Needle-Based Electrical Impedance Imaging Technology for Needle Navigation
Needle insertion is a common procedure in modern healthcare practices, such as blood sampling, tissue biopsy, and cancer treatment. Various guidance systems have been developed to reduce the risk of incorrect needle positioning. While ultrasound imaging is considered the gold standard, it has limita...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/5/590 |
| _version_ | 1797601063371538432 |
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| author | Jan Liu Ömer Atmaca Peter Paul Pott |
| author_facet | Jan Liu Ömer Atmaca Peter Paul Pott |
| author_sort | Jan Liu |
| collection | DOAJ |
| description | Needle insertion is a common procedure in modern healthcare practices, such as blood sampling, tissue biopsy, and cancer treatment. Various guidance systems have been developed to reduce the risk of incorrect needle positioning. While ultrasound imaging is considered the gold standard, it has limitations such as a lack of spatial resolution and subjective interpretation of 2D images. As an alternative to conventional imaging techniques, we have developed a needle-based electrical impedance imaging system. The system involves the classification of different tissue types using impedance measurements taken with a modified needle and the visualization in a MATLAB Graphical User Interface (GUI) based on the spatial sensitivity distribution of the needle. The needle was equipped with 12 stainless steel wire electrodes, and the sensitive volumes were determined using Finite Element Method (FEM) simulation. A k-Nearest Neighbors (k-NN) algorithm was used to classify different types of tissue phantoms with an average success rate of 70.56% for individual tissue phantoms. The results showed that the classification of the fat tissue phantom was the most successful (60 out of 60 attempts correct), while the success rate decreased for layered tissue structures. The measurement can be controlled in the GUI, and the identified tissues around the needle are displayed in 3D. The average latency between measurement and visualization was 112.1 ms. This work demonstrates the feasibility of using needle-based electrical impedance imaging as an alternative to conventional imaging techniques. Further improvements to the hardware and the algorithm as well as usability testing are required to evaluate the effectiveness of the needle navigation system. |
| first_indexed | 2024-03-11T03:56:12Z |
| format | Article |
| id | doaj.art-eaf45aa626f2489db8c325c50e1630bd |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-11T03:56:12Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-eaf45aa626f2489db8c325c50e1630bd2023-11-18T00:31:43ZengMDPI AGBioengineering2306-53542023-05-0110559010.3390/bioengineering10050590Needle-Based Electrical Impedance Imaging Technology for Needle NavigationJan Liu0Ömer Atmaca1Peter Paul Pott2Institute of Medical Device Technology, University of Stuttgart, 70569 Stuttgart, GermanyInstitute of Medical Device Technology, University of Stuttgart, 70569 Stuttgart, GermanyInstitute of Medical Device Technology, University of Stuttgart, 70569 Stuttgart, GermanyNeedle insertion is a common procedure in modern healthcare practices, such as blood sampling, tissue biopsy, and cancer treatment. Various guidance systems have been developed to reduce the risk of incorrect needle positioning. While ultrasound imaging is considered the gold standard, it has limitations such as a lack of spatial resolution and subjective interpretation of 2D images. As an alternative to conventional imaging techniques, we have developed a needle-based electrical impedance imaging system. The system involves the classification of different tissue types using impedance measurements taken with a modified needle and the visualization in a MATLAB Graphical User Interface (GUI) based on the spatial sensitivity distribution of the needle. The needle was equipped with 12 stainless steel wire electrodes, and the sensitive volumes were determined using Finite Element Method (FEM) simulation. A k-Nearest Neighbors (k-NN) algorithm was used to classify different types of tissue phantoms with an average success rate of 70.56% for individual tissue phantoms. The results showed that the classification of the fat tissue phantom was the most successful (60 out of 60 attempts correct), while the success rate decreased for layered tissue structures. The measurement can be controlled in the GUI, and the identified tissues around the needle are displayed in 3D. The average latency between measurement and visualization was 112.1 ms. This work demonstrates the feasibility of using needle-based electrical impedance imaging as an alternative to conventional imaging techniques. Further improvements to the hardware and the algorithm as well as usability testing are required to evaluate the effectiveness of the needle navigation system.https://www.mdpi.com/2306-5354/10/5/590bioimpedanceelectrical impedance imagingimpedance measurementsneedle navigationtissue classification |
| spellingShingle | Jan Liu Ömer Atmaca Peter Paul Pott Needle-Based Electrical Impedance Imaging Technology for Needle Navigation Bioengineering bioimpedance electrical impedance imaging impedance measurements needle navigation tissue classification |
| title | Needle-Based Electrical Impedance Imaging Technology for Needle Navigation |
| title_full | Needle-Based Electrical Impedance Imaging Technology for Needle Navigation |
| title_fullStr | Needle-Based Electrical Impedance Imaging Technology for Needle Navigation |
| title_full_unstemmed | Needle-Based Electrical Impedance Imaging Technology for Needle Navigation |
| title_short | Needle-Based Electrical Impedance Imaging Technology for Needle Navigation |
| title_sort | needle based electrical impedance imaging technology for needle navigation |
| topic | bioimpedance electrical impedance imaging impedance measurements needle navigation tissue classification |
| url | https://www.mdpi.com/2306-5354/10/5/590 |
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