Bone Ablation Depth Estimation From Er:YAG Laser-Generated Acoustic Waves
Using a laser for cutting bones instead of the traditional saws improves a patient’s healing process. Additionally, the laser has the potential to reduce the collateral damage to the surrounding tissue if appropriately applied. This can be achieved by building additional sensing elements...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9966604/ |
| _version_ | 1811205689095225344 |
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| author | Carlo Seppi Antal Huck Arsham Hamidi Eva Schnider Massimiliano Filipozzi Georg Rauter Azhar Zam Philippe C. Cattin |
| author_facet | Carlo Seppi Antal Huck Arsham Hamidi Eva Schnider Massimiliano Filipozzi Georg Rauter Azhar Zam Philippe C. Cattin |
| author_sort | Carlo Seppi |
| collection | DOAJ |
| description | Using a laser for cutting bones instead of the traditional saws improves a patient’s healing process. Additionally, the laser has the potential to reduce the collateral damage to the surrounding tissue if appropriately applied. This can be achieved by building additional sensing elements besides the laser itself into an endoscope. To this end, we use a microsecond pulsed Erbium-doped Yttrium Aluminium Garnet (Er:YAG) laser to cut bones. During ablation, each pulse emits an acoustic shock wave that is captured by an air-coupled transducer. In our research, we use the data from these acoustic waves to predict the depth of the cut during the ablation process. We use a Neural Network (NN) to estimate the depth, where we use one or multiple consecutive measurements of acoustic waves. The NN outperforms the base-line method that assumes a constant ablation rate with each pulse to predict the depth. The results are evaluated and compared against the ground-truth depth measurements from Optical Coherence Tomography (OCT) images that measure the depth in real-time during the ablation process. |
| first_indexed | 2024-04-12T03:36:19Z |
| format | Article |
| id | doaj.art-2b9d99db6a5040e8871d5deb547ec592 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T03:36:19Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2b9d99db6a5040e8871d5deb547ec5922022-12-22T03:49:24ZengIEEEIEEE Access2169-35362022-01-011012660312661110.1109/ACCESS.2022.32256519966604Bone Ablation Depth Estimation From Er:YAG Laser-Generated Acoustic WavesCarlo Seppi0https://orcid.org/0000-0002-5906-3546Antal Huck1Arsham Hamidi2https://orcid.org/0000-0002-3138-8939Eva Schnider3https://orcid.org/0000-0002-0226-9519Massimiliano Filipozzi4Georg Rauter5https://orcid.org/0000-0001-9089-8181Azhar Zam6Philippe C. Cattin7https://orcid.org/0000-0001-8785-2713Department of Biomedical Engineering, University of Basel, Basel, SwitzerlandDepartment of Biomedical Engineering, University of Basel, Basel, SwitzerlandDepartment of Biomedical Engineering, University of Basel, Basel, SwitzerlandDepartment of Biomedical Engineering, University of Basel, Basel, SwitzerlandDepartment of Biomedical Engineering, University of Basel, Basel, SwitzerlandDepartment of Biomedical Engineering, University of Basel, Basel, SwitzerlandDepartment of Biomedical Engineering, University of Basel, Basel, SwitzerlandDepartment of Biomedical Engineering, University of Basel, Basel, SwitzerlandUsing a laser for cutting bones instead of the traditional saws improves a patient’s healing process. Additionally, the laser has the potential to reduce the collateral damage to the surrounding tissue if appropriately applied. This can be achieved by building additional sensing elements besides the laser itself into an endoscope. To this end, we use a microsecond pulsed Erbium-doped Yttrium Aluminium Garnet (Er:YAG) laser to cut bones. During ablation, each pulse emits an acoustic shock wave that is captured by an air-coupled transducer. In our research, we use the data from these acoustic waves to predict the depth of the cut during the ablation process. We use a Neural Network (NN) to estimate the depth, where we use one or multiple consecutive measurements of acoustic waves. The NN outperforms the base-line method that assumes a constant ablation rate with each pulse to predict the depth. The results are evaluated and compared against the ground-truth depth measurements from Optical Coherence Tomography (OCT) images that measure the depth in real-time during the ablation process.https://ieeexplore.ieee.org/document/9966604/Acoustic feedbackdepth controllaser ablationneural network |
| spellingShingle | Carlo Seppi Antal Huck Arsham Hamidi Eva Schnider Massimiliano Filipozzi Georg Rauter Azhar Zam Philippe C. Cattin Bone Ablation Depth Estimation From Er:YAG Laser-Generated Acoustic Waves IEEE Access Acoustic feedback depth control laser ablation neural network |
| title | Bone Ablation Depth Estimation From Er:YAG Laser-Generated Acoustic Waves |
| title_full | Bone Ablation Depth Estimation From Er:YAG Laser-Generated Acoustic Waves |
| title_fullStr | Bone Ablation Depth Estimation From Er:YAG Laser-Generated Acoustic Waves |
| title_full_unstemmed | Bone Ablation Depth Estimation From Er:YAG Laser-Generated Acoustic Waves |
| title_short | Bone Ablation Depth Estimation From Er:YAG Laser-Generated Acoustic Waves |
| title_sort | bone ablation depth estimation from er yag laser generated acoustic waves |
| topic | Acoustic feedback depth control laser ablation neural network |
| url | https://ieeexplore.ieee.org/document/9966604/ |
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