Multi-parameter acoustic imaging of uniform objects in inhomogeneous media
H. Emre Guven, Eric L. Miller, and Robin O. Cleveland

TL;DR
This paper develops a novel ultrasound imaging method that accounts for inhomogeneous media and background clutter, using statistical modeling and convex optimization to improve object classification accuracy.
Contribution
It introduces a multi-parameter acoustic imaging approach incorporating statistical interference models and total-variation minimization for better reconstruction in complex media.
Findings
Robust classification performance at low signal-to-clutter ratios
Effective reconstruction using convex optimization methods
Comparison shows improved results over minimum-l2-norm reconstruction
Abstract
The problem studied in this paper is ultrasound image reconstruction from frequency-domain measurements of the scattered field from an object with contrast in attenuation and sound speed. The case where the object has uniform but unknown contrast in these properties relative to the background is considered. Background clutter is taken into account in a physically realistic manner by considering an exact scattering model for randomly located small scatterers that vary in sound speed. The resulting statistical characteristics of the interference is incorporated into the imaging solution, which includes applying a total-variation minimization based approach where the relative effect of perturbation in sound speed to attenuation is included as a parameter. Convex optimization methods provide the basis for the reconstruction algorithm. Numerical data for inversion examples are generated by…
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Taxonomy
TopicsUltrasonics and Acoustic Wave Propagation · Microwave Imaging and Scattering Analysis · Photoacoustic and Ultrasonic Imaging
