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Optical coherence elastography needle for biomechanical characterization of deep tissue
Publikationstyp
Conference Paper
Date Issued
2023
Sprache
English
Author(s)
Behrendt, Finn
First published in
Number in series
14228
Volume
14228 LNCS
Start Page
607
End Page
617
Citation
25th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Springer International Publishing AG
ISBN
978-3-031-43995-7
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.
Subjects
Optical Coherence Tomography
Prostate Biopsy
Tissue Elasticity
DDC Class
004: Computer Sciences
610: Medicine, Health