Compressive Deconvolution in Medical Ultrasound Imaging

TL;DR

This paper introduces a novel compressive deconvolution framework for ultrasound imaging that reconstructs high-quality RF images from compressed measurements, reducing data volume while enhancing image resolution and contrast.

Contribution

It presents a unified model combining compressed sensing and deconvolution, along with an optimization method based on ADMM, to improve ultrasound image quality from limited data.

Findings

Effective reconstruction of RF images from compressed data.

Improved spatial resolution and contrast in ultrasound images.

Validated on both simulated and real in vivo data.

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.