Highly resolved strain imaging during needle insertion: Results with a novel biologically inspired device

Percutaneous needle insertions are a common part of minimally invasive surgery. However, the insertion process is necessarily disruptive to the substrate. Negative side effects are migration of deep-seated targets and trauma to the surrounding material. Mitigation of these effects is highly desirable, but relies on a detailed understanding of the needle-tissue interactions, which are difficult to capture at a sufficiently high resolution. Here, an adapted Digital Image Correlation (DIC) technique is used to quantify mechanical behaviour at the sliding interface, with resolution of measurement points which is better than 0.5mm, representing a marked improvement over the state of the art. A method for converting the Eulerian description of DIC output to Lagrangian displacements and strains is presented and the method is validated during the simple insertion of a symmetrical needle into a gelatine tissue phantom. The needle is comprised of four axially interlocked quadrants, each with a bevel tip. Tests are performed where the segments are inserted into the phantom simultaneously, or in a cyclic sequence taking inspiration from the unique insertion strategy associated to the ovipositor of certain wasps. Data from around the needle-tissue interface includes local strain variations, material dragged along the needle surface and relaxation of the phantom, which show that the cyclic actuation of individual needle segments is potentially able to mitigate tissue strain and could be used to reduce target migration.

Estimation of the uncertainty of elastic image registration with the demons algorithm

The accuracy of elastic image registration is limited. We propose an approach to detect voxels where registration based on the demons algorithm is likely to perform inaccurately, compared to other locations of the same image. The approach is based on the assumption that the local reproducibility of the registration can be regarded as a measure of uncertainty of the image registration. The reproducibility is determined as the standard deviation of the displacement vector components obtained from multiple registrations. These registrations differ in predefined initial deformations. The proposed approach was tested with artificially deformed lung images, where the ground truth on the deformation is known. In voxels where the result of the registration was less reproducible, the registration turned out to have larger average registration errors as compared to locations of the same image, where the registration was more reproducible. The proposed method can show a clinician in which area of the image the elastic registration with the demons algorithm cannot be expected to be accurate.