Frequency-Space Prediction Filtering for Phase Aberration Correction in Plane-Wave Ultrasound

TL;DR

This paper introduces an adaptive-order autoregressive model for frequency-space prediction filtering to improve phase aberration correction in plane-wave ultrasound imaging, addressing depth-dependent signal relevance issues.

Contribution

It proposes an adaptive AR model for FXPF in plane-wave ultrasound, enhancing phase aberration correction at various depths compared to fixed-order models.

Findings

Improved contrast and noise metrics demonstrate effectiveness.

Adaptive AR model outperforms fixed-order models.

Enhanced image quality at different depths.

Abstract

Ultrasound imaging often suffers from image degradation stemming from phase aberration, which represents a significant contributing factor to the overall image degradation in ultrasound imaging. Frequency-space prediction filtering or FXPF is a technique that has been applied within focused ultrasound imaging to alleviate the phase aberration effect. It presupposes the existence of an autoregressive (AR) model across the signals received at the transducer elements and removes any components that do not conform to the established model. In this study, we illustrate the challenge of applying this technique to plane-wave imaging, where, at shallower depths, signals from more distant elements lose relevance, and a fewer number of elements contribute to image reconstruction. While the number of contributing signals varies, adopting a fixed-order AR model across all depths, results in suboptimal performance. To address this challenge, we propose an AR model with an adaptive order and quantify its effectiveness using contrast and generalized contrast-to-noise ratio metrics.