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Real-Time Bioimpedance-Based Biopsy Needle Can Identify Tissue Type with High Spatial Accuracy

Tutkimustuotosvertaisarvioitu

Standard

Real-Time Bioimpedance-Based Biopsy Needle Can Identify Tissue Type with High Spatial Accuracy. / Halonen, Sanna; Kari, Juho; Ahonen, Petri; Kronström, Kai; Hyttinen, Jari.

julkaisussa: Annals of Biomedical Engineering, Vuosikerta 47, Nro 3, 03.2019, s. 836-851.

Tutkimustuotosvertaisarvioitu

Harvard

Halonen, S, Kari, J, Ahonen, P, Kronström, K & Hyttinen, J 2019, 'Real-Time Bioimpedance-Based Biopsy Needle Can Identify Tissue Type with High Spatial Accuracy', Annals of Biomedical Engineering, Vuosikerta. 47, Nro 3, Sivut 836-851. https://doi.org/10.1007/s10439-018-02187-9

APA

Halonen, S., Kari, J., Ahonen, P., Kronström, K., & Hyttinen, J. (2019). Real-Time Bioimpedance-Based Biopsy Needle Can Identify Tissue Type with High Spatial Accuracy. Annals of Biomedical Engineering, 47(3), 836-851. https://doi.org/10.1007/s10439-018-02187-9

Vancouver

Halonen S, Kari J, Ahonen P, Kronström K, Hyttinen J. Real-Time Bioimpedance-Based Biopsy Needle Can Identify Tissue Type with High Spatial Accuracy. Annals of Biomedical Engineering. 2019 maalis;47(3):836-851. https://doi.org/10.1007/s10439-018-02187-9

Author

Halonen, Sanna ; Kari, Juho ; Ahonen, Petri ; Kronström, Kai ; Hyttinen, Jari. / Real-Time Bioimpedance-Based Biopsy Needle Can Identify Tissue Type with High Spatial Accuracy. Julkaisussa: Annals of Biomedical Engineering. 2019 ; Vuosikerta 47, Nro 3. Sivut 836-851.

Bibtex - Lataa

@article{c07ac4bc8ca04bfa8d122a30a8e221fa,
title = "Real-Time Bioimpedance-Based Biopsy Needle Can Identify Tissue Type with High Spatial Accuracy",
abstract = "Histological analysis is meaningful in diagnosis only if the targeted tissue is obtained in the biopsy. Often, physicians have to take a tissue sample without accurate information about the location of the instrument tip. A novel biopsy needle with bioimpedance-based tissue identification has been developed to provide data for the automatic classification of the tissue type at the tip of the needle. The aim of this study was to examine the resolution of this identification method and to assess how tissue heterogeneities affect the measurement and tissue classification. Finite element method simulations of bioimpedance measurements were performed using a 3D model. In vivo data of a porcine model were gathered with a moving needle from fat, muscle, blood, liver, and spleen, and a tissue classifier was created and tested based on the gathered data. Simulations showed that very small targets were detectable, and targets of 2 texttimes 2 texttimes 2 mm3 and larger were correctly measurable. Based on the in vivo data, the performance of the tissue classifier was high. The total accuracy of classifying different tissues was approximately 94{\%}. Our results indicate that local bioimpedance-based tissue classification is feasible in vivo, and thus the method provides high potential to improve clinical biopsy procedures.",
author = "Sanna Halonen and Juho Kari and Petri Ahonen and Kai Kronstr{\"o}m and Jari Hyttinen",
note = "EXT={"}Halonen, Sanna{"}",
year = "2019",
month = "3",
doi = "10.1007/s10439-018-02187-9",
language = "English",
volume = "47",
pages = "836--851",
journal = "Annals of Biomedical Engineering",
issn = "0090-6964",
publisher = "Springer Verlag",
number = "3",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Real-Time Bioimpedance-Based Biopsy Needle Can Identify Tissue Type with High Spatial Accuracy

AU - Halonen, Sanna

AU - Kari, Juho

AU - Ahonen, Petri

AU - Kronström, Kai

AU - Hyttinen, Jari

N1 - EXT="Halonen, Sanna"

PY - 2019/3

Y1 - 2019/3

N2 - Histological analysis is meaningful in diagnosis only if the targeted tissue is obtained in the biopsy. Often, physicians have to take a tissue sample without accurate information about the location of the instrument tip. A novel biopsy needle with bioimpedance-based tissue identification has been developed to provide data for the automatic classification of the tissue type at the tip of the needle. The aim of this study was to examine the resolution of this identification method and to assess how tissue heterogeneities affect the measurement and tissue classification. Finite element method simulations of bioimpedance measurements were performed using a 3D model. In vivo data of a porcine model were gathered with a moving needle from fat, muscle, blood, liver, and spleen, and a tissue classifier was created and tested based on the gathered data. Simulations showed that very small targets were detectable, and targets of 2 texttimes 2 texttimes 2 mm3 and larger were correctly measurable. Based on the in vivo data, the performance of the tissue classifier was high. The total accuracy of classifying different tissues was approximately 94%. Our results indicate that local bioimpedance-based tissue classification is feasible in vivo, and thus the method provides high potential to improve clinical biopsy procedures.

AB - Histological analysis is meaningful in diagnosis only if the targeted tissue is obtained in the biopsy. Often, physicians have to take a tissue sample without accurate information about the location of the instrument tip. A novel biopsy needle with bioimpedance-based tissue identification has been developed to provide data for the automatic classification of the tissue type at the tip of the needle. The aim of this study was to examine the resolution of this identification method and to assess how tissue heterogeneities affect the measurement and tissue classification. Finite element method simulations of bioimpedance measurements were performed using a 3D model. In vivo data of a porcine model were gathered with a moving needle from fat, muscle, blood, liver, and spleen, and a tissue classifier was created and tested based on the gathered data. Simulations showed that very small targets were detectable, and targets of 2 texttimes 2 texttimes 2 mm3 and larger were correctly measurable. Based on the in vivo data, the performance of the tissue classifier was high. The total accuracy of classifying different tissues was approximately 94%. Our results indicate that local bioimpedance-based tissue classification is feasible in vivo, and thus the method provides high potential to improve clinical biopsy procedures.

U2 - 10.1007/s10439-018-02187-9

DO - 10.1007/s10439-018-02187-9

M3 - Article

VL - 47

SP - 836

EP - 851

JO - Annals of Biomedical Engineering

JF - Annals of Biomedical Engineering

SN - 0090-6964

IS - 3

ER -