Blind Deconvolution of Medical Ultrasound Images: A Parametric Inverse Filtering Approach

Michailovich, Oleg V.; Tannenbaum, Allen

doi:10.1109/tip.2007.910179

Cited by 124 publications

(101 citation statements)

References 44 publications

(117 reference statements)

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“…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.…”

Section: Background and Motivationmentioning

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%

“…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.…”

Section: Background and Motivationmentioning

confidence: 99%

See 2 more Smart Citations

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

Roquette

Simeoni

Hurley

et al. 2017

2017 IEEE International Ultrasonics Symposium (IUS)

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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...

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Section: Background and Motivationmentioning

confidence: 99%

Section: Background and Motivationmentioning

confidence: 99%

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

Roquette

Simeoni

Hurley

et al. 2017

2017 IEEE International Ultrasonics Symposium (IUS)

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“…We have adapted the techniques of image restoration in signal processing [7][8][9] to the improvement of the resolution of data issued from the scanning of the magnetic near-field. In image processing, the different image restoration techniques are: the inverse filtering, the Wiener filtering and the iterative methods.…”

Section: Wiener Filteringmentioning

confidence: 99%

Wiener Filtering Applied to Magnetic Near Field Scanning

Labarre¹,

Costa²,

Gautier³

2009

PIER

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Abstract-Magnetic near-field scanning is a growing-up technique in power electronics. However, due to the large size of the devices, a trade-off is necessary to find between the spatial resolution of the field and the measurement time, this one is often unsatisfying. In this paper, we propose to improve drastically this trade-off by using a signal-processing technique (Wiener filtering) which allows a large scanning step (short scanning duration) while keeping an accurate spatial resolution of the magnetic field. This technique is adapted to the magnetic near-field probing, as described in the paper. Our experiments show that a factor 25 can be gained on the product "resolution-×-duration" of the measurements.

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“…The deconvolution algorithms for enhancing the spatial resolution and/or the signal-tonoise ratio (SNR) have been widely studied in various imaging modalities such as X-ray imaging [25], ultrasound imaging [26,27], and optical coherence tomography [28,29]. Deconvolution approaches have recently been investigated in PA imaging in ORPAM and PA computed tomography systems [18,[30][31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

In vivo deconvolution acoustic-resolution photoacoustic microscopy in three dimensions

Cai¹,

Li²,

Chen³

2016

Biomed. Opt. Express

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Acoustic-resolution photoacoustic microscopy (ARPAM) provides a spatial resolution on the order of tens of micrometers, and is becoming an essential tool for imaging fine structures, such as the subcutaneous microvasculature. High lateral resolution of ARPAM is achieved using high numerical aperture (NA) of acoustic transducer; however, the depth of focus and working distance will be deteriorated correspondingly, thus sacrificing the imaging range and accessible depth. The axial resolution of ARPAM is limited by the transducer's bandwidth. In this work, we develop deconvolution ARPAM (D-ARPAM) in three dimensions that can improve the lateral resolution by 1.8 and 3.7 times and the axial resolution by 1.7 and 2.7 times, depending on the adopted criteria, using a 20-MHz focused transducer without physically increasing its NA and bandwidth. The resolution enhancement in three dimensions by D-ARPAM is also demonstrated by in vivo imaging of the microvasculature of a chick embryo. The proposed D-ARPAM has potential for biomedical imaging that simultaneously requires high spatial resolution, extended imaging range, and long accessible depth.

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Blind Deconvolution of Medical Ultrasound Images: A Parametric Inverse Filtering Approach

Cited by 124 publications

References 44 publications

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

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

Wiener Filtering Applied to Magnetic Near Field Scanning

In vivo deconvolution acoustic-resolution photoacoustic microscopy in three dimensions

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