First‐arrival traveltime sound speed inversion with a priori information - Hooi, Fong Ming; Carson, Paul L. (Medical physics (Lancaster), 2014)
Purpose: A first‐arrival travel‐time sound speed algorithm presented by Tarantola [Inverse Problem Theory and Methods for Model Parameter Estimation (SIAM, Philadelphia, PA, 2005)] is adapted to the medical ultrasonics setting. Through specification of a covariance matrix for the object model, the algorithm allows for natural inclusion of physical a priori information of the object. The algorithm's ability to accurately and robustly reconstruct a complex sound speed distribution is demonstrated on simulation and experimental data using a limited aperture. Methods: The algorithm is first demonstrated generally in simulation with a numerical breast phantom imaged in different geometries. As this work is motivated by the authors' limited aperture dual sided ultrasound breast imaging system, experimental data are acquired with a Verasonics system with dual, 128 element, linear L7‐4 arrays. The transducers are automatically calibrated for usage in the eikonal forward model.A priori information such as knowledge of correlated regions within the object is obtained via segmentation of B‐mode images generated from synthetic aperture imaging. Results: As one illustration of the algorithm's facility for inclusion ofa priori information, physically grounded regularization is demonstrated in simulation. The algorithm's practicality is then demonstrated through experimental realization in limited aperture cases. Reconstructions of sound speed distributions of various complexity are improved through inclusion of a priori information. The sound speed maps are generally reconstructed with accuracy within a few m/s. Conclusions: This paper demonstrates the ability to form sound speed images using two opposed commercial linear arrays to mimic ultrasound image acquisition in the compressed mammographic geometry. The ability to create reasonably good speed of sound images in the compressed mammographic geometry allows images to be readily coregistered to tomosynthesis image volumes for breast cancer detection and characterization studies.