Restoration of Ultrasound Images Using Spatially-Variant Kernel Deconvolution

Flarea, Mihai I.; Basarab, Adrian; Kouamé, Denis; Vorobyov, Sergiy A.

doi:10.1109/icassp.2018.8461339

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…This can be due to phase aberration or overlapping MBs in this specific areas that distorts the PSF shape. This observation shows the limits of the NCC detection method that is sensitive to PSF shape, while in the context of ULM, this shape is a function of time and space [52]. Meanwhile, the NP method is able to connect the vessels as it can be seen in Fig.…”

Section: Results From In Vivo Rat Brain Datamentioning

confidence: 91%

Microbubble Identification Based on Decision Theory for Ultrasound Localization Microscopy

Corazza

Muleki-Seya

Basarab

et al. 2023

IEEE Open J. Ultrason., Ferroelect., Freq. Contr.

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Ultrasound localization microscopy (ULM) enables the evaluation of the vascular microstructure by detecting, localizing, and tracking microbubbles (MBs) in the vascular network. ULM provides a vascular map of the network with improved spatial resolution but with an acquisition time of several minutes. Thus, it is of great importance to increase the number of MBs detected in order to limit the acquisition time. The standard MB detection method in ULM assumes that the contrast agents are the highest-intensity structures on the ultrasound images. However, in vivo data show that MB intensity may be lower than residual tissue or even noise. Thus, to facilitate the detection of these MBs, an MB detector based on decision theory is proposed in this paper. In this study, the proposed method based on the Neyman-Pearson criterion is compared with the standard intensity-based and the normalized cross-correlation detection methods on simulated and in vivo rat brain and kidney data. The new detection method makes it possible to control the false positive detection rate without degrading the MB detection rate on simulated data, to enhance the ULM vessel map resolution on in vivo brain data and to detect more vessels on in vivo kidney data.

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Section: Results From In Vivo Rat Brain Datamentioning

confidence: 91%

Microbubble Identification Based on Decision Theory for Ultrasound Localization Microscopy

Corazza

Muleki-Seya

Basarab

et al. 2023

IEEE Open J. Ultrason., Ferroelect., Freq. Contr.

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“…US image restoration or deconvolution aims at recovering an image of the tissues by mitigating the effect of the transducer spatial impulse response, also called point spread function (PSF). While few studies consider a spatially varying PSF [7][8][9], most of the existing approaches assume a stationary PSF to reduce the complexity of the restoration problem [10][11][12][13]. In this case, image restoration is applied to image segments where the hypothesis of non-stationary PSF may hold.…”

Section: Introductionmentioning

confidence: 99%

Block-Wise Ultrasound Image Deconvolution from Fundamental and Harmonic Images

Hourani¹,

Basarab²,

Varray³

et al. 2020

2020 IEEE International Ultrasonics Symposium (IUS)

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Ultrasound image deconvolution has been extensively explored in the literature as a powerful tool to enhance the spatial resolution and the contrast of ultrasound (US) images, or to estimate the tissue reflectivity function from the radiofrequency (RF) data. Its principle generally uses the first order Born approximation, which leads a linear model relating the RF images to the tissue reflectivity function and the point spread function (PSF). Non linear interactions between the US waves and the tissues are the origin of harmonics in the received signals allowing to extract the so-called harmonic image. This paper deals with the joint restoration of fundamental and harmonic images by taking into consideration the axial variation of the PSF within a block-wise restoration process. The interest of the proposed method is shown through phantom and in vivo results and compared to joint restoration methods considering a spatially invariant PSF.

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