Reconstruction of Enhanced Ultrasound Images From Compressed Measurements Using Simultaneous Direction Method of Multipliers

TL;DR

This paper presents a novel optimization method based on the simultaneous direction method of multipliers for reconstructing high-resolution ultrasound images from compressed measurements, improving image quality and acquisition efficiency.

Contribution

The paper introduces a new optimization scheme using SDMM for compressive deconvolution in ultrasound imaging, incorporating sparsity and tissue reflectivity priors.

Findings

Effective reconstruction demonstrated on simulated data.

Successful in vivo image enhancement achieved.

Method outperforms existing approaches in quality and speed.

Abstract

High resolution ultrasound image reconstruction from a reduced number of measurements is of great interest in ultrasound imaging, since it could enhance both the frame rate and image resolution. Compressive deconvolution, combining compressed sensing and image deconvolution, represents an interesting possibility to consider this challenging task. The model of compressive deconvolution includes, in addition to the compressive sampling matrix, a 2D convolution operator carrying the information on the system point spread function. Through this model, 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. In this paper, we mainly focused on the last two aspects. We proposed a novel simultaneous direction method of multipliers-based optimization scheme 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. The performance of the method is evaluated on both simulated and in vivo data.