  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.
