Evaluation of stiffness feedback for hard nodule identification on a phantom silicone model.

Haptic information in robotic surgery can significantly improve clinical outcomes and help detect hard soft-tissue inclusions that indicate potential abnormalities. Visual representation of tissue stiffness information is a cost-effective technique. Meanwhile, direct force feedback, although considerably more expensive than visual representation, is an intuitive method of conveying information regarding tissue stiffness to surgeons. In this study, real-time visual stiffness feedback by sliding indentation palpation is proposed, validated, and compared with force feedback involving human subjects. In an experimental tele-manipulation environment, a dynamically updated color map depicting the stiffness of probed soft tissue is presented via a graphical interface. The force feedback is provided, aided by a master haptic device. The haptic device uses data acquired from an F/T sensor attached to the end-effector of a tele-manipulated robot. Hard nodule detection performance is evaluated for 2 modes (force feedback and visual stiffness feedback) of stiffness feedback on an artificial organ containing buried stiff nodules. From this artificial organ, a virtual-environment tissue model is generated based on sliding indentation measurements. Employing this virtual-environment tissue model, we compare the performance of human participants in distinguishing differently sized hard nodules by force feedback and visual stiffness feedback. Results indicate that the proposed distributed visual representation of tissue stiffness can be used effectively for hard nodule identification. The representation can also be used as a sufficient substitute for force feedback in tissue palpation.

Finite-element modeling of soft tissue rolling indentation.

We describe a finite-element (FE) model for simulating wheel-rolling tissue deformations using a rolling FE model (RFEM). A wheeled probe performing rolling tissue indentation has proven to be a promising approach for compensating for the loss of haptic and tactile feedback experienced during robotic-assisted minimally invasive surgery (H. Liu, D. P. Noonan, B. J. Challacombe, P. Dasgupta, L. D. Seneviratne, and K. Althoefer, "Rolling mechanical imaging for tissue abnormality localization during minimally invasive surgery, " IEEE Trans. Biomed. Eng., vol. 57, no. 2, pp. 404-414, Feb. 2010; K. Sangpradit, H. Liu, L. Seneviratne, and K. Althoefer, "Tissue identification using inverse finite element analysis of rolling indentation," in Proc. IEEE Int. Conf. Robot. Autom. , Kobe, Japan, 2009, pp. 1250-1255; H. Liu, D. Noonan, K. Althoefer, and L. Seneviratne, "The rolling approach for soft tissue modeling and mechanical imaging during robot-assisted minimally invasive surgery," in Proc. IEEE Int. Conf. Robot. Autom., May 2008, pp. 845-850; H. Liu, P. Puangmali, D. Zbyszewski, O. Elhage, P. Dasgupta, J. S. Dai, L. Seneviratne, and K. Althoefer, "An indentation depth-force sensing wheeled probe for abnormality identification during minimally invasive surgery," Proc. Inst. Mech. Eng., H, vol. 224, no. 6, pp. 751-63, 2010; D. Noonan, H. Liu, Y. Zweiri, K. Althoefer, and L. Seneviratne, "A dual-function wheeled probe for tissue viscoelastic property identification during minimally invasive surgery," in Proc. IEEE Int. Conf. Robot. Autom. , 2008, pp. 2629-2634; H. Liu, J. Li, Q. I. Poon, L. D. Seneviratne, and K. Althoefer, "Miniaturized force indentation-depth sensor for tissue abnormality identification," IEEE Int. Conf. Robot. Autom., May 2010, pp. 3654-3659). A sound understanding of wheel-tissue rolling interaction dynamics will facilitate the evaluation of signals from rolling indentation. In this paper, we model the dynamic interactions between a wheeled probe and a soft tissue sample using the ABAQUS FE software package. The aim of this work is to more precisely locate abnormalities within soft tissue organs using RFEM and hence aid surgeons to improve diagnostic ability. The soft tissue is modeled as a nonlinear hyperelastic material with geometrical nonlinearity. The proposed RFEM was validated on a silicone phantom and a porcine kidney sample. The results show that the proposed method can predict the wheel-tissue interaction forces of rolling indentation with good accuracy and can also accurately identify the location and depth of simulated tumors.