PSF dedicated to estimation of displacement vectors for tissue elasticity imaging with ultrasound
Publication: Research - peer-review › Journal article – Annual report year: 2007
This paper investigates a new approach devoted to displacement vector estimation in ultrasound imaging. The main idea is to adapt the image formation to a given displacement estimationmethod to increase the precision
of the estimation. The displacement is identified as the
zero crossing of the phase of the complex cross-correlation
between signals extracted from the lateral direction of the
ultrasound RF image. For precise displacement estimation,
a linearity of the phase slope is needed as well as a high
phase slope. Consequently, a particular point spread function (PSF) dedicated to this estimator is designed. This
PSF, showing oscillations in the lateral direction, leads to
synthesis of lateral RF signals. The estimation is included
in a 2-D displacement vector estimation method. The improvement of this approach is evaluated quantitatively by
simulation studies. A comparison with a speckle tracking
technique is also presented. The lateral oscillations improve both the speckle tracking estimation and our 2-D estimation method. Using our dedicated images, the precision of the estimation is improved by reducing the standard deviation of the lateral displacement error by a factor of 2 for speckle tracking and more than 3 with our method compared to using conventional images. Our method performs 7
times better than speckle tracking. Experimentally, the improvement in the case of a pure lateral translation reaches a factor of 7. Finally, the experimental feasibility of the 2-D displacement vector estimation is demonstrated on data acquired from a Cryogel phantom.
| Original language | English |
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| Journal | IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control |
| Publication date | 2007 |
| Volume | 54 |
| Journal number | 4 |
| Pages | 746-756 |
| DOIs | |
| State | Published |
| Citations | Web of Science® Times Cited: 16 |
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ID: 3077877