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A quality-guided displacement tracking algorithm for ultrasonic elasticity imaging.

Chen L, Treece GM, Lindop JE, Gee AH, Prager RW - Med Image Anal (2008)

Bottom Line: This increases the accuracy and reduces the computational expense compared with exhaustive search.This paper introduces a novel displacement tracking algorithm, with a search strategy guided by a data quality indicator.Comparisons with existing methods show that the proposed algorithm is more robust when the displacement distribution is challenging.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK. lc420@eng.cam.ac.uk

ABSTRACT
Displacement estimation is a key step in the evaluation of tissue elasticity by quasistatic strain imaging. An efficient approach may incorporate a tracking strategy whereby each estimate is initially obtained from its neighbours' displacements and then refined through a localized search. This increases the accuracy and reduces the computational expense compared with exhaustive search. However, simple tracking strategies fail when the target displacement map exhibits complex structure. For example, there may be discontinuities and regions of indeterminate displacement caused by decorrelation between the pre- and post-deformation radio frequency (RF) echo signals. This paper introduces a novel displacement tracking algorithm, with a search strategy guided by a data quality indicator. Comparisons with existing methods show that the proposed algorithm is more robust when the displacement distribution is challenging.

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Related in: MedlinePlus

Same data as in Fig. 4a. (a) Displacement obtained by the single-seed quality-guided tracking algorithm. (b) Early displacement propagation from the single-seed. Dark pixels indicate points that have had their displacement estimated by phase zero search, while bright pixels indicate unprocessed points. (c)–(e) Further displacement propagation into the high quality region enclosed by the arc. (f)–(h) Subsequent propagation into remaining high quality regions, reached around the bottom of the arc. (i) When all high quality regions are exhausted, displacement estimates are propagated into the lower quality regions at the top and bottom of the frame, and finally (j) into the noisy arc itself.
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fig5: Same data as in Fig. 4a. (a) Displacement obtained by the single-seed quality-guided tracking algorithm. (b) Early displacement propagation from the single-seed. Dark pixels indicate points that have had their displacement estimated by phase zero search, while bright pixels indicate unprocessed points. (c)–(e) Further displacement propagation into the high quality region enclosed by the arc. (f)–(h) Subsequent propagation into remaining high quality regions, reached around the bottom of the arc. (i) When all high quality regions are exhausted, displacement estimates are propagated into the lower quality regions at the top and bottom of the frame, and finally (j) into the noisy arc itself.

Mentions: The single-seed, quality-guided tracking algorithm retrieves a plausible deformation distribution without any gross errors outside the arc-shaped region – see Fig. 5a. The propagation sequence in Fig. 5b–j exhibits the behaviour anticipated in Fig. 1, with high quality data in the central band processed first, then the lower quality data at the top and bottom, and finally the noisy arc itself. Here, estimation errors are inevitable, but they are prevented from propagating into other regions of the frame. Without constraints on the propagation direction, the quality-guided tracking algorithm is capable of retrieving the displacement distribution of a geometrically irregular region in one pass, while minimizing the effects of estimation errors.


A quality-guided displacement tracking algorithm for ultrasonic elasticity imaging.

Chen L, Treece GM, Lindop JE, Gee AH, Prager RW - Med Image Anal (2008)

Same data as in Fig. 4a. (a) Displacement obtained by the single-seed quality-guided tracking algorithm. (b) Early displacement propagation from the single-seed. Dark pixels indicate points that have had their displacement estimated by phase zero search, while bright pixels indicate unprocessed points. (c)–(e) Further displacement propagation into the high quality region enclosed by the arc. (f)–(h) Subsequent propagation into remaining high quality regions, reached around the bottom of the arc. (i) When all high quality regions are exhausted, displacement estimates are propagated into the lower quality regions at the top and bottom of the frame, and finally (j) into the noisy arc itself.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2665204&req=5

fig5: Same data as in Fig. 4a. (a) Displacement obtained by the single-seed quality-guided tracking algorithm. (b) Early displacement propagation from the single-seed. Dark pixels indicate points that have had their displacement estimated by phase zero search, while bright pixels indicate unprocessed points. (c)–(e) Further displacement propagation into the high quality region enclosed by the arc. (f)–(h) Subsequent propagation into remaining high quality regions, reached around the bottom of the arc. (i) When all high quality regions are exhausted, displacement estimates are propagated into the lower quality regions at the top and bottom of the frame, and finally (j) into the noisy arc itself.
Mentions: The single-seed, quality-guided tracking algorithm retrieves a plausible deformation distribution without any gross errors outside the arc-shaped region – see Fig. 5a. The propagation sequence in Fig. 5b–j exhibits the behaviour anticipated in Fig. 1, with high quality data in the central band processed first, then the lower quality data at the top and bottom, and finally the noisy arc itself. Here, estimation errors are inevitable, but they are prevented from propagating into other regions of the frame. Without constraints on the propagation direction, the quality-guided tracking algorithm is capable of retrieving the displacement distribution of a geometrically irregular region in one pass, while minimizing the effects of estimation errors.

Bottom Line: This increases the accuracy and reduces the computational expense compared with exhaustive search.This paper introduces a novel displacement tracking algorithm, with a search strategy guided by a data quality indicator.Comparisons with existing methods show that the proposed algorithm is more robust when the displacement distribution is challenging.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering, University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK. lc420@eng.cam.ac.uk

ABSTRACT
Displacement estimation is a key step in the evaluation of tissue elasticity by quasistatic strain imaging. An efficient approach may incorporate a tracking strategy whereby each estimate is initially obtained from its neighbours' displacements and then refined through a localized search. This increases the accuracy and reduces the computational expense compared with exhaustive search. However, simple tracking strategies fail when the target displacement map exhibits complex structure. For example, there may be discontinuities and regions of indeterminate displacement caused by decorrelation between the pre- and post-deformation radio frequency (RF) echo signals. This paper introduces a novel displacement tracking algorithm, with a search strategy guided by a data quality indicator. Comparisons with existing methods show that the proposed algorithm is more robust when the displacement distribution is challenging.

Show MeSH
Related in: MedlinePlus