Elevational motion estimation for 3D ultrasound with machine learning and a speckle generating gel pad

Ching-Yen Lee; U-Wai Lok; Pai-Chi Li

doi:10.1117/12.2590727

20 April 2021 Elevational motion estimation for 3D ultrasound with machine learning and a speckle generating gel pad

Ching-Yen Lee, U-Wai Lok, Pai-Chi Li

Proceedings Volume 11792, International Forum on Medical Imaging in Asia 2021; 117920U (2021) https://doi.org/10.1117/12.2590727
Event: International Forum on Medical Imaging in Asia 2021, 2021, Taipei, Taiwan

Abstract

Freehand 3D ultrasound imaging using a 1D transducer array has been widely investigated. Speckle decorrelation-based elevational displacement estimation is often applied. Generally, the correlation coefficient (C.C.) of two regions of interest is mapped to the beam pattern which can be utilized to estimate the elevational displacement. However, performance has been limited due to several factors, including the inherent variance of pure speckle patterns. In this study, we propose a more robust and accurate approach that utilizes a speckle generating ultrasound gel pad, singular value decomposition (SVD), and machine learning for improving estimating performance. First, a 0.5-cm-thick speckle generating gel pad was used to produce homogeneous patterns with statistically fully developed scatterers. Second, calculations of the decorrelation curves were improved with the introduction of SVD method. Third, the two-layer artificial neural networks were utilized for estimation. With training by totally 4600 motion data with frame space of 0.01 mm and 0.1° respectively, our estimator achieves 0.906 precision while estimating the motion type, as well as the average error of displacement / rotation movement is 0.0002 mm and 0.004° respectively.

Citation Download Citation

Ching-Yen Lee, U-Wai Lok, and Pai-Chi Li "Elevational motion estimation for 3D ultrasound with machine learning and a speckle generating gel pad", Proc. SPIE 11792, International Forum on Medical Imaging in Asia 2021, 117920U (20 April 2021); https://doi.org/10.1117/12.2590727

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