Optoacoustic Tissue Differentiation Using a Mach–Zehnder Interferometer

Kenhagho, Hervé Nguendon; Rauter, Georg; Guzman, Raphael; Cattin, Philippe C.; Zam, Azhar

doi:10.1109/tuffc.2019.2923696

Cited by 10 publications

(3 citation statements)

References 28 publications

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“…The possible reason is that most soft tissues contain 79% of water, while hard bone includes up to 85–95% of carbonated hydroxyapatite [12]. Hence, we can assume that the carbonated hydroxyapatite resulted in a higher difference in ablation thresholds and so in the ablation dynamics due to its compact structure, as compared to soft tissues [31]. Furthermore, the main mismatch between soft tissues could be attributed to some fatty tissue, which was not completely removed before the ablation experiments and so were present during the ablation process.…”

Section: Resultsmentioning

confidence: 99%

Machine learning monitoring for laser osteotomy

Shevchik

Kenhagho

Le-Quang

et al. 2021

Journal of Biophotonics

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This work proposes a new online monitoring method for an assistance during laser osteotomy. The method allows differentiating the type of ablated tissue and the applied dose of laser energy. The setup analyzes the laser‐induced acoustic emission, detected by an airborne microphone sensor. The analysis of the acoustic signals is carried out using a machine learning algorithm that is pre‐trained in a supervised manner. The efficiency of the method is experimentally evaluated with several types of tissues, which are: skin, fat, muscle, and bone. Several cutting‐edge machine learning frameworks are tested for the comparison with the resulting classification accuracy in the range of 84–99%. It is shown that the datasets for the training of the machine learning algorithms are easy to collect in real‐life conditions. In the future, this method could assist the doctors during laser osteotomy, minimizing the damage of the nearby healthy tissues and provide cleaner pathologic tissue removal.

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Section: Resultsmentioning

confidence: 99%

Machine learning monitoring for laser osteotomy

Shevchik

Kenhagho

Le-Quang

et al. 2021

Journal of Biophotonics

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“…Similar to LIBS and laser-induced shockwave measurement methods, the procedure is based on generating plasma at the local spot of the tissue. However, in our method, the laser energy applied (4.10 mJ) is much lower than that used for LIBS (38 mJ for ns-Excimer laser [32,33], 73-108 mJ for ns-Nd:YAG laser [31,[34][35][36][37][38], and 75-200 mJ for CO 2 -LIBS [41]) or shockwave measurements (200 mJ for ns-Nd:YAG [27,28], and 0.75-15 J for ms-fiber laser [29]). Also, the energy applied is well below the reported energy required to pump the tissue for random lasing (100 mJ for ns-Nd:YAG [30]), and pyrolysis analysis (300-2000 mJ for µs-Er:YAG [43,44]), which occurs inside the ablation zone in the laser-tissue interaction map.…”

Section: Advantage and Disadvantage Of The Introduced Methodsmentioning

confidence: 99%

“…In order to preserve the adjacent soft tissue, several approaches to such differentiation have been developed using the optical properties of the ablated tissues. These methods include optical coherence tomography (OCT) [12,13], Raman spectroscopy [14][15][16][17], autofluorescence spectroscopy [18,19], diffuse reflectance spectroscopy (DRS) [20][21][22][23], ablative optoacoustic techniques [24][25][26][27][28][29], random lasing [30], laser-induced breakdown spectroscopy (LIBS) [31][32][33][34][35][36][37][38][39][40][41][42], and combustion/pyrolysis light analysis [43,44]. However, many of these methods have not been tested in combination with an ablating laser; studies have focused on tissue differentiation only.…”

Section: Introductionmentioning

confidence: 99%

Combined Nd:YAG and Er:YAG lasers for real-time closed-loop tissue-specific laser osteotomy

Abbasi¹,

Bernal²,

Hamidi³

et al. 2020

Biomed. Opt. Express

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A novel real-time and non-destructive method for differentiating soft from hard tissue in laser osteotomy has been introduced and tested in a closed-loop fashion. Two laser beams were combined: a low energy frequency-doubled nanosecond Nd:YAG for detecting the type of tissue, and a high energy microsecond Er:YAG for ablating bone. The working principle is based on adjusting the energy of the Nd:YAG laser until it is low enough to create a microplasma in the hard tissue only (different energies are required to create plasma in different tissue types). Analyzing the light emitted from the generated microplasma enables real-time feedback to a shutter that prevents the Er:YAG laser from ablating the soft tissue.

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Toward optoacoustic sciatic nerve detection using an all‐fiber interferometric‐based sensor for endoscopic smart laser surgery

et al. 2021

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Objectives Laser surgery requires efficient tissue classification to reduce the probability of undesirable or unwanted tissue damage. This study aimed to investigate acoustic shock waves (ASWs) as a means of classifying sciatic nerve tissue. Materials and Methods In this study, we classified sciatic nerve tissue against other tissue types—hard bone, soft bone, fat, muscle, and skin extracted from two proximal and distal fresh porcine femurs—using the ASWs generated by a laser during ablation. A nanosecond frequency‐doubled Nd:YAG laser at 532 nm was used to create 10 craters on each tissue type's surface. We used a fiber‐coupled Fabry–Pérot sensor to measure the ASWs. The spectrum's amplitude from each ASW frequency band measured was used as input for principal component analysis (PCA). PCA was combined with an artificial neural network to classify the tissue types. A confusion matrix and receiver operating characteristic (ROC) analysis was used to calculate the accuracy of the testing‐data‐based scores from the sciatic nerve and the area under the ROC curve (AUC) with a 95% confidence‐level interval. Results Based on the confusion matrix and ROC analysis of the model's tissue classification results (leave‐one‐out cross‐validation), nerve tissue could be classified with an average accuracy rate and AUC result of 95.78 ± 1.3% and 99.58 ± 0.6%, respectively. Conclusion This study demonstrates the potential of using ASWs for remote classification of nerve and other tissue types. The technique can serve as the basis of a feedback control system to detect and preserve sciatic nerves in endoscopic laser surgery.

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Optoacoustic Tissue Differentiation Using a Mach–Zehnder Interferometer

Cited by 10 publications

References 28 publications

Machine learning monitoring for laser osteotomy

Machine learning monitoring for laser osteotomy

Combined Nd:YAG and Er:YAG lasers for real-time closed-loop tissue-specific laser osteotomy

Toward optoacoustic sciatic nerve detection using an all‐fiber interferometric‐based sensor for endoscopic smart laser surgery

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