Many medical procedures involving needle insertion into soft tissues, such as anesthesia, biopsy, brachytherapy, and placement of electrodes, are performed without image guidance. In such procedures, haptic detection of changing tissue properties at different depths during needle insertion is important for needle localization and detection of subsurface structures. However, changes in tissue mechanical properties deep inside the tissue are difficult for human operators to sense, because the relatively large friction force between the needle shaft and the surrounding tissue masks the smaller tip forces. A novel robotic coaxial needle insertion assistant, which enhances operator force perception, is presented. This one-degree-of-freedom cable-driven robot provides to the operator a scaled version of the force applied by the needle tip to the tissue, using a novel design and sensors that separate the needle tip force from the shaft friction force. The ability of human operators to use the robot to detect membranes embedded in artificial soft tissue was tested under the conditions of 1) tip force and shaft force feedback, and 2) tip force only feedback. The ratio of successful to unsuccessful membrane detections was significantly higher (up to 50%) when only the needle tip force was provided to the user.

The control-based method developed for force estimation is compatible with the neurosurgical application and is also capable of measuring tissue resistance without any additional sensors. Force feedback in minimally invasive surgery allows the human operator to manipulate tissues as if his/her hands were in contact with the patient organs.

Abstract-During needle insertion in soft tissue, detection of change in tissue properties is important both for diagnosis to detect pathological tissue and for prevention to avoid puncture of important structures. The presence of a membrane located deep inside the tissue results in a relatively small force variation at the needle tip that can be masked by relatively large friction force between the needle shaft and the surrounding tissue. Also, user perception of force can be limited due to the overall small force amplitude in some applications (e.g. brain surgery).A novel robotic coaxial needle insertion assistant was developed to enhance operator force perception. The coaxial needle separates the cutting force at the needle tip from shear friction on the needle shaft. The assistant is force controlled (admittance control), providing the operator with force feedback that is a scaled version of the force applied by the needle tip to the tissue. The effectiveness of the assistant in enhancing the detection of different tissue types was tested experimentally. Users were asked to blindly insert a needle into artificial tissues with membranes at various depths under two force feedback conditions: (1) shaft and tip force together, and (2) only tip force. The ratio of successful to unsuccessful membrane detection was significantly higher when only the needle tip force is displayed to the user. The system proved to be compliant with the clinical applications requirements.

No abstract

In brain surgery procedures, such as deep brain stimulation, drug-resistant epilepsy and tumour surgery, the patient is intentionally awakened to map functional neural bases via electrophysiological assessment. This assessment can involve patient's body movements; thus, increasing the mechanical load on the head-restraint systems used for keeping the skull still during the surgery. The loads exchanged between the head and the restraining device can potentially result into skin and bone damage. The aim of this work is to assess such loads for laying down the requirements of a surgical robotics system for dynamic head movements compensation by fast moving arms and by an active restraint able to damp such actions. A Mayfield Ò head clamp was tracked and instrumented with strain gages (SGs). SG locations were chosen according to finite element analyses. During an actual brain surgery, displacements and strains were measured and clustered according to events that generated them. Loads were inferred from strain data. The greatest force components were exerted vertically (median 5.5 N, maximum 151.87 N) with frequencies up to 1.5 Hz. Maximum measured displacement and velocity were 9 mm and 60 mm/s, with frequencies up to 2.8 Hz. The analysis of loads and displacements allowed to identify the surgery steps causing maximal loads on the headrestraint device.