Abstract
Compression-based optical coherence elastography (OCE) enables characterization of soft tissue by estimating elastic properties. However, previous probe designs have been limited to surface applications. We propose a bevel tip OCE needle probe for percutaneous insertions, where biomechanical characterization of deep tissue could enable precise needle placement, e.g., in prostate biopsy. We consider a dual-fiber OCE needle probe that provides estimates of local strain and load at the tip. Using a novel setup, we simulate deep tissue indentations where frictional forces and bulk sample displacement can affect biomechanical characterization. Performing surface and deep tissue indentation experiments, we compare our approach with external force and needle position measurements at the needle shaft. We consider two tissue mimicking materials simulating healthy and cancerous tissue and demonstrate that our probe can be inserted into deep tissue layers. Compared to surface indentations, external force-position measurements are strongly affected by frictional forces and bulk displacement and show a relative error of 49.2% and 42.4% for soft and stiff phantoms, respectively. In contrast, quantitative OCE measurements show a reduced relative error of 26.4% and 4.9% for deep indentations of soft and stiff phantoms, respectively. Finally, we demonstrate that the OCE measurements can be used to effectively discriminate the tissue mimicking phantoms.
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Acknowledgements
This work was partially funded by Deutsche Forschungsgemeinschaft under Grant SCHL 1844/6-1, the \(i^3\) initiative of Hamburg University of Technology, and the Interdisciplinary Competence Center for Interface Research (ICCIR) on behalf of the University Medical Center Hamburg-Eppendorf and the Hamburg University of Technology.
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Mieling, R., Latus, S., Fischer, M., Behrendt, F., Schlaefer, A. (2023). Optical Coherence Elastography Needle for Biomechanical Characterization of Deep Tissue. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14228. Springer, Cham. https://doi.org/10.1007/978-3-031-43996-4_58
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