Co-robotic synthetic tracked aperture ultrasound imaging with cross-correlation based dynamic error compensation and virtual fixture control

Autor: Haichong K. Zhang, Rodolfo Finocchi, Emad M. Boctor, Kalyna Apkarian
Rok vydání: 2016
Předmět:
Zdroj: 2016 IEEE International Ultrasonics Symposium (IUS).
DOI: 10.1109/ultsym.2016.7728522
Popis: Acquiring high resolution images in deep regions is challenging in ultrasound imaging due to limited probe aperture size and low transmit frequency usage. The concept of synthetic tracked aperture ultrasound (STRATUS) imaging is introduced to extend the effective aperture size by moving the probe while accurately tracking its orientation and translation. Based on the synthetic aperture technique, sub-apertures from each pose can be synthesized to construct a high-resolution image. In particular, we propose a mechanical tracking configuration using a 6 degree-of-freedom (DOF) robotic arm with force sensors that not only provides a robust tracking accuracy, but also enables co-operative control. The ultrasound probe is moved by an operator, while a virtual fixture uses force feedback of the robotic arm to constrain the motion to be on a desired plane or trajectory. Furthermore, we developed an algorithm to mitigate the potential errors between consecutive poses, such as tracking inaccuracy, tissue deformation, and phase aberration. Those errors were extracted by computing subtle image shift through cross-correlation for all neighboring poses, and the procedure is dynamically applied to the entire image. Comparing the STRATUS image to a conventional single pose image, the full width at the half maximum (FWHM) of a point target located at a depth of around 85 mm improved from 3.13 mm to 2.78 mm, and SNR improved from 28.96 dB to 30.27 dB. In addition, the dynamic error compensation further improved the FWHM and SNR to be 1.15 mm and 33.17 dB, respectively. The results proved the feasibility of the co-robotic STRATUS imaging, and dynamic error compensation improved the system's tolerance to errors.
Databáze: OpenAIRE