Reverse Scan Conversion and Efficient Deep Learning Network Architecture for Ultrasound Imaging on a Mobile Device

Point-of-care ultrasound (POCUS), realized by recent developments in portable ultrasound imaging systems for prompt diagnosis and treatment, has become a major tool in accidents or emergencies. Concomitantly, the number of untrained/unskilled staff not familiar with the operation of the ultrasound system for diagnosis is increasing. By providing an imaging guide to assist clinical decisions and support diagnosis, the risk brought by inexperienced users can be managed. Recently, deep learning has been employed to guide users in ultrasound scanning and diagnosis. However, in a cloud-based ultrasonic artificial intelligence system, the use of POCUS is limited due to information security, network integrity, and significant energy consumption. To address this, we propose (1) a structure that simultaneously provides ultrasound imaging and a mobile device-based ultrasound image guide using deep learning, and (2) a reverse scan conversion (RSC) method for building an ultrasound training dataset to increase the accuracy of the deep learning model. Experimental results show that the proposed structure can achieve ultrasound imaging and deep learning simultaneously at a maximum rate of 42.9 frames per second, and that the RSC method improves the image classification accuracy by more than 3%.

Medical Image Synthesis using Computer Vision Techniques

Magnetic Resonance Imaging (MRI) is a type of scan that produces comprehensive images of the inside of the body using a steady magnetic field and radio waves. On the other hand, Computed Tomography (CT) scans, is a combination of a series of X-ray images, which are a type of radiation called ionizing radiation. It can be harmful to the DNA in your cells and also increase the chances that they'll turn cancerous. MRI is a safer option compared to CT and does not involve any radiation exposure. In this paper, we propose the use of Generative Adversarial Networks (GANs) to translate MRI images into equivalent CT images. We compare it with past techniques of MRI to CT scan conversion and elaborate on why GANs produce more realistic CT images while modeling the nonlinear relationship from MRI to CT.

Hardware/software co-design for a parallel three-dimensional bresenham’s algorithm

<p>Line plotting is the one of the basic operations in the scan conversion. Bresenham’s line drawing algorithm is an efficient and high popular algorithm utilized for this purpose. This algorithm starts from one end-point of the line to the other end-point by calculating one point at each step. As a result, the calculation time for all the points depends on the length of the line thereby the number of the total points presented. In this paper, we developed an approach to speed up the Bresenham algorithm by partitioning each line into number of segments, find the points belong to those segments and drawing them simultaneously to formulate the main line. As a result, the higher number of segments generated, the faster the points are calculated. By employing 32 cores in the Field Programmable Gate Array, a line of length 992 points is formulated in 0.31μs only. The complete system is implemented using Zybo board that contains the Xilinx Zynq-7000 chip (Z-7010).<em></em></p>