Compressive Deconvolution in Medical Ultrasound Imaging

Abstract

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 Sparse Reconstruction Framework for Fourier-Based Plane-Wave Imaging

“…a model in which the US images are compressible meaning that their representation in this model contains many zeroes. In the literature, various models have already been proposed mainly relying on wavelet-based models [32], [34], [36], [43]. However, the choice of the best model is a hard task since it is highly dependent on the content of the image, unknown a priori.…”

“…In the context of ultrasonic imaging, several studies have already exploited the sparsity of backscattered echo signals in the wave atom frame [30], as well as of radio frequency images in specific frames such as 2D Fourier basis [31], wavelet basis [32], or even learned dictionaries [33]. Schiffner et al introduced CS-based plane wave beamforming in the frequency domain assuming sparsity in an orthonormal wavelet basis [34] while Chernyakova et al used a Xampling scheme and a finite rate of innovation model to achieve CS-based Fourier beamforming [35].…”

Line detection in speckle images using Radon transform and ℓ<inf>1</inf> regularization

“…Subsequently, random multiplicative noise was added and the convolution with the simulated US PSF was applied, as used in [11]. The B-mode simulated image is shown in Fig.…”

Morphological component analysis for sparse regularization in plane wave imaging

“…Various sparsifying models have already been proposed that rely mainly on wavelet-based models [1], [4], [14]. In our previous works [5], [6], [9], [15], we suggested to use a concatenation of wavelet bases since it exhibits better reconstruction results than traditional wavelet-based models.…”