2-D Locally Regularized Tissue Strain Estimation From Radio-Frequency Ultrasound Images: Theoretical Developments and Results on Experimental Data

In this paper, a 2-D locally regularized strain estimation method for imaging deformation of soft biological tissues from radio-frequency (RF) ultrasound (US) data is introduced. Contrary to most 2-D techniques that model the compression-induced local displacement as a 2-D shift, our algorithm also...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on medical imaging Ročník 27; číslo 2; s. 145 - 160
Hlavní autoři: Brusseau, E., Kybic, J., Deprez, J.-F., Basset, O.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.02.2008
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Animals
Anisotropic magnetoresistance
Anisotropy
Biological system modeling
Biological tissues
Capacitive sensors
Computer Simulation
Connective Tissue - diagnostic imaging
Connective Tissue - physiology
Deformable models
Economic models
Elasticity
Elasticity Imaging Techniques - instrumentation
Elasticity Imaging Techniques - methods
Elasticity Imaging Techniques - trends
Elastography
Humans
Image coding
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Image resolution
Models, Biological
Noise robustness
optimization
Phantoms, Imaging
Radio frequency
Reproducibility of Results
Sensitivity and Specificity
strain estimation
Studies
Ultrasonic imaging
ultrasound (US)
United States > US
ISSN:0278-0062, 1558-254X
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this paper, a 2-D locally regularized strain estimation method for imaging deformation of soft biological tissues from radio-frequency (RF) ultrasound (US) data is introduced. Contrary to most 2-D techniques that model the compression-induced local displacement as a 2-D shift, our algorithm also considers a local scaling factor in the axial direction. This direction-dependent model of tissue motion and deformation is induced by the highly anisotropic resolution of RF US images. Optimal parameters are computed through the constrained maximization of a similarity criterion defined as the normalized correlation coefficient. Its value at the solution is then used as an indicator of estimation reliability, the probability of correct estimation increasing with the correlation value. In case of correlation loss, the estimation integrates an additional constraint, imposing local continuity within displacement and strain fields. Using local scaling factors and regularization increase the method's robustness with regard to decorrelation noise, resulting in a wider range of precise measurements. Results on simulated US data from a mechanically homogeneous medium subjected to successive uniaxial loadings demonstrate that our method is theoretically able to accurately estimate strains up to 17%. Experimental strain images of phantom and cut specimens of bovine liver clearly show the harder inclusions.
Bibliografie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
content type line 23
ObjectType-Article-1
ObjectType-Feature-2
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2007.897408