian and beamspace coordinates. Finally, tracking-accuracy is compared and measured in different regions (apical, mid-level and basal segments) of the LV.