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

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

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

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

Reconstruction of enhanced ultrasound images from compressed measurements

L'intérêt de l'échantillonnage compressé dans l'imagerie ultrasonore a été récemment évalué largement par plusieurs équipes de recherche. Suite aux différentes configurations d'application, il a été démontré que les données RF peuvent être reconstituées à partir d'un faible nombre de mesures et / ou en utilisant un nombre réduit d'émission d'impulsions ultrasonores. Selon le modèle de l'échantillonnage compressé, la résolution des images ultrasonores reconstruites à partir des mesures compressées dépend principalement de trois aspects: la configuration d'acquisition, c.à.d. l'incohérence de la matrice d'échantillonnage, la régularisation de l'image, c.à.d. l'a priori de parcimonie et la technique d'optimisation. Nous nous sommes concentrés principalement sur les deux derniers aspects dans cette thèse. Néanmoins, la résolution spatiale d'image RF, le contraste et le rapport signal sur bruit dépendent de la bande passante limitée du transducteur d'imagerie et du phénomène physique lié à la propagation des ondes ultrasonores. Pour surmonter ces limitations, plusieurs techniques de traitement d'image en fonction de déconvolution ont été proposées pour améliorer les images ultrasonores. Dans cette thèse, nous proposons d'abord un nouveau cadre de travail pour l'imagerie ultrasonore, nommé déconvolution compressée, pour combiner l'échantillonnage compressé et la déconvolution. Exploitant une formulation unifiée du modèle d'acquisition directe, combinant des projections aléatoires et une convolution 2D avec une réponse impulsionnelle spatialement invariante, l'avantage de ce cadre de travail est la réduction du volume de données et l'amélioration de la qualité de l'image. Une méthode d'optimisation basée sur l'algorithme des directions alternées est ensuite proposée pour inverser le modèle linéaire, en incluant deux termes de régularisation exprimant la parcimonie des images RF dans une base donnée et l'hypothèse statistique gaussienne généralisée sur les fonctions de réflectivité des tissus. Nous améliorons les résultats ensuite par la méthode basée sur l'algorithme des directions simultanées. Les deux algorithmes sont évalués sur des données simulées et des données in vivo. Avec les techniques de régularisation, une nouvelle approche basée sur la minimisation alternée est finalement développée pour estimer conjointement les fonctions de réflectivité des tissus et la réponse impulsionnelle. Une investigation préliminaire est effectuée sur des données simulées.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. According to the model of compressive sampling, the resolution of reconstructed ultrasound images from compressed measurements mainly depends on three aspects: the acquisition setup, i.e. the incoherence of the sampling matrix, the image regularization, i.e. the sparsity prior, and the optimization technique. We mainly focused on the last two aspects in this thesis. 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 Ultrasound wave propagation. To overcome these limitations, several deconvolution-based image processing techniques have been proposed to enhance the ultrasound images. In this thesis, we first propose a novel framework for Ultrasound imaging, named compressive deconvolution, to combine the compressive sampling and deconvolution. 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 this framework is the joint data volume reduction and image quality improvement. An optimization method based on the Alternating Direction Method of Multipliers is then proposed to invert the linear model, including two regularization terms expressing the sparsity of the RF images in a given basis and the generalized Gaussian statistical assumption on tissue reflectivity functions. It is improved afterwards by the method based on the Simultaneous Direction Method of Multipliers. Both algorithms are evaluated on simulated and in vivo data. With regularization techniques, a novel approach based on Alternating Minimization is finally developed to jointly estimate the tissue reflectivity function and the point spread function. A preliminary investigation is made on simulated data

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

Ultrasound compressive deconvolution with lp-norm prior

International audienceIt has been recently shown that compressive sampling is an interesting perspective for fast ultrasound imaging. This paper addresses the problem of compressive deconvolution for ultrasound imaging systems using an assumption of generalized Gaussian distributed tissue reflectivity function. The benefit of compressive deconvolution is the joint volume reduction of the acquired data and the image resolution improvement. The main contribution of this work is to apply the framework of compressive deconvolution on ultrasound imaging and to propose a novel ℓp-norm (1 ≤ p ≤ 2) algorithm based on Alternating Direction Method of Multipliers. The performance of the proposed algorithm is tested on simulated data and compared with those obtained by a more intuitive sequential compressive deconvolution method