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PubMed · 15957599

Computer vision elastography: speckle adaptive motion estimation for elastography using ultrasound sequences.

James Revell·Majid Mirmehdi·Donal McNally
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Abstract

We present the development and validation of an image based speckle tracking methodology, for determining temporal two-dimensional (2-D) axial and lateral displacement and strain fields from ultrasound video streams. We refine a multiple scale region matching approach incorporating novel solutions to known speckle tracking problems. Key contributions include automatic similarity measure selection to adapt to varying speckle density, quantifying trajectory fields, and spatiotemporal elastograms. Results are validated using tissue mimicking phantoms and in vitro data, before applying them to in vivo musculoskeletal ultrasound sequences. The method presented has the potential to improve clinical knowledge of tendon pathology from carpel tunnel syndrome, inflammation from implants, sport injuries, and many others.

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AlgorithmsArtificial IntelligenceConnective TissueElasticityHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalIn Vitro TechniquesMovementPattern Recognition, AutomatedPhantoms, ImagingStress, MechanicalSubtraction TechniqueTendonsUltrasonography, Doppler, DuplexClinical TrialJournal ArticleResearch Support, Non-U.S. Gov't

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James Revell, Majid Mirmehdi, Donal McNally. 2005. Computer vision elastography: speckle adaptive motion estimation for elastography using ultrasound sequences.. https://doi.org/10.1109/tmi.2005.848331

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