Physically Inspired Constraint for Unsupervised Regularized Ultrasound Elastography
This work addresses a domain-specific problem in medical imaging for ultrasound elastography practitioners, offering an incremental improvement by integrating physical laws into unsupervised methods.
The paper tackled the problem of improving lateral displacement estimation in Ultrasound Elastography by incorporating physical constraints on Poisson's ratio, resulting in substantial quality improvements as shown in experiments on phantom and in vivo data.
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.