Effect of pulse duration on the acoustic frequency emissions during the laser-induced breakdown of atmospheric air

Manikanta, E.; Kumar, L. Vinoth; Venkateshwarlu, P.; Leela, Ch.; Kiran, P. Prem

doi:10.1364/ao.55.000548

Cited by 26 publications

(8 citation statements)

References 37 publications

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“…As indicated in the introductory section, properties of laser radiation (pulse shape, wavelength, length, and energy) are key variables to characterize the launched acoustic waves [ 58 , 59 ]. Here, the effect of pulse energy on the acoustic frequency emissions during the laser-induced breakdown of systems was investigated.…”

Section: Resultsmentioning

confidence: 99%

Remotely Exploring Deeper-Into-Matter by Non-Contact Detection of Audible Transients Excited by Laser Radiation

Moros

Gaona

Laserna

2017

Sensors

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An acoustic spectroscopic approach to detect contents within different packaging, with substantially wider applicability than other currently available subsurface spectroscopies, is presented. A frequency-doubled Nd:YAG (neodymium-doped yttrium aluminum garnet) pulsed laser (13 ns pulse length) operated at 1 Hz was used to generate the sound field of a two-component system at a distance of 50 cm. The acoustic emission was captured using a unidirectional microphone and analyzed in the frequency domain. The focused laser pulse hitting the system, with intensity above that necessary to ablate the irradiated surface, transferred an impulsive force which led the structure to vibrate. Acoustic airborne transients were directly radiated by the vibrating elastic structure of the outer component that excited the surrounding air in contact with. However, under boundary conditions, sound field is modulated by the inner component that modified the dynamical integrity of the system. Thus, the resulting frequency spectra are useful indicators of the concealed content that influences the contributions originating from the wall of the container. High-quality acoustic spectra could be recorded from a gas (air), liquid (water), and solid (sand) placed inside opaque chemical-resistant polypropylene and stainless steel sample containers. Discussion about effects of laser excitation energy and sampling position on the acoustic emission events is reported. Acoustic spectroscopy may complement the other subsurface alternative spectroscopies, severely limited by their inherent optical requirements for numerous detection scenarios.

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Section: Resultsmentioning

confidence: 99%

Remotely Exploring Deeper-Into-Matter by Non-Contact Detection of Audible Transients Excited by Laser Radiation

Moros

Gaona

Laserna

2017

Sensors

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“…As already mentioned, the Er:YAG laser at 2940 nm is generally used for deep ablation accompanied by a water‐cooling system, 15 while, the Nd:YAG laser at 532 nm can be used to ablate surfaces in a wet environment. 20 , 25 , 43 Depending on the laser pulse parameters, such as laser energy, pulse duration, and focusing conditions, 43 the ASW properties can be slightly different. 20 Therefore, for more application, investigating tissue classification using the Er:YAG at 2940 nm combined with the Nd:YAG laser at 532 nm in vivo is an essential next step.…”

Section: Discussionmentioning

confidence: 99%

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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“…The higher values were theoretically expected, as the ablation with the Nd:YAG is based on plasma mediation, which increases the pressure energy measured by the transducer. The Er:YAG laser source results in thermal ablation, thus, most of the light energy is absorbed by the exposed tissue and transformed into heat [12,20,56]. This is likely also the reason the acoustic signal duration generated by the Nd:YAG laser was longer as compared to the one generated by the Er:YAG laser ( Fig.…”

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Machine Learning‐Based Optoacoustic Tissue Classification Method for Laser Osteotomes Using an Air‐Coupled Transducer

et al. 2020

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Background and Objectives: Using lasers instead of mechanical tools for bone cutting holds many advantages, including functional cuts, contactless interaction, and faster wound healing. To fully exploit the benefits of lasers over conventional mechanical tools, a real-time feedback to classify tissue is proposed. Study Design/Materials and Methods: In this paper, we simultaneously classified five tissue types-hard and soft bone, muscle, fat, and skin from five proximal and distal fresh porcine femurs-based on the laser-induced acoustic shock waves (ASWs) generated. For laser ablation, a nanosecond frequency-doubled Nd:YAG laser source at 532 nm and a microsecond Er:YAG laser source at 2940 nm were used to create 10 craters on the surface of each proximal and distal femur. Depending on the application, the Nd:YAG or Er:YAG can be used for bone cutting. For ASW recording, an air-coupled transducer was placed 5 cm away from the ablated spot. For tissue classification, we analyzed the measured acoustics by looking at the amplitude-frequency band of 0.11-0.27 and 0.27-0.53 MHz, which provided the least average classification error for Er:YAG and Nd:YAG, respectively. For data reduction, we used the amplitude-frequency band as an input of the principal component analysis (PCA). On the basis of PCA scores, we compared the performance of the artificial neural network (ANN), the quadratic-and Gaussian-support vector machine (SVM) to classify tissue types. A set of 14,400 data points, measured from 10 craters in four proximal and distal femurs, was used as training data, while a set of 3,600 data points from 10 craters in the remaining proximal and distal femur was considered as testing data, for each laser. Results: The ANN performed best for both lasers, with an average classification error for all tissues of 5.01 ± 5.06% and 9.12 ± 3.39%, using the Nd:YAG and Er:YAG lasers, respectively. Then, the Gaussian-SVM performed better than the quadratic SVM during the cutting with both lasers. The Gaussian-SVM yielded average classification errors of 15.17 ± 13.12% and 16.85 ± 7.59%, using the Nd:YAG and Er:YAG lasers, respectively. The worst performance was achieved with the quadratic-SVM with a classification error of 50.34 ± 35.04% and 69.96 ± 25.49%, using the Nd:YAG and Er:YAG lasers. Conclusion: We foresee using the ANN to differentiate tissues in real-time during laser osteotomy. Lasers Surg. Med.

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Effect of pulse duration on the acoustic frequency emissions during the laser-induced breakdown of atmospheric air

Cited by 26 publications

References 37 publications

Remotely Exploring Deeper-Into-Matter by Non-Contact Detection of Audible Transients Excited by Laser Radiation

Remotely Exploring Deeper-Into-Matter by Non-Contact Detection of Audible Transients Excited by Laser Radiation

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

Machine Learning‐Based Optoacoustic Tissue Classification Method for Laser Osteotomes Using an Air‐Coupled Transducer

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