PROJECT TITLE :
A model and regularization scheme for ultrasonic beamforming clutter reduction
Acoustic muddle made by off-axis and multipath scattering is thought to cause image degradation, and in some cases these sources might be the prime determinants of in vivo image quality. We tend to have previously shown some success addressing these sources of image degradation by modeling the aperture domain signal from completely different sources of clutter, and then decomposing aperture domain information using the modeled sources. Our previous model had some shortcomings together with model mismatch and failure to recover B-Mode speckle statistics. These shortcomings are addressed here by developing a better model and by using a general regularization approach applicable for the model and information. We present results with L1 (lasso), L2 (ridge), and L1/L2 combined (elastic-internet) regularization methods. We tend to decision our new technique aperture domain model image reconstruction (ADMIRE). Our results demonstrate that ADMIRE with L1 regularization, or weighted toward L1 in the case of elastic-web regularization, have improved image quality. L1 by itself works well, but further enhancements are seen with elastic-internet regularization over the pure L1 constraint. On in vivo example cases, L1 regularization showed mean contrast enhancements of four.6 and six.eight dB on fundamental and harmonic pictures, respectively. Elastic internet regularization (α = zero.nine) showed mean contrast improvements of seventeen.eight dB on basic pictures and 11.8 dB on harmonic pictures. We tend to additionally demonstrate that in uncluttered Field II simulations the decluttering algorithm produces the same distinction, contrast-tonoise ratio, and speckle SNR as traditional B-mode imaging, demonstrating that ADMIRE preserves typical image options.
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