A Calibration Approach for Elasticity Estimation with Medical Tools
Soft tissue elasticity is directly related to different stages of diseases and can be used for tissue identification during minimally invasive procedures. By palpating a tissue with a robot in a minimally invasive fashion force-displacement curves can be acquired. However, force-displacement curves...
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| Format: | Article |
| Language: | English |
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De Gruyter
2024-09-01
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| Series: | Current Directions in Biomedical Engineering |
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| Online Access: | https://doi.org/10.1515/cdbme-2024-1077 |
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| author | Grube S. Neidhardt M. Hermann A.-K. Sprenger J. Abdolazizi K. Latus S. Cyron C. J. Schlaefer A. |
| author_facet | Grube S. Neidhardt M. Hermann A.-K. Sprenger J. Abdolazizi K. Latus S. Cyron C. J. Schlaefer A. |
| author_sort | Grube S. |
| collection | DOAJ |
| description | Soft tissue elasticity is directly related to different stages of diseases and can be used for tissue identification during minimally invasive procedures. By palpating a tissue with a robot in a minimally invasive fashion force-displacement curves can be acquired. However, force-displacement curves strongly depend on the tool geometry which is often complex in the case of medical tools. Hence, a tool calibration procedure is desired to directly map force-displacement curves to the corresponding tissue elasticity.We present an experimental setup for calibrating medical tools with a robot. First, we propose to estimate the elasticity of gelatin phantoms by spherical indentation with a state-of-the-art contact model. We estimate force-displacement curves for different gelatin elasticities and temperatures. Our experiments demonstrate that gelatin elasticity is highly dependent on temperature, which can lead to an elasticity offset if not considered. Second, we propose to use a more complex material model, e.g., a neural network, that can be trained with the determined elasticities. Considering the temperature of the gelatin sample we can represent different elasticities per phantom and thereby increase our training data.We report elasticity values ranging from 10 to 40 kPa for a 10% gelatin phantom, depending on temperature. |
| format | Article |
| id | doaj-art-f09b7c71b5d94c2194aaf09a3cfff113 |
| institution | Kabale University |
| issn | 2364-5504 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Current Directions in Biomedical Engineering |
| spelling | doaj-art-f09b7c71b5d94c2194aaf09a3cfff1132025-02-02T15:45:00ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042024-09-011029910210.1515/cdbme-2024-1077A Calibration Approach for Elasticity Estimation with Medical ToolsGrube S.0Neidhardt M.1Hermann A.-K.2Sprenger J.3Abdolazizi K.4Latus S.5Cyron C. J.6Schlaefer A.7Hamburg University of Technology, Institute of Medical Technology and Intelligent Systems,Hamburg, GermanyInstitute of Medical Technology and Intelligent Systems, Hamburg University of Technology,Hamburg, GermanyInstitute of Medical Technology and Intelligent Systems, Hamburg University of Technology,Hamburg, GermanyInstitute of Medical Technology and Intelligent Systems, Hamburg University of Technology,Hamburg, GermanyInstitute for Continuum and Mate-rial Mechanics, Hamburg University of Technology,Hamburg, GermanyInstitute of Medical Technology and Intelligent Systems, Hamburg University of Technology,Hamburg, GermanyInstitute for Continuum and Mate-rial Mechanics, Hamburg University of Technology,Hamburg, GermanyInstitute of Medical Technology and Intelligent Systems, Hamburg University of Technology,Hamburg, GermanySoft tissue elasticity is directly related to different stages of diseases and can be used for tissue identification during minimally invasive procedures. By palpating a tissue with a robot in a minimally invasive fashion force-displacement curves can be acquired. However, force-displacement curves strongly depend on the tool geometry which is often complex in the case of medical tools. Hence, a tool calibration procedure is desired to directly map force-displacement curves to the corresponding tissue elasticity.We present an experimental setup for calibrating medical tools with a robot. First, we propose to estimate the elasticity of gelatin phantoms by spherical indentation with a state-of-the-art contact model. We estimate force-displacement curves for different gelatin elasticities and temperatures. Our experiments demonstrate that gelatin elasticity is highly dependent on temperature, which can lead to an elasticity offset if not considered. Second, we propose to use a more complex material model, e.g., a neural network, that can be trained with the determined elasticities. Considering the temperature of the gelatin sample we can represent different elasticities per phantom and thereby increase our training data.We report elasticity values ranging from 10 to 40 kPa for a 10% gelatin phantom, depending on temperature.https://doi.org/10.1515/cdbme-2024-1077young’s modulusgelatin phantomstool calibrationpalpationsoft tissue |
| spellingShingle | Grube S. Neidhardt M. Hermann A.-K. Sprenger J. Abdolazizi K. Latus S. Cyron C. J. Schlaefer A. A Calibration Approach for Elasticity Estimation with Medical Tools Current Directions in Biomedical Engineering young’s modulus gelatin phantoms tool calibration palpation soft tissue |
| title | A Calibration Approach for Elasticity Estimation with Medical Tools |
| title_full | A Calibration Approach for Elasticity Estimation with Medical Tools |
| title_fullStr | A Calibration Approach for Elasticity Estimation with Medical Tools |
| title_full_unstemmed | A Calibration Approach for Elasticity Estimation with Medical Tools |
| title_short | A Calibration Approach for Elasticity Estimation with Medical Tools |
| title_sort | calibration approach for elasticity estimation with medical tools |
| topic | young’s modulus gelatin phantoms tool calibration palpation soft tissue |
| url | https://doi.org/10.1515/cdbme-2024-1077 |
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