Abstract
A novel modality of ultrasound imaging known as elastography has been shown to improve cancer detection for women with dense breast tissue. However, the scanning procedure for this technique is often difficult for a human to perform in a consistent manner and could conceivably benefit from robot assistance. In this work, we present a novel robot-assisted probabilistic elasticity mapping algorithm which uses Gaussian filter techniques to produce elastograms and uncertainty maps. We demonstrate the proposed approach using a 7-DOF robot manipulator on a gelatin phantom designed to imitate the elasticity of human tissue. The results indicate the algorithm is capable of imaging a \({6.5\,\mathrm{\text {m}\text {m}}}\) lesion and reducing map uncertainty in the observable region.
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Notes
- 1.
Some research reports numbers as high as 90%.
- 2.
A low PPV is synonomous to a high number of false positives.
- 3.
The transducer is limited to 128 lateral samples as opposed to thousands in the axial direction.
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Acknowledgment
This work was supported by the Office of the Assistant Secretary of Defense for Health Affairs through the Breast Cancer Research Program under Award No. W81XWH-17-1-0021 and W81XWH-17-1-0022.
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Napoli, M.E., Goswami, S., McAleavey, S.A., Doyley, M.M., Howard, T.M. (2022). Probabilistic Mapping of Tissue Elasticity for Robot-Assisted Medical Ultrasound. In: Asfour, T., Yoshida, E., Park, J., Christensen, H., Khatib, O. (eds) Robotics Research. ISRR 2019. Springer Proceedings in Advanced Robotics, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-030-95459-8_43
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