Point spread functions and deconvolution of ultrasonic images

Dalitz, Christoph; Pohle-Fröhlich, Regina; Michalk, Thorsten

doi:10.1109/tuffc.2014.006717

Cited by 40 publications

(21 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We introduced a new formulation of the PSF as the kernel of the integral operator acting on the TRF in order to form the RF image (2). From this perspective, we derived an analytical formula for it that encompasses any emitting wave strategy, array geometry, transducer bandwidth, electrical excitation or apodization method.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

On an analytical, spatially-varying, point-spread-function

Roquette

Simeoni

Hurley

et al. 2017

2017 IEEE International Ultrasonics Symposium (IUS)

View full text Add to dashboard Cite

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 the spatially-variant blurring of the Tissue Reflectivity Function as caused by Delay-And-Sum reconstruction. We validate the derived PSF against simulation using Field II, and show that accounting for PSF spatial-variability in sparsebased deconvolution improves reconstruction.Index Terms-Point-Spread-Function, deconvolution, image enhancement, ultrasonic image simulation, apodization design. I. BACKGROUND AND MOTIVATIONIn ultrasound (US) imaging, the finite bandwidth and aperture of transducer elements limits image resolution. This limitation on the resolving capability of the tissue reflectivity function (TRF) can be modelled explicitly by re-casting the radio-frequency (RF) image as the results of an operator between the point-spread function (PSF) and the TRF. In this sense, the PSF, defined as the response of the imaging method in presence of a single scatterer, contains the blurring due to the instrument, and is a powerful tool in assessing the equipment performance in terms of imaging quality.Deconvolution methods use RF images to retrieve the TRF by accounting for the effect of PSF and prior knowledge of the image type. Many authors thus discuss deconvolution in conjunction with PSF assessment [1]- [3]. Two approaches to account for PSF blurring can be distinguished: deterministic Most deconvolution methods, blind or not, assume a spatially-invariant PSF model, primarily for computational purposes. In the blind-deconvolution context, [3], [5] argue that tissue-dependent attenuation and dispersive effects require the PSF to be estimated during the TRF computation. To avoid too complex an optimization, the PSF is usually assumed spatially-invariant across the imaging domain. In non-blind deconvolution [4], [6], a deterministic model of the PSF is obtained by means of simulation, such as Field II [7] or numerical approximation [1]. Again, the convenience of a spatiallyinvariant PSF avoids time-consuming repeated simulation.In both cases, a spatial invariance assumption on the PSF hence leads to important computational gain, since the operator linking the TRF and RF image becomes a convolution, which can efficiently be implemented in the Fourier domain. This assumption can however have profound consequences on the quality of the recovered TRF images: in practice, the PSF can indeed vary quite dramatically across the imaging domain, leading to non-uniform recovery performances.A few attempts have been made to account for spatiallyvarying PSFs [3]. However, they usually come at the cost of some simplifying...

show abstract

Section: Discussionmentioning

confidence: 99%

“…Many authors thus discuss deconvolution in conjunction with PSF assessment [1]- [3]. Two approaches to account for PSF blurring can be distinguished: deterministic [1], [2], [4] and blind deconvolution [3], [5].…”

Section: Background and Motivationmentioning

confidence: 99%

On an analytical, spatially-varying, point-spread-function

Roquette

Simeoni

Hurley

et al. 2017

2017 IEEE International Ultrasonics Symposium (IUS)

View full text Add to dashboard Cite

show abstract

“…In particular, the intrinsic bandlimitedness of ultrasound scanners along with the coherent nature of ultrasound image formation remain responsible for the familiar limitations of this imaging modality in terms of its effective spatial resolution and contrast. These limitations, on the other hand, can be alleviated by post-processing means, among which image deconvolution is arguably the most traditional method of choice [1].…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, more often than not, the effect of dispersive attenuation along with the non-uniformity of acoustic focusing make the PSF vary across the image domain [3]. This being said, however, the above variability has been observed to be relatively slow in most practical scenarios, which makes it possible to apply the convolution model to localized segments of an ultrasound scan 1 . For this reason, the discussion below will be restricted to one of such segments, associated with a spatially invariable, yet generally unknown PSF.…”

Section: Introductionmentioning

confidence: 99%

Iterative Reconstruction of Medical Ultrasound Images Using Spectrally Constrained Phase Updates

Michailovich

Basarab

Kouamé

2019

2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

View full text Add to dashboard Cite

Image deconvolution is a standard numerical procedure used in medical ultrasound imaging for improving the resolution and contrast of diagnostic sonograms. However, due to the intrinsic bandlimitedness of ultrasound scanners and the adverse effect of measurement noises, image deconvolution is known to be exceedingly sensitive to the errors incurred during inference of the point spread function (PSF) that characterizes the imaging system in use. In this case, even the slightest errors in specification of the PSF are likely to result in significant artifacts, rendering the reconstructed images worthless. To address the aforementioned problem, this paper describes a new method for blind deconvolution of ultrasound images, in which the errors due to inaccuracies in specification of the PSF are eliminated concurrently with estimation of tissue reflectivity directly from its associated radio-frequency data. A principal derivation and justification of the proposed method are supported by experimental results which demonstrate the effectiveness and viability of the new technique.

show abstract

“…Generally, existing approaches turn the TRF estimation into a deconvolution problem, by considering, under the first order Born approximation, that the formation of ultrasound images follows a 2D convolution model between the TRF and the system point-spread function (PSF). The PSF can be either estimated in a pre-processing step (see, e.g., [1][2][3][4][5][6]) or jointly estimated with the TRF, i.e., blind deconvolution (see, e.g., [7][8][9][10]). Mainly for computational reasons, most of the existing ultrasound image restoration methods consider a spatially-invariant PSF model and circulant boundary conditions.…”

Section: Introductionmentioning

confidence: 99%