  Ultrasound elastography is used to estimate the mechanical properties of the
tissue by monitoring its response to an internal or external force. Different
levels of deformation are obtained from different tissue types depending on
their mechanical properties, where stiffer tissues deform less. Given two radio
frequency (RF) frames collected before and after some deformation, we estimate
displacement and strain images by comparing the RF frames. The quality of the
strain image is dependent on the type of motion that occurs during deformation.
In-plane axial motion results in high-quality strain images, whereas
out-of-plane motion results in low-quality strain images. In this paper, we
introduce a new method using a convolutional neural network (CNN) to determine
the suitability of a pair of RF frames for elastography in only 5.4 ms. Our
method could also be used to automatically choose the best pair of RF frames,
yielding a high-quality strain image. The CNN was trained on 3,818 pairs of RF
frames, while testing was done on 986 new unseen pairs, achieving an accuracy
of more than 91%. The RF frames were collected from both phantom and in vivo
data.
