Semi-blind ultrasound image deconvolution from compressed measurements

The recently proposed framework of ultrasound compressive deconvolution offers the possibility of decreasing the acquired data while improving the image spatial resolution. By combining compressive sampling and image deconvolution, the direct model of compressive deconvolution combines random projections and 2D convolution with a spatially invariant point spread function. Considering the point spread function known, existing algorithms have shown the ability of this framework to reconstruct enhanced ultrasound images from compressed measurements by inverting the forward linear model. In this paper, we propose an extension of the previous approach for compressive blind deconvolution, whose aim is to jointly estimate the ultrasound image and the system point spread function. The performance of the method is evaluated on both simulated and in vivo ultrasound data

Joint compressive sampling and deconvolution in ultrasound medical imaging

International audienceThe interest of compressive sampling and image deconvolution has been extensively explored in the ultrasound imaging literature. The first seeks to reduce the volume of acquired data and/or to accelerate the frame rate. The second aims at improving the ultrasound image quality in terms of spatial resolution, contrast and signal to noise ratio. In this paper, we propose a novel approach combining these two frameworks, resulting into a compressive deconvolution technique aiming at obtaining high quality reconstructions from a reduced number of measurements. The resulting inverse problem is solved by minimizing an objective function taking into account the data attachment term and two appropriate prior information terms adapted to ultrasound imaging

A simulation study on the choice of regularization parameter in l2-norm ultrasound image restoration

International audienceUltrasound image deconvolution has been widely investigated in the literature. Among the existing approaches, the most common are based on ℓ2-norm regularization (or Tikhonov optimization) or the well-known Wiener filtering. However, the success of the Wiener filter in practical situations largely depends on the choice of the regularization hyperparameter. An appropriate choice is necessary to guarantee the balance between data fidelity and smoothness of the deconvolution result. In this paper, we revisit different approaches for automatically choosing this regularization parameter and compare them in the context of ultrasound image deconvolution via Wiener filtering. Two synthetic ultrasound images are used in order to compare the performances of the addressed methods

Compressive deconvolution in medical ultrasound imaging

The interest of compressive sampling in ultrasound imaging has been recently extensively evaluated by several research teams. Following the different application setups, it has been shown that the RF data may be reconstructed from a small number of measurements and/or using a reduced number of ultrasound pulse emissions. Nevertheless, RF image spatial resolution, contrast and signal to noise ratio are affected by the limited bandwidth of the imaging transducer and the physical phenomenon related to US wave propagation. To overcome these limitations, several deconvolution-based image processing techniques have been proposed to enhance the ultrasound images. In this paper, we propose a novel framework, named compressive deconvolution, that reconstructs enhanced RF images from compressed measurements. Exploiting an unified formulation of the direct acquisition model, combining random projections and 2D convolution with a spatially invariant point spread function, the benefit of our approach is the joint data volume reduction and image quality improvement. The proposed optimization method, based on the Alternating Direction Method of Multipliers, is evaluated on both simulated and in vivo data

Formation de voies avec renforcement des échos forts en imagerie ultrasonore

En imagerie ultrasonore, la formation de voies influence considérablement la qualité des images. Classiquement, l’approche par retard et somme est utilisée essentiellement pour sa rapidité. Cependant, la qualité des images peut être largement améliorée avec des méthodes adaptatives comme celles inspirées du filtre de Capon. Nous proposons dans cet article d’améliorer davantage le contraste des images en nous basant sur un modèle parcimonieux des échos forts. Les résultats de simulations réalistes et expérimentaux présentés dans cet article montrent le potentiel de notre approche

Enhanced ultrasound image reconstruction using a compressive blind deconvolution approach

Compressive deconvolution, combining compressive sampling and image deconvolution, represents an interesting possibility to reconstruct enhanced ultrasound images from compressed measurements. The model of compressive deconvolution includes, in addition to the measurement matrix, a 2D convolution operator carrying the information on the system point spread function which is usually unkown in practice. In this paper, we propose a novel alternating minimization-based optimization scheme to invert the resulting linear model, to jointly reconstruct enhanced ultrasound images and estimate the point spread function. The performance of the method is evaluated on both Shepp-Logan phantom and simulated ultrasound data

Analytical formulation of the fractal dimension of filtered stochastic signals

International audienceThe aim of this study was to investigate the effects of a linear filter on the regularity of a given stochastic process in terms of the fractal dimension. This general approach, described in a continuous time domain, is new and is characterized by its simplicity. The framework of this problem is general since it emerges when a fractal process undertakes a transformation, as is the case in denoising or measurement processes

Medical ultrasound image reconstruction using distributed compressive sampling

International audienceThis paper investigates ultrasound (US) radiofrequency (RF) signal recovery using the distributed compressed sampling framework. The “correlation” between the RF signals forming a RF image is exploited by assuming that they have the same sparse support in the 1D Fourier transform, with different coefficient values. The method is evaluated using an experimental US image. The results obtained are shown to improve a previously proposed recovery method, where the correlation between RF signals was taken into account by assuming the 2D Fourier transform of the RF image sparse

Amélioration de la résolution des images ultrasonores en mode B par déconvolution semi-aveugle

National audienceEn imagerie médicale, et plus précisément dans le domaine de l'imagerie ultrasonore, les problématiques liées à l'amélioration de la résolution font aujourd'hui l'objet de très nombreux travaux. Alors que beaucoup d'approches se consacrent à l'amélioration du dispositif d'acquisition des images échographiques (pré-traitement) pour pallier leur faible résolution, très peu de travaux se sont attachés à des techniques de post-traitement. Nous proposons ici une nouvelle approche pour la restauration d'image basée sur une formulation de type déconvolution semi-aveugle, résolue dans le cadre algorithmique de la méthode des directions alternées. Les performances de notre algorithme sont évaluées à l'aide de données synthétiques (fantôme de Shepp-Logan) et d'une image ultrasonore in vivo en mode B, sur la base de plusieurs critères quantitatifs. Dans le cas où la réponse impulsionnelle spatiale du système est mal connue, les résultats démontrent une robustesse accrue par rapport à une méthode de déconvolution classique (non aveugle)

Semi-Blind Deconvolution for Resolution Enhancement in Ultrasound Imaging

International audienceIn the field of ultrasound imaging, resolution enhancement is an up-to-date challenging task. Many device-based approaches have been proposed to overcome the low resolution nature of ultrasound images but very few works deal with post-processing methods. This paper investigates a novel approach based on semi-blind deconvolution formulation and alternating direction method framework in order to perform the ultrasound image restoration task. The algorithm performance is addressed using optical images and synthetic ultrasound data for a various range of criteria. The results demonstrate that our technique is more robust to uncertainties in the a priori ultrasonic pulse than classical non-blind deconvolution methods