  Robot-assisted surgery has advantages compared to conventional laparoscopic
procedures, e.g., precise movement of the surgical instruments, improved
dexterity, and high-resolution visualization of the surgical field. However,
mechanical tissue properties may provide additional information, e.g., on the
location of lesions or vessels. While elastographic imaging has been proposed,
it is not readily available as an online modality during robot-assisted
surgery. We propose modifying a da~Vinci surgical instrument to realize optical
coherence elastography (OCE) for quantitative elasticity estimation. The
modified da~Vinci instrument is equipped with piezoelectric elements for shear
wave excitation and we employ fast optical coherence tomography (OCT) imaging
to track propagating wave fields, which are directly related to biomechanical
tissue properties. All high-voltage components are mounted at the proximal end
outside the patient. We demonstrate that external excitation at the instrument
shaft can effectively stimulate shear waves, even when considering damping.
Comparing conventional and deep learning-based signal processing, resulting in
mean absolute errors of 19.27 kPa and 6.29 kPa, respectively. These results
illustrate that precise quantitative elasticity estimates can be obtained. We
also demonstrate quantitative elasticity estimation on ex-vivo tissue samples
of heart, liver and stomach, and show that the measurements can be used to
distinguish soft and stiff tissue types.
