Analysis of elastography methods using mathematical and ex vivo data

Brett C. Byram; Michael R. Wahl; David Richard Holmes III; Amir Lerman; Richard A. Robb

doi:10.1117/12.480309

15 May 2003 Analysis of elastography methods using mathematical and ex vivo data

Brett C. Byram, Michael R. Wahl, David Richard Holmes III, Amir Lerman, Richard A. Robb

Proceedings Volume 5032, Medical Imaging 2003: Image Processing; (2003) https://doi.org/10.1117/12.480309
Event: Medical Imaging 2003, 2003, San Diego, California, United States

Abstract

Intravascular ultrasound (IVUS) currently has a limited ability to characterize endovascular anatomic properties. IVUS elastography enhances the ability to characterize the biomechanical properties of arterial walls. A mathematical phantom generator was developed based on the characteristics of 30MHz, 64 element IVUS catheter images from excised canine femoral arteries. The difference between high and low-pressure intra-arterial images was modeled using phase shifts. The increase in phase shift occurred randomly, generally at every three pixels in our images. Using mathematical phantoms, different methods for calculating elastograms were quantitatively analyzed. Specifically, the effect of standard cross correlation versus cross correlation of the integral of the inflection characteristics for a given set of data, and the effect of an algorithm utilizing a non-constant kernel, were assessed. The specific methods found to be most accurate on the mathematical phantom data were then applied to ex vivo canine data of a scarred and a healthy artery. The algorithm detected significant differences between these two sets of arterial data. It will be necessary to obtain and analyze several more sets of canine arterial data in order to determine the accuracy and reproducibility of the algorithm.

Citation Download Citation

Brett C. Byram, Michael R. Wahl, David Richard Holmes III, Amir Lerman, and Richard A. Robb "Analysis of elastography methods using mathematical and ex vivo data", Proc. SPIE 5032, Medical Imaging 2003: Image Processing, (15 May 2003); https://doi.org/10.1117/12.480309

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