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

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

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

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

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