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Varying ultrasound power level to distinguish surgical instruments and tissue.

Authors :
Ren, Hongliang
Anuraj, Banani
Dupont, Pierre E.
Source :
Medical & Biological Engineering & Computing. Mar2018, Vol. 56 Issue 3, p453-467. 15p. 8 Color Photographs, 3 Black and White Photographs, 7 Graphs.
Publication Year :
2018

Abstract

We investigate a new framework of surgical instrument detection based on power-varying ultrasound images with simple and efficient pixel-wise intensity processing. Without using complicated feature extraction methods, we identified the instrument with an estimated optimal power level and by comparing pixel values of varying transducer power level images. The proposed framework exploits the physics of ultrasound imaging system by varying the transducer power level to effectively distinguish metallic surgical instruments from tissue. This power-varying image-guidance is motivated from our observations that ultrasound imaging at different power levels exhibit different contrast enhancement capabilities between tissue and instruments in ultrasound-guided robotic beating-heart surgery. Using lower transducer power levels (ranging from 40 to 75% of the rated lowest ultrasound power levels of the two tested ultrasound scanners) can effectively suppress the strong imaging artifacts from metallic instruments and thus, can be utilized together with the images from normal transducer power levels to enhance the separability between instrument and tissue, improving intraoperative instrument tracking accuracy from the acquired noisy ultrasound volumetric images. We performed experiments in phantoms and ex vivo hearts in water tank environments. The proposed multi-level power-varying ultrasound imaging approach can identify robotic instruments of high acoustic impedance from low-signal-to-noise-ratio ultrasound images by power adjustments. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01400118
Volume :
56
Issue :
3
Database :
Academic Search Index
Journal :
Medical & Biological Engineering & Computing
Publication Type :
Academic Journal
Accession number :
128292218
Full Text :
https://doi.org/10.1007/s11517-017-1695-x