Real-Time Vision-Based Stiffness Mapping †

Abstract

This paper presents new findings concerning a hand-held stiffness probe for the medical diagnosis of abnormalities during palpation of soft-tissue. Palpation is recognized by the medical community as an essential and low-cost method to detect and diagnose disease in soft-tissue. However, differences are often subtle and clinicians need to train for many years before they can conduct a reliable diagnosis. The probe presented here fills this gap providing a means to easily obtain stiffness values of soft tissue during a palpation procedure. Our stiffness sensor is equipped with a multi degree of freedom (DoF) Aurora magnetic tracker, allowing us to track and record the 3D position of the probe whilst examining a tissue area, and generate a 3D stiffness map in real-time. The stiffness probe was integrated in a robotic arm and tested in an artificial environment representing a good model of soft tissue organs; the results show that the sensor can accurately measure and map the stiffness of a silicon phantom embedded with areas of varying stiffness.

Keywords: hand-held probe; medical examination; palpation; soft tissue characterization; stiffness sensor.

Figure 1

Multi-axis stiffness sensor interacting with a soft surface. The definition of the angles is represented in the upper left part of the image.

Figure 2

Operating mechanical principle. Contact between the soft surface and the sensor showing the interacting forces and the differential force between the linear model with an embedded hard spring (HS) and the three modules with embedded softer springs (SS1, SS2, SS3).

Figure 3

Stiffness probe integrated with a commercially available tracking system: (a) Exploded view of the sensor showing the position of the Aurora tracker. (b) Schematic representation of the sensor’s working principle: the interaction with external objects generates the sliding of the indenters, hence, the visual features change their positions in the camera’s image. The correlation between the indenters and the features allows to measure the new positions of the indenters in the local reference frame. A static transformation maps the new positions of the indenters from the local frame into the tracker frame.

Figure 4

Stiffness probe fixed at the tip of the robotic arm: the interaction with external objects generates the sliding of the indenters. Hence, the visual features change their positions in the camera’s image. The correlation between the indenters and the features allows for measuring the new positions of the indenters in the local reference frame. A static transformation maps the new positions of the indenters from the local frame into the robot kinematic chain.

Figure 5

Manual tests for the artificial stiffness samples with spring constant of 0.29 N/mm and 0.62 N/mm: Correlation between the measured stiffness and the orientation of the hand-held probe, which is defined by the pan angle $\theta$ (a,e) and the tilt angle $\alpha$ (b,f); Distribution of the stiffness during the experiments (c,g); The stiffness variation in function of the two angles (d,h). The mean of the stiffness and the standard deviation are 0.29 and 0.01 for the first sample, and 0.68 and 0.08 for the second.

Figure 6

Stiffness mapping of homogeneous silicone phantoms: (a) the experimental setup; The discrimination between the different stiffness values of the silicone samples is visible in the coloured points in (b); The post-processed stiffness map is illustrated in (c).

Figure 7

Computation of the stiffness map: CAD model of the phantom mould (a); Experimental setup (b); Post-processed map and generated surface are shown in (c,d). The stiffness of the track (red) is successfully distinguished from the surrounding silicone (green).