Randomized transmit and receive ultrasound tomography
Gregory T. Clement, Tomoo Kamakura

TL;DR
This paper introduces a randomized ultrasound tomography method that uses specific source and receiver signal randomization to produce well-conditioned matrices, enabling effective image reconstruction even with low SNR and without traditional approximations.
Contribution
The paper presents a novel randomization technique for ultrasound tomography that improves matrix conditioning and image quality in noisy environments, surpassing traditional methods.
Findings
Successful image reconstruction at SNR as low as 1
Randomization yields well-conditioned matrices for inversion
Superiority over synthetic aperture in low SNR conditions
Abstract
A tomographic method is considered that forms images from sets of spatially randomized source signals and receiver sensitivities. The method is designed to allow image reconstruction for an extended number of transmitters and receivers in the presence noise and without plane wave approximation or otherwise approximation on the size or regularity of source and receiver functions. An overdetermined set of functions are formed from the Hadamard product between a Gaussian function and a uniformly distributed random number set. It is shown that this particular type of randomization tends to produce well-conditioned matrices whose pseudoinverses may be determined without implementing relaxation methods. When the inverted sets are applied to simulated first-order scattering from a Shepp-Logan phantom, successful image reconstructions are achieved for signal-to-noise ratios (SNR) as low as 1.…
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Taxonomy
TopicsMicrowave Imaging and Scattering Analysis · Ultrasound Imaging and Elastography · Ultrasonics and Acoustic Wave Propagation
