Dual-mode photoacoustic and ultrasound imaging system based on a Fabry-Pérot scanner

The planar Fabry-Pérot (FP) scanner is an ultrasound detector that simultaneously provides high sensitivity, a high density of small (sub-100 μm) acoustic elements, and a broad bandwidth (> 30 MHz). These features enable the FP scanner to acquire high-resolution 3D in vivo photoacoustic images of biological tissues up to depths of approximately 10 mm. The aim was to add complementary morphological ultrasound contrast to photoacoustic images to extend their clinical applicability. This was achieved by developing a dual-mode photoacoustic and ultrasound imaging system based on the FP scanner, which was modified to transmit optically generated ultrasound. The FP sensor head was coated with an optically absorbing polydimethylsiloxane(PDMS) composite layer, which was excited with nanosecond laser pulses to generate broadband planar ultrasound waves for pulse-echo imaging. First, an all-optical ultrasound system was developed using a highly absorbing carbon nanotube-PDMS composite coating. The system was characterised with a series of experiments, and its imaging performance was tested on tissue mimicking phantoms and ex vivo tissue samples. Second, the effect of the frequency content of the detected signals and the effect of spatial aliasing on the image quality were investigated in simulation. A broadband system was found to reduce the effect of spatial undersampling of high frequencies which results in a reduction of contrast due to the formation of grating lobe artefacts. Third, to improve the image quality, frequency and angle compounding were explored in simulations and experimentally. Coherent and incoherent compounding were considered, as well as the effect of the filter bandwidth on frequency compounded images, and the influence of the number and spread of angles used in angle compounded images. Finally, a dual- mode photoacoustic and ultrasound imaging system was demonstrated with a gold nanoparticle-PDMS composite which enabled wavelength-selective absorption of light. The system was shown to obtain high-resolution 3D dual-mode images providing complementary contrast from optically absorbing and acoustically scattering structures

The Accuracy of Endodontic Length Measurement Using Cone-beam Computed Tomography in Comparison with Electronic Apex Locators

Introduction: The aim of this in vitro study was to evaluate the accuracy of cone-beam computed tomography (CBCT) and two electronic apex locators (EALs) when measuring the actual length of root canals. Methods and Materials: One hundred and eighty four root canals in 135 extracted anterior and posterior permanent teeth were studied. Root canal curvatures were analyzed on CBCT images, and root canals with curvatures less than 70º were chosen. Root canal length measurements were performed using CBCT, ProPex Pixi, E-Pex Pro, and the actual length (AL). The percentages of the measurements in the range of ±0.5 mm to the AL were compared using Fisher’s Exact test. The ICC indices and Bland-Altman plots were used to display the agreement of three devices with the AL measurements. The statistical significance was set at P<0.05. Results: The accuracies of E-Pex Pro and ProPex Pixi (87.5% and 82.6%, respectively) were better than that of CBCT (71.7%) (P<0.05). Conclusion: This in vitro study showed that although the accuracies of the two EALs were at high level, there was no device that had an agreement with the actual root canal length measurementKeywords:Cone-beam Computed Tomography; Electronic Apex Locator; Endodontics; Root Canal Length; Root Canal Therapy 

Robust-MBFD: A Robust Deep Learning System for Motor Bearing Faults Detection Using Multiple Deep Learning Training Strategies and A Novel Double Loss Function

This paper presents a comprehensive analysis of motor bearing fault detection (MBFD), which involves the task of identifying faults in a motor bearing based on its vibration. To this end, we first propose and evaluate various machine learning based systems for the MBFD task. Furthermore, we propose three deep learning based systems for the MBFD task, each of which explores one of the following training strategies: supervised learning, semi-supervised learning, and unsupervised learning. The proposed machine learning based systems and deep learning based systems are evaluated, compared, and then they are used to identify the best model for the MBFD task. We conducted extensive experiments on various benchmark datasets of motor bearing faults, including those from the American Society for Mechanical Failure Prevention Technology (MFPT), Case Western Reserve University Bearing Center (CWRU), and the Condition Monitoring of Bearing Damage in Electromechanical Drive Systems from Paderborn University (PU). The experimental results on different datasets highlight two main contributions of this study. First, we prove that deep learning based systems are more effective than machine learning based systems for the MBFD task. Second, we achieve a robust and general deep learning based system with a novel loss function for the MBFD task on several benchmark datasets, demonstrating its potential for real-life MBFD applications