An unsupervised learning-based shear wave tracking method for ultrasound elastography (2404.16953v1)
Abstract: Shear wave elastography involves applying a non-invasive acoustic radiation force to the tissue and imaging the induced deformation to infer its mechanical properties. This work investigates the use of convolutional neural networks to improve displacement estimation accuracy in shear wave imaging. Our training approach is completely unsupervised, which allows to learn the estimation of the induced micro-scale deformations without ground truth labels. We also present an ultrasound simulation dataset where the shear wave propagation has been simulated via finite element method. Our dataset is made publicly available along with this paper, and consists in 150 shear wave propagation simulations in both homogenous and hetegeneous media, which represents a total of 20,000 ultrasound images. We assessed the ability of our learning-based approach to characterise tissue elastic properties (i.e., Young's modulus) on our dataset and compared our results with a classical normalised cross-correlation approach.
- Palmeri, M. L., McAleavey, S. A., Trahey, G. E., and Nightingale, K. R., “Ultrasonic tracking of acoustic radiation force-induced displacements in homogeneous media,” IEEE transactions on ultrasonics, ferroelectrics, and frequency control 53(7), 1300–1313 (2006).
- Loupas, T., Powers, J., and Gill, R. W., “An axial velocity estimator for ultrasound blood flow imaging, based on a full evaluation of the doppler equation by means of a two-dimensional autocorrelation approach,” IEEE transactions on ultrasonics, ferroelectrics, and frequency control 42(4), 672–688 (1995).
- Rouze, N. C., Wang, M. H., Palmeri, M. L., and Nightingale, K. R., “Parameters affecting the resolution and accuracy of 2-d quantitative shear wave images,” IEEE transactions on ultrasonics, ferroelectrics, and frequency control 59(8), 1729–1740 (2012).
- Tehrani, A. K. and Rivaz, H., “MPWC-Net++: evolution of optical flow pyramidal convolutional neural network for ultrasound elastography,” in [Medical Imaging 2021: Ultrasonic Imaging and Tomography ], 11602, 1160206, International Society for Optics and Photonics (2021).
- Chan, D. Y., Morris, D. C., Polascik, T. J., Palmeri, M. L., and Nightingale, K. R., “Deep convolutional neural networks for displacement estimation in ARFI imaging,” IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 68(7), 2472–2481 (2021).
- Ahmed, S., Kamal, U., and Hasan, M. K., “DSWE-Net: A deep learning approach for shear wave elastography and lesion segmentation using single push acoustic radiation force,” Ultrasonics 110, 106283 (2021).
- Delaunay, R., Hu, Y., and Vercauteren, T., “An unsupervised learning approach to ultrasound strain elastography with spatio-temporal consistency,” Physics in Medicine & Biology Online ahead of print (2021).
- Palmeri, M. L., Sharma, A. C., Bouchard, R. R., Nightingale, R. W., and Nightingale, K. R., “A finite-element method model of soft tissue response to impulsive acoustic radiation force,” IEEE transactions on ultrasonics, ferroelectrics, and frequency control 52(10), 1699–1712 (2005).
- Jensen, J. A. and Svendsen, N. B., “Calculation of pressure fields from arbitrarily shaped, apodized, and excited ultrasound transducers,” IEEE transactions on ultrasonics, ferroelectrics, and frequency control 39(2), 262–267 (1992).
- Tanter, M., Bercoff, J., Athanasiou, A., Deffieux, T., Gennisson, J.-L., Montaldo, G., Muller, M., Tardivon, A., and Fink, M., “Quantitative assessment of breast lesion viscoelasticity: initial clinical results using supersonic shear imaging,” Ultrasound in medicine & biology 34(9), 1373–1386 (2008).