Physically Inspired Constraint for Unsupervised Regularized Ultrasound Elastography

TL;DR

This paper introduces PICTURE, a physics-based constraint that leverages tissue compression laws to enhance lateral displacement estimation in ultrasound elastography, outperforming existing methods.

Contribution

The novel contribution is the integration of Poisson's ratio constraints into unsupervised elastography, capturing the correlation between axial and lateral displacements.

Findings

Significant improvement in lateral displacement accuracy.

Enhanced elastography image quality.

Validated on phantom and in vivo data.

Abstract

Displacement estimation is a critical step of virtually all Ultrasound Elastography (USE) techniques. Two main features make this task unique compared to the general optical flow problem: the high-frequency nature of ultrasound radio-frequency (RF) data and the governing laws of physics on the displacement field. Recently, the architecture of the optical flow networks has been modified to be able to use RF data. Also, semi-supervised and unsupervised techniques have been employed for USE by considering prior knowledge of displacement continuity in the form of the first- and second-derivative regularizers. Despite these attempts, no work has considered the tissue compression pattern, and displacements in axial and lateral directions have been assumed to be independent. However, tissue motion pattern is governed by laws of physics in USE, rendering the axial and the lateral displacements highly correlated. In this paper, we propose Physically Inspired ConsTraint for Unsupervised Regularized Elastography (PICTURE), where we impose constraints on the Poisson's ratio to improve lateral displacement estimates. Experiments on phantom and in vivo data show that PICTURE substantially improves the quality of the lateral displacement estimation.