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.

本研究提出了一种基于物理启发的无监督正则化弹性成像方法，通过对材料泊松比的约束，改善了横向位移估计的质量。实验结果表明，该方法可以显著提高弹性成像中横向位移的估计质量。

基于物理模型的无监督正则化超声弹性成像约束

Physically Inspired Constraint for Unsupervised Regularized Ultrasound Elastography

Convolutional Neural Networks (CNN) have been found to have great potential
in optical flow problems thanks to an abundance of data available for training
a deep network. The displacement estimation step in UltraSound Elastography
(USE) can be viewed as an optical flow problem. Despite the high performance of
CNNs in optical flow, they have been rarely used for USE due to unique
challenges that both input and output of USE networks impose. Ultrasound data
has much higher high-frequency content compared to natural images. The outputs
are also drastically different, where displacement values in USE are often
smooth without sharp motions or discontinuities. The general trend is currently
to use pre-trained networks and fine-tune them on a small simulation ultrasound
database. However, realistic ultrasound simulation is computationally
expensive. Also, the simulation techniques do not model complex motions,
nonlinear and frequency-dependent acoustics, and many sources of artifact in
ultrasound imaging. Herein, we propose an unsupervised fine-tuning technique
which enables us to employ a large unlabeled dataset for fine-tuning of a CNN
optical flow network. We show that the proposed unsupervised fine-tuning method
substantially improves the performance of the network and reduces the artifacts
generated by networks trained on computer vision databases.

本研究提出了一种无监督微调方法，利用大量未标记的数据集对 CNN 光流网络微调，在超声弹性成像中的应用，可以在降低计算机视觉数据库训练的网络生成的伪影的同时，显着提高网络性能。