Image registration techniques using free-form deformation models have shown promising results for 3D myocardial strain estimation from ultrasound. However, the use of this technique has mostly been limited to research institutes due to the high computational demand, which is primarily due to the computational load of the regularization term ensuring spatially smooth cardiac strain estimates. Indeed, this term typically requires evaluating derivatives of the transformation field numerically in each voxel of the image during every iteration of the optimization process. In this paper, we replace this time-consuming step with a closed-form solution directly associated with the transformation field resulting in a speed up factor of ~10-60,000, for a typical 3D B-mode image of 2503 and 5003 voxels, depending upon the size and the parametrization of the transformation field. The performance of the numeric and the analytic solutions was contrasted by computing tracking and strain accuracy on two realistic synthetic 3D cardiac ultrasound sequences, mimicking two ischemic motion patterns. Mean and standard deviation of the displacement errors over the cardiac cycle for the numeric and analytic solutions were 0.68±0.40 mm and 0.75±0.43 mm respectively. Correlations for the radial, longitudinal and circumferential strain components at end-systole were 0.89, 0.83 and 0.95 versus 0.90, 0.88 and 0.92 for the numeric and analytic regularization respectively. The analytic solution matched the performance of the numeric solution as no statistically significant differences (p>0.05) were found when expressed in terms of bias or limits-of-agreement.
Ultrasonic tissue characterization has been gaining increasing attention. This procedure is generally based on
the analysis of the echo signal. As the ultrasound echo is degraded by the system Point Spread Function,
deconvolution could be employed to provide a tissue response estimate, exploitable for a better characterization.
In this context, we present a deconvolution framework expressively designed to improve tissue characterization.
Thanks to a new model for tissue reflectivity the proposed framework overcomes limitations associated with
standard ones. The performance was evaluated from several tissue-mimicking phantoms. Obtained results show
relevant improvements in classification accuracy. From a comparison with standard schemes the superiority of
the proposed algorithm was attested.
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