Deep-Learning-Based Algorithm for the Removal of Electromagnetic Interference Noise in Photoacoustic Endoscopic Image Processing

Despite all the expectations for photoacoustic endoscopy (PAE), there are still several technical issues that must be resolved before the technique can be successfully translated into clinics. Among these, electromagnetic interference (EMI) noise, in addition to the limited signal-to-noise ratio (SNR), have hindered the rapid development of related technologies. Unlike endoscopic ultrasound, in which the SNR can be increased by simply applying a higher pulsing voltage, there is a fundamental limitation in leveraging the SNR of PAE signals because they are mostly determined by the optical pulse energy applied, which must be within the safety limits. Moreover, a typical PAE hardware situation requires a wide separation between the ultrasonic sensor and the amplifier, meaning that it is not easy to build an ideal PAE system that would be unaffected by EMI noise. With the intention of expediting the progress of related research, in this study, we investigated the feasibility of deep-learning-based EMI noise removal involved in PAE image processing. In particular, we selected four fully convolutional neural network architectures, U-Net, Segnet, FCN-16s, and FCN-8s, and observed that a modified U-Net architecture outperformed the other architectures in the EMI noise removal. Classical filter methods were also compared to confirm the superiority of the deep-learning-based approach. Still, it was by the U-Net architecture that we were able to successfully produce a denoised 3D vasculature map that could even depict the mesh-like capillary networks distributed in the wall of a rat colorectum. As the development of a low-cost laser diode or LED-based photoacoustic tomography (PAT) system is now emerging as one of the important topics in PAT, we expect that the presented AI strategy for the removal of EMI noise could be broadly applicable to many areas of PAT, in which the ability to apply a hardware-based prevention method is limited and thus EMI noise appears more prominently due to poor SNR

Diode Laser—Can It Replace the Electrical Current Used in Endoscopic Submucosal Dissection?

Background/Aims A new medical fiber-guided diode laser system (FDLS) is expected to offer high-precision cutting with simultaneous hemostasis. Thus, this study aimed to evaluate the feasibility of using the 1,940-nm FDLS to perform endoscopic submucosal dissection (ESD) in the gastrointestinal tract of an animal model. Methods In this prospective animal pilot study, gastric and colorectal ESD using the FDLS was performed in ex vivo and in vivo porcine models. The completeness of en bloc resection, the procedure time, intraprocedural bleeding, histological injuries to the muscularis propria (MP) layer, and perforation were assessed. Results The en bloc resection and perforation rates in the ex vivo study were 100% (10/10) and 10% (1/10), respectively; those in the in vivo study were 100% (4/4) and 0% for gastric ESD and 100% (4/4) and 25% (1/4) for rectal ESD, respectively. Deep MP layer injuries tended to occur more frequently in the rectal than in the gastric ESD cases, and no intraprocedural bleeding occurred in either group. Conclusions The 1,940-nm FDLS was capable of yielding high en bloc resection rates without intraprocedural bleeding during gastric and colorectal ESD in animal models

Role of Endoscopic Gastroplasty Techniques in the Management of Obesity

Health and wellness represent a major global concern. Trends such as a lack of exercise and excessive consumption of calories are major causes of the rapid increase in obesity worldwide. Obesity should be controlled because it can result in other illnesses, such as diabetes, high blood pressure, high cholesterol, coronary artery disease, stroke, breathing disorders, or cancer. However, many people have difficulty in managing obesity through exercise, dietary control, behavioral modifications, and drug therapy. Bariatric surgery is not commonly used due to a variety of complications, even though it has been demonstrated to produce reliable results with respect to adequate weight loss when performed using an open or a laparoscopic approach. Endoscopic bariatric procedures are emerging techniques that are less invasive and safer compared with current surgical approaches. However, the evaluation of endoluminal procedures is limited by the small number of studies and their short-term follow-up

FPGA Implementation of an Efficient FFT Processor for FMCW Radar Signal Processing

This paper presents the design and implementation results of an efficient fast Fourier transform (FFT) processor for frequency-modulated continuous wave (FMCW) radar signal processing. The proposed FFT processor is designed with a memory-based FFT architecture and supports variable lengths from 64 to 4096. Moreover, it is designed with a floating-point operator to prevent the performance degradation of fixed-point operators. FMCW radar signal processing requires windowing operations to increase the target detection rate by reducing clutter side lobes, magnitude calculation operations based on the FFT results to detect the target, and accumulation operations to improve the detection performance of the target. In addition, in some applications such as the measurement of vital signs, the phase of the FFT result has to be calculated. In general, only the FFT is implemented in the hardware, and the other FMCW radar signal processing is performed in the software. The proposed FFT processor implements not only the FFT, but also windowing, accumulation, and magnitude/phase calculations in the hardware. Therefore, compared with a processor implementing only the FFT, the proposed FFT processor uses 1.69 times the hardware resources but achieves an execution time 7.32 times shorter

VLSI Implementation of Restricted Coulomb Energy Neural Network with Improved Learning Scheme

This paper proposes a restricted coulomb energy neural network (RCE-NN) with an improved learning algorithm and presents the hardware architecture design and VLSI implementation results. The learning algorithm of the existing RCE-NN applies an inefficient radius adjustment, such as learning all neurons at the same radius or reducing the radius excessively in the learning process. Moreover, since the reliability of eliminating unnecessary neurons is estimated without considering the activation region of each neuron, it is inaccurate and leaves unnecessary neurons extant. To overcome this problem, the proposed learning algorithm divides each neuron region in the learning process and measures the reliability with different factors for each region. In addition, it applies a process of gradual radius reduction by a pre-defined reduction rate. In performance evaluations using two datasets, RCE-NN with the proposed learning algorithm showed high recognition accuracy with fewer neurons compared to existing RCE-NNs. The proposed RCE-NN processor was implemented with 197.8K logic gates in 0.535 mm 2 using a 55 nm CMOS process and operated at the clock frequency of 150 MHz