2017 IEEE International Ultrasonics Symposium (IUS) 2017
DOI: 10.1109/ultsym.2017.8092301
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On an analytical, spatially-varying, point-spread-function

Abstract: Abstract-The point spread function (PSF), namely the response of an ultrasound system to a point source, is a powerful measure of the quality of an imaging system. The lack of an analytical formulation inhibits many applications ranging from apodization optimization, array-design, and deconvolution algorithms. We propose to fill this gap through a general PSF derivation that is flexible with respect to the type of transmission (synthetic aperture, plane-wave, diverging-wave etc.), while faithfully capturing th… Show more

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Cited by 12 publications
(15 citation statements)
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“…In this work, we address the problem of non-stationary deconvolution in US imaging. More precisely, we propose a continuous spatially-varying PSF operator which accounts for diffraction effects related to US propagation and extends the one presented in our previous work [16]. The proposed model is based on a physical modelling of both the US propagation and the DAS, recently discussed in several studies [17], [18].…”
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confidence: 88%
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“…In this work, we address the problem of non-stationary deconvolution in US imaging. More precisely, we propose a continuous spatially-varying PSF operator which accounts for diffraction effects related to US propagation and extends the one presented in our previous work [16]. The proposed model is based on a physical modelling of both the US propagation and the DAS, recently discussed in several studies [17], [18].…”
mentioning
confidence: 88%
“…We are now equipped with the two operators D and H that can be injected in (2) to compute the PSF operator. By following similar arguments to the ones developed in [16], K can be decomposed as follows,…”
Section: Delay-and-sum Operatormentioning
confidence: 99%
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“…To overcome this issue, ultrasound images are generally divided in local regions prior to deconvolution, assuming a block-wise spatiallyinvariant PSF (see, e.g., [11]). To avoid issues related to stitching together the results of block-wise techniques, a few attempts have been very recently made in [12] and [13] to account for non-stationary convolution models in ultrasound imaging. The former relies on a very restrictive model with few degrees of freedom whereas the deconvolution method in the latter is computationally intractable for large images.…”
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confidence: 99%