I am pleased that a paper titled “A Phase Shift and Sum Method for UWB Radar Imaging in Dispersive Media” has been published in IEEE Transactions on Microwave Theory and Techniques, in 2019, that I am a co-author on through prior work with the Celadon Research Division of Ellumen Inc.
This paper discusses applying a novel algorithm called phase shift and sum (PSAS) algorithm to reconstruct images from data collected from a fully automatic frequency and time domain measurement system for microwave imaging using a pair of movable antennas. The system described in the paper incorporates features from the Microwave Imaging Device patent where a pair of movable antennas are independently controlled to rotate around a region of interest. This paper builds upon work previously presented in 2018, in IEEE Transactions on Microwave Theory and Techniques in the paper A Time-Domain Measurement System for UWB Microwave Imaging and in 2017, in Progress In Electromagnetic Research C in the paper A novel cavity backed monopole antenna with UWB unidirectional radiation.
The PSAS algorithm resolves the multispeed and multipath issue when UWB signals propagate in dispersive media. In the PSAS method, frequency components in the UWB scattered signal are individually processed for phase shift compensation and amplitude decay compensation. The phase shift frequency responses are integrated over the spectrum, and the results are converted to a pixel value at each focal point to form an image. Using time domain signals collected from a digital phosphor oscilloscope for experimental tests, PSAS is compared to two traditional time-shift radar-based microwave imaging algorithms: delay-multiply-and-sum (DMAS) and robust artifact resistant (RAR). In the experimental tests two different objects are placed in a plastic graduated cylinder filled with glycerin. Results demonstrate superiority of PSAS over traditional time-shift methods with the lowest possibility of missing a weak scatterer and the lowest possibility of distortion of an object. I encourage you to download and read the full “A Phase Shift and Sum Method for UWB Radar Imaging in Dispersive Media” paper from IEEE for all the details of the algorithm, experimental setup, and image reconstruction results.
I was able to attend the Radiological Society of North America’s (RSNA) 104th Scientific Assembly and Annual Meeting at McCormick Place in Chicago, IL, which occurred from November 25 to November 30, 2018. The annual meeting is a very large gathering of industry leaders in medical imaging, radiologists, and other related industry professionals. This was the 104th Scientific Assembly and Annual Meeting with the tagline: Tomorrow’s Radiology today. This year brought back much emphasis on machine learning and 3D printing. As usual there were many exhibitors with new medical imaging devices ready to discuss and provide demonstrations. In particular there were a few 1st time exhibitors I was excited to see including EMTensor and Butterfly Network. There was also a U.S. market debut by United Imaging Healthcare which had a large exhibitor space. As usual there were also numerous posters and presentations.
This year brought back the popular deep learning classroom presented by the NVIDIA Deep Learning Institute (DLI) designed for attendees to engage with deep learning tools, write algorithms and improve their understanding of deep learning technology. In one session, called Introduction to Deep Learning, attendees used convolutional neural networks (CNNs) along with a MedNIST data set that consists of 1,000 images each from 5 different categories: Chest X-ray, hand X-ray, Head CT, Chest CT, Abdomen CT, and Breast MRI. The task was for the attendees to identify the image type. Another session focused on 3D segmentation of Brain MR using deep learning methods for segmentation, particularly V-nets.
This year also brought back the machine learning showcase which allowed for the opportunity to network with nearly 80 companies on the forefront of the developments in machine learning and artificial intelligence. This year introduced a new showcase called the 3D printing & advanced visualization showcase which focused on groundbreaking technology in 3D printing, virtual reality and augmented reality. Another new feature this year was a Recruiters Row which allow for attendees to connect with organizations offering career opportunities. Like last year there was also a start-up showcase that featured emerging companies bringing innovations in medical imaging.
I was able to attend a few educational courses and scientific sessions. In particular I attended a session titled Image Processing in Imaging and Radiation Therapy and another session titled Deep Learning in Radiology: How Do We Do It? In the former session I was intrigued by the talk from ImBio which trained a CNN to create quality ventricle segmentations with only 43 scans in the training dataset and used data augmentations to improve the performance on the test dataset and another talk from researchers at the University of Chicago to classify chest radiographs as anteroposterior or posteroanterior. The latter session as indicated above I attended featured insights into deep learning in radiology at The Ohio State University, Stanford University, and the Mayo Clinic Rochester.
Below are some of the pictures I took while at the RSNA annual meeting in 2018, in Chicago, IL.
I also attended last years RSNA annual meeting in 2017 which you can find more information and photos at here http://www.toddmccollough.com/radiological-society-north-america-rsna-chicago-il-2017-mccormick-place/.
On September 30, 2018, I visited the National Zoological Park of the Smithsonian Institution in Washington D.C. The National Zoo features exhibits of many different animals including gorillas, orangutans, lions, tigers, cheetahs, zebras, red river hogs, scimitar-horned oryx, alligators, crocodiles, turtles, american bison, elephants, seals, beavers, porcupines and giant pandas. Below are some pictures taken at the National Zoo in Washington D.C.
On Friday, September, 21, 2018, I attended the New York Mets versus the Washington Nationals baseball game at Nationals Park in Washington, D. C. Jacob deGrom started for the Mets while Joe Ross started for the Nationals. Jacob deGrom pitched well over seven innings with one run on three hits with eight strikeouts. The Mets beat the Nationals with a score of 4 to 2. Below are some pictures I took during the game.