共查询到20条相似文献，搜索用时 15 毫秒
1.
Background
Neutrosophic based methods are becoming very popular in denoising of images due to the capability of handling indeterminacy. The main goal of denoising is to maintain balance between edge preservation and speckle reduction.Methods
To achieve this, neutrosophic based total variation method using Nakagami statistics have been explored to develop an efficient speckle reduction method. The proposed Neutrosophic based Nakagami Total Variation (NNTV) method initially transforms the image into the neutrosophic domain and then employs the neutrosophic filtering process for speckle reduction. The NNTV quantifies the indeterminacy of image by determining the entropy of indeterminate set.Results
The performance of the proposed method has been evaluated quantitatively by quality metrics on synthetic images, qualitatively using real thyroid ultrasound images through visual examination by medical experts and by Mean Opinion Score.Conclusion
From results, it has been observed that NNTV method performed better than other speckle reduction methods in terms of both speckle suppression and edge preservation. 相似文献2.
Background
Stem cell therapy has a huge potential to enhance the recovery of damaged tissues and organs. However, it has been reported that majority of implanted stem cells cannot survive after implantation. Therefore, noninvasive monitoring of stem cell viability is essential to estimate the efficacy of stem cell therapy. However, current imaging methods have disadvantages for monitoring of stem cell viability such as cost, penetration depth, and safety. To overcome the limitations, photoacoustic imaging well known for sufficient penetration depth, relatively low cost, and non-ionizing radiation can be a novel alternative assessment method of stem cell viability.Methods
In this study, indocyanine green was used as exogenous photoacoustic contrast agents to label mesenchymal stem cells. The photoacoustic signals were acquired before and after the cell death and quantified to monitor photoacoustic signal changes related to the cell viability.Results
The fluorescence intensity changes of ICG labeled MSCs corresponded to decrease of PA intensity after cell death. Furthermore, the PA imaging of MSCs showed similarity between the PA intensity and the cell viability.Conclusion
The experimental results imply the feasibility of noninvasive detection of stem cell viability during therapeutic procedures. 相似文献3.
Background
The contrast enhancement of Magnetic Resonance Imaging (MRI) data is quite challenging as the noise present in this data also get amplified in this process. Dynamic Stochastic Resonance (DSR) is the technique that utilizes the noise to enhance the contrast of MRI data.Method
The present study proposes the cascaded stochastic resonance, which exploits the properties of modified potential neuron model and quartic bistable model of DSR. The Multi-objective Particle Swarm Optimization (MOPSO) tunes the DSR parameters associated with the cascading of both the models. The MOPSO produces a set of the solution called Pareto front for the maximization of two image quality measures, i.e., contrast enhancement factor and universal image quality index. Further, the maximization of another image quality measure, i.e., anisotropy helps to obtain the optimum enhanced image from the Pareto fronts solution.Results
The present study included the simulated and real MRI data. The results show that the proposed method obtained mean contrast enhancement factor, universal image quality index and anisotropy equal to 1.79, 0.78 and 0.021 respectively. These values are better than those obtained for classical bistable DSR and other conventional contrast enhancement techniques. The proposed algorithm has been tested on real MRI data as well and found valuable in the diagnosis of lacunar infarct and mesial temporal sclerosis.Conclusion
The cascaded DSR based on MOPSO has shown promising results and may be highly beneficial to the diagnosis of different brain pathology. 相似文献4.
Background
Routine ergonomic assessment of postures and gestures in the workplace are mostly conducted by visual observations, either direct or based on video recordings. Nowadays, low-cost three-dimensional cameras like Microsoft Kinect offers the possibility of recording the full kinematics of workers in a non-intrusive way, providing a more precise, and reliable assessment of their motor strategies.Methods
We have developed a tracking application using the Kinect SDK for Windows in C?, allowing the simultaneous recording of the three-dimensional coordinates of all the body points tracked by the Microsoft Kinect at a sampling frequency of 30 Hz and an expected accuracy of 3 cm. Measurements are performed on violinists, whose playing is representative of a work situation involving repeated gestures and postures that can be described as non-ergonomic.Results
Microsoft Kinect can be efficiently used to quantify the motion performed by the violinists. Playing strategies can even be noticed despite the low-cost nature of the sensor used.Conclusion
Low-cost three-dimensional cameras can be a useful aid in ergonomic risk assessment of developing musculoskeletal disorders and give the example of the repetition of movements and postural items included in the OCRA checklist, whose scoring can be facilitated by such a device. 相似文献5.
C. Dhaenens J. Jacques V. Vandewalle M. Vandromme E. Chazard C. Preda A. Amarioarei P. Chaiwuttisak C. Cozma G. Ficheur M.-E. Kessaci R. Perichon J. Taillard R. Bordet A. Lansiaux L. Jourdan D. Delerue A. Hansske 《IRBM》2018,39(2):83-92
Context
A better understanding of “patient pathway” thanks to data analysis can lead to better treatments for patients. The ClinMine project, supported by the French National Research Agency (ANR), aims at proposing, from various case studies, algorithmic and statistical models able to handle this type of pathway data, focusing primarily on hospital data.Methods
This article presents two of these case studies, focusing on the integration of temporal data within analysis. First, the hypothesis that some aspects of the patient pathway can be described, even predicted, from the management process of the hospital medical mail is studied. Therefore a specific functional data analysis is driven, and several types of patients have been detected. The second case study deals with the detection of profiles through a biclustering of the patients. The difficulty to simultaneously deal with heterogeneous data, including temporal data is exposed and a method is proposed.Results
Experiments are driven on real data coming from a hospital. Results on these data show the effectiveness of the two proposed methods.Conclusion
The project ClinMine aimed at dealing with hospital data in order to provide a better understanding of “patient pathway”. The two methods proposed here show their ability to simultaneously deal with heterogeneous data, including temporal aspects, and manages to give information for the understanding of “patient pathway” (identification of interesting clusters of patients). 相似文献6.
O. Gsib M. Deneufchatel M. Goczkowski M. Trouillas M. Resche-Guigon S. Bencherif O. Fichet J.-J. Lataillade V. Larreta-Garde C. Egles 《IRBM》2018,39(2):103-108
Context
The FibriDerm project aims at the development and usage of fibrin-based biomaterials, with mechanical properties adapted to new applications.Methods
These materials are elaborated from interpenetrating polymer networks in which a fibrin-based gel, obtained through enzymatic hydrolysis of fibrinogen, is associated with a synthetic polymeric network, synthesized by photochemistry. These materials are self-supported and not retractable, properties which open new fields of application for these biomaterials as mechanical support for cellular growth, and particularly relevant for tissue regeneration.Results
The main goal of this project is to optimize already elaborated biomaterials to create Human Dermal Equivalents (HDE) solely made of cells and proteins from human origin. An intermediate material, capable of being colonized by surrounding cells and biodegradable in the long-term, will be first developed.Conclusion
The FibriDerm project has the ambition to lead to the development of new materials for tissue regeneration, from the initial research developments and optimizations up to pre-clinical stages, via an interdisciplinary approach. 相似文献7.
8.
Purpose
Fat accumulation and iron overload are important cofactors in chronic liver disease. Clinical quantifications of fat fraction and iron are currently assessed using MRI protocols. The purpose is to improve these measurements to simultaneously provide iron and fat maps from a single acquisition.Methods
Ten healthy volunteers and ten patients with steatosis underwent MRI for fat fraction (FF: IDEAL-IQ®), iron overload concentration (IOC: Gandon, Starmap®) and viscoelastic characterization (MR-Touch®). IDEAL-IQ® data, the clinical FF reference, were compared to the advanced Gandon protocol, post-treated with a 3pt Dixon method. The originality was to use IDEAL-IQ® fat sequence to quantify iron volumetrically using the Wood equation. To validate the iron data, the reference Gandon protocol was applied and improved to provide map of IOC. Then, IOC data were also compared to another clinical sequence (Starmap®) which was also improved (scale, number of ROI). The estimated error associated with each method was evaluated with the coefficient of variation.Results
IDEAL-IQ® and Gandon protocols were modified to provide simultaneously FF and IOC maps (2D, volume). Healthy FF were in the same range with all protocols (≈3%). For patients with steatosis, Gandon protocols underestimated the FF value (≈7%) compared to IDEAL-IQ®. Healthy and fibrosis patients were correctly diagnosed (no hemochromatosis) with all the protocols and viscoelastic properties were in the same range.Conclusion
Manufacturer's tools were improved to simultaneously quantify liver markers saving time for the patient and the clinical setting. These parameters are of great value for clinical diagnostics and novel therapeutics to treat liver diseases. 相似文献9.
Background
Polycaprolactone (PCL) is a biodegradable polymer which is used in tissue engineering applications thanks to its many favorable characteristics. However, PCL surfaces are known as hydrophobic leading to a lack of favorable cell response. To overcome this problem, PCL surfaces will undergo a surface functionalization by grafting bioactive polymers bearing ionic groups.Objective
Our laboratory has demonstrated that the grafting of bioactive polymers onto biomaterials can improve cell and antibacterial response. The objective of this work is to functionalize PCL surfaces by the grafting of a bioactive polymer.Methods
The grafting of an ionic polymer poly(sodium styrene sulfonate) (polyNaSS), using UV irradiation on PCL surfaces was carried out in a two-steps reaction process. PCL surfaces were (1) chemically oxidized in order to allow the formation of (hydro)peroxide species. (2) Then immersed in a sodium styrene sulfonate (NaSS) solution and placed under UV irradiation to induce the decomposition of (hydro)peroxides to form radicals able to initiate the polymerization of the NaSS monomer. Various parameters, such as polymerization time, the effect of the surface activation, lamp power and monomer concentration were investigated in order to optimize the yield of polyNaSS grafting. The amount of polyNaSS grafted onto PCL surfaces was first determined by toluidine blue colorimetric method and characterized by contact angle measurement, Fourier-transform infrared spectra recorded in attenuated total reflection mode (ATR-FTIR), scanning electron microscopy with Oxford energy dispersive spectroscopy (SEM-EDS).Results
Various techniques showed that the grafting of ionic polymer polyNaSS bearing sulfonate groups was successful by using radicals from (hydro)peroxides able to initiate the radical polymerization of ionic monomers onto PCL surfaces.Conclusion
We developed a new approach of radical grafting which allows us to successfully graft bioactive polymer polyNaSS covalently to PCL surfaces using UV irradiation. 相似文献10.
Background
Aiming for autonomous living for the people after a stroke is the challenge these days especially for swallowing disorders or dysphagia. The most common cause of dysphagia is stroke. In France, stroke occurs every 4 minutes, which implies 13000 hospitalizations per year. Currently, continuous medical home monitoring of patients is not available. The patient must be hospitalized or visit the medical community for possible follow-up. It is in this context that E-SwallHome (Swallowing & Breathing: Modelling and e-Health at Home) project proposes to develop tools, from hospital care until the patient returns home, which are able to monitor in real time the process of swallowing.Method
This paper presents a relevant health problem affecting patient recovering from stroke. We propose a frequency acoustical analysis for automatic detection of swallowing process and a non-invasive acoustic based method to differentiate between swallowing sounds and other sounds in normal ambient environment during food intake.Result
The proposal algorithm for events detection gives a global rate of good detection of 87.31%. Classification of sounds of swallowing and other sounds based on Gaussian Mixture Models (GMM), using the leave-one-out approach according to the small amount of data in our database, gives a good recognition rate of swallowing sounds of 84.57%.Conclusion
The proposal method has great potential to assist in the clinical evaluation using only swallowing sounds, which is a non-invasive technic for swallowing studies. 相似文献11.
Background
Epileptic seizures are unpredictable in nature and its quick detection is important for immediate treatment of patients. In last few decades researchers have proposed different algorithms for onset and offset detection of seizure using Electroencephalogram (EEG) signals.Methods
In this paper, a combined approach for onset and offset detection is proposed using Triadic wavelet decomposition based features. Standard deviation, variance and higher order moments, extracted as significant features to represent different EEG activities.Classification between seizure and non-seizure EEG was carried out using linear discriminant analysis (LDA) and k-nearest neighbour (KNN) classifiers. The method was tested using two benchmark EEG datasets in the field of seizure detection.CHBMIT EEG dataset was used for evaluating the performance of proposed seizure onset and offset detection method.Further for testing the robustness of the algorithm, the effect of the signal-to-noise ratio on the detection accuracy has been also investigated using Bonn University EEG dataset.Results
The seizure onset and offset detection method yielded classification accuracy, specificity and sensitivity of 99.45%, 99.62% and 98.36% respectively with 6.3 s onset and ?1.17 s offset latency using KNN classifier.The seizure detection method using Bonn University EEG dataset got classification accuracy of 92% when SNR = 5 dB, 94% when SNR = 10 dB, and 96% when SNR = 20 dB, while it also yielded 96% accuracy for noiseless EEG.Conclusion
The present study focuses on detection of seizure onset and offset rather than only seizure detection. The major contribution of this work is that the novel triadic wavelet transform based method is developed for the analysis of EEG signals. The results show improvement over other existing dyadic wavelet based Triadic techniques. 相似文献12.
Background
3D reconstruction of the coronary arteries can provide more information in the interventional surgery. Motion compensation is one kind of the 3D reconstruction method.Methods
We propose a novel and complete 2D motion compensated reconstruction method. The main components include initial reconstruction, forward projection, registration and compensated reconstruction. We apply the mutual information (MI) and rigidity penalty (RP) as registration measure. The advanced adaptive stochastic gradient descent (ASGD) is adopted to optimize this cost function. We generate the maximum forward projection by the simplified distance driven (SDD) projector. The compensated reconstruction adopts the MAP iterative reconstruction algorithm which is based on prior.Results
Comparing with the ECG-gating reconstruction and other reference method, the evaluation indicates that the proposed 2D motion compensation leads to a better 3D reconstruction for both the rest and stronger motion phases. The algorithm compensates the residual motion and reduces the artifact largely. As the gating window width increases, the overall image noise decreases and the contrast of the vessels improves.Conclusions
The proposed method improved the 3D reconstruction quality and reduced the artifact level. The considerable improvement in the image quality results from motion compensation increases the clinical usability of 3D coronary artery. 相似文献13.
S. Mirzaei M. El Yacoubi S. Garcia-Salicetti J. Boudy C. Kahindo V. Cristancho-Lacroix H. Kerhervé A.-S. Rigaud 《IRBM》2018,39(6):430-435
Background
The goal of this work is to develop a non-invasive method in order to help detecting Alzheimer's disease in its early stages, by implementing voice analysis techniques based on machine learning algorithms.Methods
We extract temporal and acoustical voice features (e.g. Jitter and Harmonics-to-Noise Ratio) from read speech of patients in Early Stage of Alzheimer's Disease (ES-AD), with Mild Cognitive Impairment (MCI), and from a Healthy Control (HC) group. Three classification methods are used to evaluate the efficiency of these features, namely kNN, SVM and decision Tree. To assess the effectiveness of this set of features, we compare them with two sets of feature parameters that are widely used in speech and speaker recognition applications. A two-stage feature selection process is conducted to optimize classification performance. For these experiments, the data samples of HC, ES-AD and MCI groups were collected at AP-HP Broca Hospital, in Paris.Results
First, a wrapper feature selection method for each feature set is evaluated and the relevant features for each classifier are selected. By combining, for each classifier, the features selected from each initial set, we improve the classification accuracy by a relative gain of more than 30% for all classifiers. Then the same feature selection procedure is performed anew on the combination of selected feature sets, resulting in an additional significant improvement of classification accuracy.Conclusion
The proposed method improved the classification accuracy for ES-AD, MCI and HC groups and promises the effectiveness of speech analysis and machine learning techniques to help detect pathological diseases. 相似文献14.
Background
Remote health monitoring plays a major role in handling the critical situation of patients and avoiding death and also enhancing the quality of healthcare services. The effective real time monitoring with accurate decision has to be made in advance with the help of decision making system by continuously acquiring biosignals.Objectives
The main objective was to outline the research on remote patient health monitoring system that constitutes the multimodal biosignal acquisition system, thereby providing multi-label classification and clinical decision support system (CDSS).Methods and results
A review was conducted with search terms such as multi-label classification, clinical decision support system, context-awareness and remote health monitoring. The study criteria included the randomized clinical trials evaluating the impact of efficient remote health monitoring system which incorporates CDSS for context-awareness systems by correlating several vital signs. From the total papers () which were included in the review, the major concentration of the review is multi-label classification (, 40%). Further, this article included the review in context-awareness methods (, 10%), clinical decision support systems (, 23%), different means of biosignal acquisition and pre-processing (, 10%), databases and software techniques for developing learning algorithms (, 6%) and from general category (, 12%). Several studies were effectively included which provides faster diagnosis for critically ill-patients. It is decisive for the critically ill-patients to be treated at the right time with proper and effective treatment which can be done efficiently using the CDSS and multi-label classification. The disease labels are classified as single and multi-labels where multi-label classification includes the disease labels for the correlated multiple vital signs and single label classification includes disease labels for individual vital signs. Further, on developing the logical learning model using multi-label classification, decision support system can be enhanced using context-awareness methods to predict the future vital signs, thereby providing an alert to the patients or doctors to take necessary actions.Conclusion
The proposed system includes the model that provides the correlations of several biosignals like electrocardiogram (ECG), peripheral capillary oxygen saturation (SPO2), body temperature and heartbeat, thereby identifying the critical situations and making the decisions using CDSS that helps in taking the necessary clinical interventions. 相似文献15.
Background
The electrocardiogram (ECG) signals provide important information about the heart electrical activities in medical and diagnostic applications. This signal may be contaminated by different types of noises. One of the noise types which has a considerable overlap with the ECG signals in frequency domain is electromyogram (EMG). Among the exciting approaches for de-noising the ECG signals, those based on singular spectrum analysis (SSA) are popular.Methods
In this paper, we propose a method based on SSA to separate the ECG signals from EMG noises. In general, SSA contains four steps as: embedding, singular value decomposition, grouping, and diagonal averaging. Among these steps, grouping step contains parameter (indices) which can be adjusted to achieve the desirable results. Indeed, grouping is one of the important steps of SSA as the ECG and EMG signals are separated in this step. Hence, in the proposed method, a new criterion is presented to select the indices in grouping step to separate the ECG from EMG signal with higher accuracy.Results
Performance of the proposed method is investigated using several experiments. Two sub-sets from Physionet MIT-BIH arrhythmia database are used for this purpose.Conclusion
The experimental results demonstrate effectiveness of the proposed method in comparison with other SSA-based techniques. 相似文献16.
Angel Bernal-Treviño Ana María González-Amaro Verónica Méndez González Amaury Pozos-Guillen 《Revista iberoamericana de micología》2018,35(2):78-82
Background
Microbiological identification in endodontic infections has focused mainly on bacteria without giving much attention to yeasts, which, due to their virulence factors, can affect the outcomes of root canal treatment.Aims
To determine the frequency of Candida in anaerobic conditions in root canals with primary and persistent endodontic infection, as well as to evaluate a microbiological sampling method using aspiration compared to the traditional absorption method with paper points.Methods
Fifty microbiological samples were obtained from teeth of 47 patients requiring endodontic treatments, due to either primary or persistent infections. Two microbiological sampling methods were used: an aspiration method, and the traditional paper point absorption method. In each of these methods, two types of medium were used (M1-M4). Samples were cultured under anaerobic conditions until reaching 0.5 McFarland turbidity, and then inoculated on Sabouraud dextrose, as well as on anaerobic enriched blood agar plates. Macroscopic and microscopic observations of the colonies were performed. The germ-tube test, growth on CHROMagar, and biochemical identification were performed on the isolated yeasts.Results
Fungal infection was found in 18 (36%) samples out of the 50 teeth evaluated. In the 18 samples positive for fungal infection, 15 out of 36 (41.6%) teeth were taken from a primary infection, and 3 out of 14 (21.4%) from a persistent infection. The aspiration method using Sabouraud dextrose medium recovered a greater diversity of species.Conclusions
Yeasts frequency was higher in teeth with primary infections compared to teeth with persistent infections. The predominant yeast species was Candida albicans. The aspirating sampling method was more efficient in the recovery of Candida isolates than the traditional absorption method. 相似文献17.
Background and objective
One of the important applications of non-invasive respiration monitoring using ECG signal is the detection of obstructive sleep apnea (OSA). ECG-derived respiratory (EDR) signals, contribute to useful information about apnea occurrence. In this paper, two EDR extraction methods are proposed, and their application in automatic OSA detection using single-lead ECG is investigated.Methods
EDR signals are extracted based on new respiration-related features in ECG beats morphology, such as ECG variance () and phase space reconstruction area (). After evaluating the EDRs by comparing them to a reference respiratory signal, they are used in an automatic OSA detection application. Fantasia and Apnea-ECG database from PhysioNet are used for EDRs assessments and OSA detection, respectively. The final performance of our OSA detection is tested on an independent test data which is also compared with results of other techniques in the literature.Results
The extracted EDRs, and show correlations of 72% and 70% with reference respiration, which outperform the other state-of-the-art EDR methods. After feature extraction from EDRs and RR intervals series, the combination of RR and feature sets achieved 100% accuracy in subject-based apnea detection on independent test data, and also minute-based apnea detection is done with accuracy, sensitivity and specificity of 90.9%, 89.6% and 91.8%, which is better than other automatic algorithms in the literature.Conclusions
Our OSA detection system using EDRs features yields better independent test results compared with other state-of-the-art automatic apnea detection methods. The results indicate that ECG-based OSA detection system can classify OSA events with high accuracy and suggest a promising, non-invasive and efficient method for apnea detection. 相似文献18.
Background
Pain is an unpleasant sensory and emotional experience followed by anxiety, depression, and frustration. Functional Near-Infrared Spectroscopy (fNIRS) as an optical technique identifies the brain functional networks by investigating connectivity between functionally linked of different anatomical regions in response to pain stimulation.Methods
In this research, fNIRS was performed in order to study the difference in effective functional connectivity of the brain prefrontal cortex between the two modes of pain and rest based on the dynamic causal modeling (DCM) method. Effective functional connectivity changes in the prefrontal cortex between pain and rest states were calculated using DCM approach to investigate (1) areas known for pain sensation and (2) to analyze inter-network functional connectivity strength (FCS) by selecting several brain functional networks based on the analysis findings. All analyses were performed using toolboxes SPM-fNIRS and SPM8, Matlab software.Results
Regional hemodynamics changes caused deoxyhemoglobin concentration to decrease in the prefrontal cortex of both hemispheres, particularly on the right side. We found a simultaneous increase in the concentration of oxyhemoglobin in the prefrontal cortex of the left hemisphere in comparison to the right hemisphere, that there was a trend toward reduction in oxyhemoglobin concentration. The results indicate that during the cold pain stimulation, the connectivities between prefrontal cortex regions were significantly changed. Specifically, a significantly consistent increase in the RPFC to MPFC connectivity was found while a significant consistent decrease was observed in the both MPFC to LPFC and LPFC to MPFC connectivities.Conclusion
This study contributes to the pain research field to identify the directionality and causality of neuronal connections in the prefrontal cortex by applying DCM to fNIRS data. The results suggest that the proposed method infers directional interactions between hidden neuronal states in the brain under neuronal dynamic conditions based on optical density changes measurement. 相似文献19.
C.R. Bawiec W.A. N&#x;Djin G. Bouchoux N. Sénégond N. Guillen J.-Y. Chapelon 《IRBM》2018,39(5):295-306
Background
Treatment of prostate cancer using endocavitary High Intensity Focused Ultrasound (HIFU) has become more commonplace since the first treatments in the 1990s. The gold standard HIFU strategy to treat prostate cancer is the complete thermal ablation of the entire prostate gland under real-time ultrasound (US) image guidance. A more desirable treatment and the current trend, however, is towards a focal treatment but more accurate and finely tunable thermal lesions are needed along with improved US imaging guidance. In this study, Capacitive Micromachined Ultrasound Transducer (CMUT) technology is being investigated, as they have shown recent promise for US imaging and potential to be used for HIFU therapy. They offer potential advantages over current piezoelectric designs in the context of ultrasound-guided HIFU (USgHIFU) focal therapies.Objective
The presented study evaluates the ability of a planar annular array CMUT design to achieve HIFU dynamic focusing and feasibility of generating thermal lesions in biological tissues.Method
The proposed CMUT design consists of a 64-element annular array for HIFU delivery with a space in the center that accommodates a high-resolution 256-element linear imaging array. The pressure field simulations of the HIFU portion of the array were performed using the Rayleigh integral method. The bioheat transfer equation was then used to predict lesion formation. The HIFU performances of the proposed CMUT phased-array design were compared to those of the device currently used in the clinic. Partial CMUT prototypes, including the therapeutic part only, were fabricated and experimentally characterized (electromechanical CMUT behavior, ultrasound pressure field distribution and acoustic intensity).Results
The planar 64-element annular CMUT design is capable of dynamically focusing a 3 MHz ultrasound beam at distances ranging from 32 to 72 mm, comparable in size and shape to the ones obtained with the clinical device. The simulated ultrasound fields correlated well to experimental measurements. Visual observation and impedance measurements of the CMUT cells allowed direct estimation of the collapse and snapback voltages of the ring-elements. The surface acoustic intensity of the CMUT ring-elements with both AC driving and DC bias voltages can achieve over 6 W/cm2, shown in simulation to be compatible with the generation of thermal lesions. The electro-acoustic efficiency of the CMUT elements increased with increasing DC bias voltages to reach 31%, and remained stable with increasing AC driving voltages. The ultrasound energy could be dynamically focused from this planar CMUT array during several dozen of minutes.Conclusion
This work demonstrates the feasibility of utilizing a planar CMUT probe for generating dynamic HIFU focusing and lesioning compatible with the ablation of prostate tissues under endocavitary treatment approach. Future investigations will consist of validating the lesioning capability experimentally both in vitro and in vivo. 相似文献20.