Optoacoustic Tissue Differentiation Using a Mach-Zehnder Interferometer: Preliminary Results

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

doi:10.1109/ultsym.2018.8579654

Cited by 8 publications

(8 citation statements)

References 10 publications

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“…The spectrums of the acoustic pulses with shocks can extend beyond 1 MHz [46]. Thus, precise frequency measurements of broadband acoustic signals generated during ablation, using broadband pressure sensors, will measure acoustic shock waves with higher frequency components [35,47,48], which could then be used as frequency parameters to distinguish between ablated tissues. That is why the FBG, combined with a photodiode with a bandwidth of 2.5 MHz, resulted in a better classification error than the one measured by a microphone (Table 2 and Table 3).…”

Section: Discussionmentioning

confidence: 99%

“…A tissue differentiation method could improve the safety of lasers as an osteotomy tool. This is particularly true if a laser system can be controlled by an in situ and real-time automatic feedback system that not only differentiates specific types of human tissues but additionally stops automatically when the laser encounters tissues that are not meant to be ablated [2,4,35]. Acoustic shock waves (ASWs) are generated when ablating material with a laser and can be measured using microphone and Fiber Bragg Grating (FBG) sensors [4,36,37,38].…”

Section: Introductionmentioning

confidence: 99%

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Characterization of Ablated Bone and Muscle for Long-Pulsed Laser Ablation in Dry and Wet Conditions

et al. 2019

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Smart laser technologies are desired that can accurately cut and characterize tissues, such as bone and muscle, with minimal thermal damage and fast healing. Using a long-pulsed laser with a 0.5–10 ms pulse width at a wavelength of 1.07 µm, we investigated the optimum laser parameters for producing craters with minimal thermal damage under both wet and dry conditions. In different tissues (bone and muscle), we analyzed craters of various morphologies, depths, and volumes. We used a two-way Analysis of Variance (ANOVA) test to investigate whether there are significant differences in the ablation efficiency in wet versus dry conditions at each level of the pulse energy. We found that bone and muscle tissue ablated under wet conditions produced fewer cracks and less thermal damage around the craters than under dry conditions. In contrast to muscle, the ablation efficiency of bone under wet conditions was not higher than under dry conditions. Tissue differentiation was carried out based on measured acoustic waves. A Principal Component Analysis of the measured acoustic waves and Mahalanobis distances were used to differentiate bone and muscle under wet conditions. Bone and muscle ablated in wet conditions demonstrated a classification error of less than 6.66 % and 3.33 %, when measured by a microphone and a fiber Bragg grating, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterization of Ablated Bone and Muscle for Long-Pulsed Laser Ablation in Dry and Wet Conditions

et al. 2019

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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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“…In contrast, our goal is to only use the acoustic signal for tissue classification to prevent the laser from continuing the cut when detecting tissue that should not be damaged. Similar approaches have been proposed in [11], [24], [25]. A different approach [26], [27] used acoustic waves in a 2D simulation to infer the acoustic density within a region of interest.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning Approach for Tissue Classification Using Acoustic Waves During Ablation With an Er:YAG Laser

Seppi¹,

Huck

Kenhagho

et al. 2021

IEEE Access

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We acknowledge the funding of the Werner Siemens Foundation through the MIRCALE (Minimally Invasive Robot-Assisted Computer-guided LaserosteotomE) project. ABSTRACT Today's mechanical tools for bone cutting (osteotomy) lead to mechanical trauma that prolong the healing process. Medical device manufacturers continuously strive to improve their tools to minimize such trauma. One example of such a new tool and procedure is minimally invasive surgery with laser as the cutting element. This setup allows for tissue ablation using laser light instead of mechanical tools, which reduces the post-surgery healing time. During surgery, a reliable feedback system is crucial to avoid collateral damage to the surrounding tissues. Therefore, we propose a tissue classification method that analyzes the acoustic waves produced during laser ablation and show its applicability in an ex-vivo experiment. The ablation process with a microsecond pulsed Erbium-doped Yttrium Aluminium Garnet (Er:YAG) laser produces acoustic waves that we captured with an air-coupled transducer. Consequently, we used these captured waves to classify five porcine tissue types: hard bone, soft bone, muscle, fat, and skin tissue. For automated tissue classification of the measured acoustic waves, we propose three Neural Network (NN) approaches: A Fully-connected Neural Network (FcNN), a one-dimensional Convolutional Neural Network (CNN), and a Recurrent Neural Network (RNN). The time-and the frequency-dependent parts of the measured waves' pressure variation were used as separate inputs to train and validate the designed NNs. In a final step, we used Grad-CAM to find the frequencies' activation map and conclude that the low frequencies are the most important ones for this classification task. In our experiments, we achieved an accuracy of 100 % for the five tissue types for all the proposed NNs. We tested the different classifiers for their robustness and concluded that using frequency-dependent data together with a FcNN is the most robust approach.

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Optoacoustic Tissue Differentiation Using a Mach-Zehnder Interferometer: Preliminary Results

Cited by 8 publications

References 10 publications

Characterization of Ablated Bone and Muscle for Long-Pulsed Laser Ablation in Dry and Wet Conditions

Characterization of Ablated Bone and Muscle for Long-Pulsed Laser Ablation in Dry and Wet Conditions

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

Deep-Learning Approach for Tissue Classification Using Acoustic Waves During Ablation With an Er:YAG Laser

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