Reconstruction of compressively sampled ultrasound images using dual prior information

Abstract

This paper introduces a new technique for compressive sampling reconstruction of biomedical ultrasound images that exploits two types of prior information. On the one hand, our proposed approach is based on the observation that ultrasound RF echoes are best characterised statistically using alpha-stable distributions. On the other hand, through knowledge of the acquisition process, the support of the RF echoes in the Fourier domain can be easily inferred. Together, these two facts inform an iteratively reweighted least squares (IRLS) algorithm, which is shown to outperform previously proposed reconstruction techniques, both visually and in terms of two objective evaluation measures.

Application of Compressive Sensing to Ultrasound Images: A Review

“…Achim [34] extended their previous proposed approach for CS reconstruction of RF US images. This approach of RF signal reconstruction has used symmetric alpha-stable-IRLS (S α S-IRLS) algorithm in the Fourier domain with prior information about US RF signals.…”

“…Using beam-forming matrices, a direct US image reconstruction based on CS is presented. The authors in [34] developed an ultrasound time domain model for beam forming along with a frequency domain equivalent. In CS sparse recovery, they used this model based on matrices of time and frequency domain to recover images from undersampled ultrasound waves.…”

“…The most commonly used US scanner for acquiring images was clinical scanner Sonoline Elegra, which was modified for research with linear probe (Siemens Medical Systems, Issaquah, WA, USA) [32, 34, 36]. The sampling frequency and central frequency are also changed in some research studies, in which RF US tissue images are commonly obtained with central frequency of 7.5 MHz and sampling frequency of 50 MHz and a real human right lobe thyroid gland image with sampling frequency adjusted to 50 MHz and 40 MHz [32, 34, 36]. In [34], an image of real human liver is acquired for the CS reconstruction using the same settings.…”

Reconstruction of Ultrasound RF Echoes Modeled as Stable Random Variables

“…Our new approach to RF signal reconstruction still relies on SαS-IRLS [7] but is implemented in the frequency domain as in [9] and modified (following [10], [18]) to incorporate information on the support of RF signals. Implementing the SαS-IRLS algorithm in the Fourier domain is motivated by the higher degree of compressibility exhibited by ultrasound echoes in the frequency domain.…”

“…In this paper, we further extend our techniques described in [7], [9], [10] by supplementing the prior information available to an p minimization algorithm with the support of the RF echoes in the frequency domain and showing via Monte Carlo simulations how to optimally choose the parameter p. In ultrasound applications the support can be easily inferred through knowledge of the ultrasound scanner specifications and transducer bandwidth. Hence, we describe this new approach as exploiting dual prior information.…”