Xing Huang

Bio: Xing Huang is an academic researcher from Southwest Petroleum University. The author has contributed to research in topics: Path tracing & Ray tracing (graphics). The author has an hindex of 1, co-authored 3 publications receiving 7 citations.

A Real-Time Medical Ultrasound Simulator Based on a Generative Adversarial Network Model

Abstract: This paper presents an artificial intelligence-based ultrasound simulator suitable for medical simulation and clinical training. Particularly, we propose a machine learning approach to realistically simulate ultrasound images based on generative adversarial networks (GANs). Using B-mode ultrasound images simulated by a known ultrasound simulator, Field II, an "image-to-image" ultrasound simulator was trained. Then, through evaluations, we found that the GAN-based simulator can generate B-mode images following Rayleigh scattering. Our preliminary study demonstrated that ultrasound B-mode images from anatomies inferred from magnetic resonance imaging (MRI) data were feasible. While some image blurring was observed, ultrasound B- mode images obtained were both visually and quantitatively comparable to those obtained using the Field II simulator. It is also important to note that the GAN-based ultrasound simulator was computationally efficient and could achieve a frame rate of 15 frames/second using a regular laptop computer. In the future, the proposed GAN-based simulator will be used to synthesize more realistic looking ultrasound images with artifacts such as shadowing.

A Real-time Ultrasound Simulator Using Monte-Carlo Path Tracing in Conjunction with Optix Engine

Abstract: Monte-Carlo ray tracing, which enables realistic simulation of ultrasound-tissue interactions such as soft shadows and fuzzy reflections, has been used to simulate ultrasound images. The main technical challenge presented with Monte-Carlo ray tracing is its computational efficiency. In this study, we investigated the use of a commercially-available ray-tracing engine (NVIDIA’s Optix 6.0), which provides a simple, recursive, and flexible pipeline for accelerating ray tracing algorithms. Our preliminary results show that our ultrasound simulation algorithm accelerated by the Optix engine can achieve a frame of 25 frames/second using an Nvidia RTX 2060 card. Furthermore, we compare ultrasound simulations built on the proposed Monte-Carlo ray-tracing algorithm with a deep-learning generative adversarial network (GANs)-based ultrasound simulator and a physics-based ultrasound simulator (Field II). The proposed ultrasound simulator was able to better visualize small-sized structures while the other two above-mentioned simulators could not. Our future work includes integration of our proposed simulator with a virtual reality platform and expansion to other ultrasound modalities such as elastography and flow imaging.

Enhanced Built-In Self-Diagnosis and Self-Repair Techniques for Daisy-Chain Design in MEDA Digital Microfluidic Biochips

Abstract: Digital microfluidic biochips have emerged as a promising alternative for various laboratory procedures in biochemistry such as drug discovery and DNA sequencing. A recent generation of digital biochips uses a micro-electrode-dotarray (MEDA) architecture, which provides finer controllability of droplets and seamlessly integrates microelectronics and microfluidics. To simplify the wiring design of such biochips, all microelectrodes and their control registers are daisy-chained together. Therefore, the ability to both identify faults in the chain and tolerate them is required in MEDA biochips. In this study, a new daisy-chain design approach is proposed, integrating a built-in self-repair scheme that can automatically detect faults and correct them. Moreover, an efficient test generation method that requires only a small number of test vectors is proposed to achieve 100% fault coverage without degrading the electrodes. The proposed self-repair scheme can be used in both offline and online modes. Experimental results show that detection and repair can be carried out for various types of faults that can occur in daisy chains.

Ultrasonic image simulating method based on generative adversarial network

Abstract: The invention discloses an ultrasonic image simulating method based on a generative adversarial network. The method comprises the following steps of firstly, obtaining a slice in a CT image or an MR image, and segmenting different areas of the image according to the morphological characteristic of the image after the slice is obtained; then distributing different reflection coefficient values to different areas; training a model through a large number of k-Wave simulated ultrasonic images, and after model convergence, performing real-time simulation of the ultrasonic image by means of the trained model. The ultrasonic image simulating method can finish ultrasonic image simulation in real time. Compared with a traditional convolutional method, the ultrasonic image simulating method has advantages of higher vividness of the obtained image, and high clinical application effect.

Advanced Machine Learning-Based Analytics on COVID-19 Data Using Generative Adversarial Networks.

Abstract: The domain of medical diagnosis and predictive analytics is one of the key domains of research with enormous dimensions whereby the diseases of different types can be predicted. Nowadays, there is a huge panic of impact and rapid mutation of the COVID-19 virus impression. The world is getting affected by this virus to a huge extent and there is no vaccine developed so far. India is also having more than 10,000 patients with than 300 deceased. The global human community is having around 20 lacs of Coronavirus patients. The Generative Adversarial Network (GAN) is the contemporary high-performance approach in which the use of advanced neural networks is done for the cavernous analytics of the images and multimedia data. In this research work, the analytics of key points from medical images of the COVID-19 dataset is to be presented using which the diagnosis and predictions can be done for the patients. The GANs are used for the generation, transformation as well as presentation of the dataset and key points using advanced deep learning models which can analyze the patterns in the medical images including X-Ray, CT Scan, and many others. Using such approaches with the integration of GANs, the overall predictive analytics can be made high performance aware as compared to the classical neural networks with multiple layers. In this research manuscript, the inscription of work is projected on the benchmark datasets with the advanced scripting so that the predictive mining and knowledge discovery can be done effectively with more accuracy.

An Application of Super-Resolution Generative Adversary Networks for Quasi-Static Ultrasound Strain Elastography: A Feasibility Study

Abstract: In this work, a super-resolution approach based on generative adversary network (GAN) was used to interpolate (up-sample) ultrasound radio-frequency (RF) echo data along the lateral (perpendicular to the acoustic beam direction) direction before motion estimation. Our primary objective was to investigate the feasibility of using a GAN-based super-solution approach to improve lateral resolution in the RF data as a means of improving strain image quality in quasi-static ultrasound strain elastography (QUSE). Unlike natural scene photographs, axial (parallel to the acoustic beam direction) resolution is significantly higher than that of lateral resolution in ultrasound RF data. To better handle RF data, we first modified a super-resolution generative adversary network (SRGAN) model developed by the computer vision community. We named the modified SRGAN model as super-resolution radio-frequency neural network (SRRFNN) model. Our preliminary experiments showed that, compared with axial strain elastograms obtained using the original ultrasound RF data, axial strain elastograms using ultrasound RF data up-sampled by the proposed SRRFNN model were improved. Based on the Wilcoxon rank-sum tests, such improvements were statistically significant ( $p ) for large deformation (3-5%). Also, the proposed SRRFNN model outperformed a commonly-used method ( i.e. bi-cubic interpolation used in MATLAB [Mathworks Inc., MA, USA]) in terms of improving axial strain elastograms. We concluded that applying the proposed (SRRFNN) model was feasible and good-quality strain elastography data could be obtained in in vivo tumor-bearing breast ultrasound data.

Deep learning in ultrasound elastography imaging: A review.

Abstract: It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi-static elastography or natural organ pulsation elastography, or by tracing a propagated shear wave induced by a source or a natural vibration using dynamic elastography. In recent years, deep learning has begun to emerge in ultrasound elastography research. In this review, several common deep learning frameworks in the computer vision community, such as multilayer perceptron, convolutional neural network, and recurrent neural network are described. Then, recent advances in ultrasound elastography using such deep learning techniques are revisited in terms of algorithm development and clinical diagnosis. Finally, the current challenges and future developments of deep learning in ultrasound elastography are prospected. This article is protected by copyright. All rights reserved.

Ultrasound Image Classification Using ACGAN with Small Training Dataset

Abstract: B-mode ultrasound imaging is a popular medical imaging technique. Like other image processing tasks, deep learning has been used for analysis of B-mode ultrasound images in the last few years. However, training deep learning models require large labeled datasets, which is often unavailable for ultrasound images. The lack of large labeled data is a bottleneck for the use of deep learning in ultrasound image analysis. To overcome this challenge, in this work, we exploit auxiliary classifier generative adversarial network (ACGAN) that combines the benefits of data augmentation and transfer learning in the same framework. We conduct experiment on a dataset of breast ultrasound images that shows the effectiveness of the proposed approach.

Generative Adversarial Networks for DNA Storage Channel Simulator

Abstract: DNA data storage systems have rapidly developed with novel error-correcting techniques, random access algorithms, and query systems. However, designing an algorithm for DNA storage systems is challenging, mainly due to the unpredictable nature of errors and the extremely high price of experiments. Thus, a simulator is of interest that can imitate the error statistics of a DNA storage system and replace the experiments in developing processes. We introduce novel generative adversarial networks that learn DNA storage channel statistics. Our simulator takes oligos (DNA sequences to write) as an input and generates a FASTQ file that includes output DNA reads and quality scores as if the oligos are synthesized and sequenced. We trained the proposed simulator with data from a single experiment consisting of 14,400 input oligo strands and 12,108,573 output reads. The error statistics between the input and the output of the trained generator match the actual error statistics, including the error rate at each position, the number of errors for each nucleotide, and high-order statistics. The code is available at https://github.com/gyfbianhuanyun/DNA_storage_simulator_GAN.