English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Needle insertion is common during minimally invasive interventions such asbiopsy or brachytherapy. During soft tissue needle insertion, forces acting atthe needle tip cause tissue deformation and needle deflection. Accurate needletip force measurement provides information on needle-tissue interaction andhelps detecting and compensating potential misplacement. For this purpose weintroduce an image-based needle tip force estimation method using an opticalfiber imaging the deformation of an epoxy layer below the needle tip over time.For calibration and force estimation, we introduce a novel deep learning-basedfused convolutional GRU-CNN model which effectively exploits thespatio-temporal data structure. The needle is easy to manufacture and our modelachieves a mean absolute error of 1.76 +- 1.5 mN with a cross-correlationcoefficient of 0.9996, clearly outperforming other methods. We test needleswith different materials to demonstrate that the approach can be adapted fordifferent sensitivities and force ranges. Furthermore, we validate our approachin an ex-vivo prostate needle insertion scenario.

Needle Tip Force Estimation using an OCT Fiber and a Fused convGRU-CNN Architecture