The Radiological Society of North America’s (RSNA) 106th Scientific Assembly and Annual Meeting was scheduled to take place at McCormick Place in Chicago, IL, starting on November 29th, in 2020; however, due to COVID-19, the meeting moved to an all virtual event from November 29 to December 5th. I was fortunate to attend RSNA 2020 in its first virtual only format. This year’s tagline was “Human Insight/Visionary Medicine.”

rsna 2020 meeting logo - Virtual Radiological Society of North America (RSNA) Meeting in 2020

This year included live meeting sessions running from 8 a.m. to 6 p.m. on November 29 to December 5th. If you were unable to attend the live programming as it happened, it converted to on demand sessions thereafter. One advantage of the live programming sessions was the ability to chat with other attendees while it was occurring. However, not all of the live programming sessions were in fact live even though they broadcasted originally live. Instead many of these live sessions occurred without the normal moderator present and were prerecorded. The meeting also included on-demand content available 24/7 during that time period. This year many exhibitors had virtual booths for exhibition and there was also virtual networking available. For those who paid for a premium registration, there was also the benefit of extended on-demand access to most meeting content until April 30, 2021. In addition, case of the day and digital posters were available at RSNA 2020. Navigation presented to users upon login to RSNA 2020 was as below:

rsna 2020 navigation - Virtual Radiological Society of North America (RSNA) Meeting in 2020

Outside of virtual booths, this year included various ways to get the latest on industry developments including two theaters: an Innovation Theater and AI (artificial intelligence) Theater. This was in addition to Lunch & Learns, Featured Demonstrations, Pre Show Presentations, and Roundtable Discussions. There was also an Imaging AI In Practice Demonstration including four videos on the following topics: 1) Introduction, 2) Work with AI, 3) Evolve with AI, and 4) Save Time with AI. These Imaging AI In Practice Demonstration videos included helpful flow-charts to describe how the AI Orchestrator and AI Algorithms integrate into the radiology workflow:

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There was certainly a large amount of content focused around AI at RSNA 2020. I attended a session titled Artificial Intelligence: Beyond Interpretive Considerations that consisted of four separate talks. One talk in this session discussed how there was minimal merger and acquisition activity in radiology AI in 2020 and there were six companies involved with radiology AI that previously attended RSNA in prior years that appear to no longer be active. Another talk discussed Generative Adversarial Networks and their potential to create synthetic data in radiology. An example was shown on using a StyleGan2 (a type of GAN) to create synthetic chest radiographs. Another talk discussed the liability risks in using AI in medicine. Those potentially at risk include radiologists, healthcare systems, and even AI developers. The presenter discussed how due to lack of meaningful case law to date a lot remains unknown. I also attended another session titled Creating Publicly Accessible Radiology Imaging Resources for Machine Learning and AI. One talk in this session discussed using the The Cancer Imaging Archive (TCIA) as a dataset for AI training. Another talk mentioned some other datasets including the Alzheimer’s Disease Neuroimaging Initiative (ADNI), Medical Information Mart for Intensive Care (MIMIC), National Biomedical Imaging Archive (NBIA), the Lung Image Database Consortium (LIDC), and datasets from Stanford University’s Center for Artificial Intelligence in Medicine and Imaging. In addition, discussion was made of efforts from developing the Medical Imaging and Data Resource Center (MIDRC), an open-source database with medical images from COVID-19 patients, being collaborated on across more than 20 organizations in the U.S.

This year there appeared to be less focus on 3D printing. Although one live session titled Medical 3D Printing Regulatory and Quality Considerations was available. One talk in this session discussed sterilization for 3D printed devices. There was discussion made of sterilization approaches for 3D printed devices both in the hospital (steam, hydrogen peroxide, and ethylene oxide) and outside the hospital (gamma radiation, e-beam, hydrogen peroxide, and ethylene oxide). There were also at least two on-demand sessions available in the Innovation Theater from two industry leaders in 3D printing: 1) Extending Access to Extended Reality from Materialise and 2) Learnings from the Field: Clinical Care and Device Development in the COVID Era from Formlabs Medical.

The sponsors of RSNA 2020 included those below:

thanks corporate partners - Virtual Radiological Society of North America (RSNA) Meeting in 2020

If you are interested learning more about past in person RSNA meetings at McCormick Place, I attended the RSNA annual meeting last year in 2019, and the prior two years in 2018 and 2017. You can find more information and numerous photographs at http://www.toddmccollough.com/radiological-society-of-north-america-rsna-meeting-in-chicago-il-in-2019-at-mccormick-place, http://www.toddmccollough.com/radiological-society-of-north-america-rsna-meeting-in-chicago-il-in-2018-at-mccormick-place/, and http://www.toddmccollough.com/radiological-society-north-america-rsna-chicago-il-2017-mccormick-place/.

I am pleased that a paper titled “Advances in Microwave Near-Field Imaging” has been published in IEEE Microwave Magazine, in 2020, that I am a co-author on. This paper is a review paper of known near-field microwave imaging systems until late 2018. One of the systems reviewed is the one I worked on with the Celadon Research Division of Ellumen Inc. that was described in the paper titled “A Time-Domain Measurement System for UWB Microwave Imaging” which published in IEEE Transactions on Microwave Theory and Techniques, in 2018. The paper appearing in IEEE Microwave Magazine is particularly focused on recent active mode systems for early stage breast-cancer and brain-injury detection. Active mode is where microwave radiation is directed towards tissue, and the scattered electromagnetic fields are detected and processed. The paper also explores nondestructive testing using microwave-imaging techniques including through-the-wall imaging and security screening applications.

Based on a thorough review of the systems, the paper also offers an outlook of using microwave imaging in the future. Microwave imaging for medical applications has attracted significant interest which is expected to continue due to technical developments and improvements in hardware manufacturing and software. Vector network analyzers and oscilloscopes that have longed been used in experimental systems are starting to become replaced by more compact and cost effective instruments which will help with future commercial products. Decreases in system cost and size is to be expected moving forward. It is believed that microwave imaging techniques will be expanded to additional clinical applications and clinical trials will help lead the way towards use in patient care utilizing this technology.

I have included an excerpt from the accepted version of the paper below. DOI: https://doi.org/10.1109/MMM.2020.2971375 © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Table 2 in the paper offers a comparison of three microwave brain-imaging detection systems. It is noteworthy that the frequency used by the three groups is typically lower than that found for comparable breast-imaging systems. This is because brain tissue is more lossy to microwaves than breast tissue and thus a lower frequency allows for more energy to enter the brain. One of the microwave brain-imaging detection systems is developed by EMTensor and detects strokes. This system was previously exhibited on the floor of the Radiological Society of North America’s (RSNA) 104th Scientific Assembly and Annual Meeting at McCormick Place in Chicago, IL, in 2018.

I was able to attend a college basketball game at Welsh-Ryan Arena in Evanston, Illinois, on Saturday, January 11, 2020. The Northwestern University Wildcats played the University of Nebraska Cornhuskers. Welsh-Ryan Arena recently underwent $110 million of renovations that were completed in late 2018. Even though it was snowing in Evanston at the conclusion of the game, with more snow expected, Northwestern fans turned out with the attendance recorded at 5,664 out of 7,039 (80.4%). My impression though was that the attendance was less than 80% of the capacity in the arena.

Coming into the basketball game, Northwestern had lost all of their four Big Ten conference games and had lost their previous five games. Northwestern played well against Nebraska and never trailed after about 10 minutes into the first half. Northwestern prevailed to beat Nebraska with a score of 62 to 57. Northwestern was coached by Chris Collins and Nebraska was coached by former Chicago Bulls coach Fred Hoiberg.

Nebraska guard Cam Mack added 11 points and 10 rebounds and sunk a 3 three-point field goal to take Nebraska within three points of Northwestern with 51 seconds left in the game. However, Northwestern forward Miller Kopp led the team with 15 points, 5 rebounds and 1 steal, and hit two late free throws with 9 seconds left in the game to clinch the win. Northwestern guard Pat Spencer also added 14 points, 8 rebounds, 5 assists, and 1 steal. In addition, Northwestern forward Robbie Beran added a double double with 10 points and 10 rebounds.

Below are some pictures I took while at Welsh-Ryan Arena during the Nebraska vs Northwestern college basketball game.

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I was able to attend the Radiological Society of North America’s (RSNA) 105th Scientific Assembly and Annual Meeting at McCormick Place in Chicago, IL, which occurred from December 1 to December 6, 2019. The annual meeting is a very large gathering of industry leaders in medical imaging, radiologists, and other related industry professionals. This was the 105th Scientific Assembly and Annual Meeting with the tagline: See Possibilities – Together. This year expanded focus on artificial intelligence with a brand new AI Showcase Technical Exhibit in the North Building. More than 100 companies were in the AI Showcase to demo software and products. In addition, the RSNA AI Deep Learning lab, a hands on classroom focusing on using open-source tools for deep learning, was now integrated into the AI Showcase Technical Exhibit. This year the AI Deep Learning Lab featured four unique sessions: Beginner Class: Classification Task, Segmentation, Data Science: Data Wrangling, and Generative Adversarial Networks (GANs).

This year also expanded focus on 3D Printing and Advanced Visualization with an expanded Showcase and Theater offering daily presentations on the latest research and innovations in 3D printing for medical applications. I was able to attend a presentation covering Category III CPT Codes for 3D Printing of Anatomic Models and Guides, Scripting for Segmentation, 3D Printing to Support Research, and Leveraging 3D Printing for Surgical Simulation. It was quite interesting to hear more about the Category III CPT Codes for 3D Printing, which includes 0559T, 0560T, 0561T, and 0562T that went into effect in July, 2019. This should allow for greater adoption by physicians and medical centers. Even so, for those utilizing 3D printing, it was encouraged by the presenter of the CPT code talk to sign up for the RSNA-ACR 3D Printing Registry to help support a future category I CPT code.

As usual there were numerous posters and presentations. Also as usual, there were many exhibitors with medical imaging devices ready to provide demonstrations of their latest technology. New exhibitors this year included Amazon Web Services (AWS) and Medical IP. I was able to attend a few educational courses and scientific sessions. In particular I attended the Artificial Intelligence: Cutting Edge Artificial Intelligence session and Creating Publicly Accessible Radiology Imaging Resources for Machine Learning and AI sessions. In the former session mentioned above, an interesting talk titled Defacing Neuroimages discussed image de-identification using a two-step deep learning model for head CTs and brain MRIs. In the former session, I also was intrigued by a talk titled Automated Detection of Vertebral Fractures in CT Using 3D Convolutional Neural Networks that discussed automatically detecting vertebral fractures in CT images of the spine using a learning method with 3D features. The latter session featured several talks discussing practical challenges with data preparation including image pre-processing steps, techniques for creating ground truth labeling, and statistical approaches to create training and testing data sets.

Below are some of the pictures I took while at the RSNA annual meeting in 2019, in Chicago, IL.

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I attended the RSNA annual meeting last year in 2018 and the prior year in 2017, where you can find more information and photos at http://www.toddmccollough.com/radiological-society-of-north-america-rsna-meeting-in-chicago-il-in-2018-at-mccormick-place/ and http://www.toddmccollough.com/radiological-society-north-america-rsna-chicago-il-2017-mccormick-place/.

During my work with the Celadon Research Division of Ellumen Inc., I was a co-inventor on a patent titled “Phase Confocal Method for Near-Field Microwave Imaging” that issued on October 8, 2019. This is the fifth patent I have been a co-inventor on.  If you are interested in learning more about my prior four patents see the post titled “Description of Three Patents Named Co-Inventor On Assigned to Ellumen Inc” and also the post titled “Microwave Imaging Device Patent Named Co-Inventor on Assigned to Ellumen Inc.

This patent builds upon work presented in 2017 in a paper titled paper titled “A Phase Confocal Method for Near-Field Microwave Imaging” published in IEEE Transactions on Microwave Theory and Techniques. The patent describes a frequency domain based method that uses electromagnetic waves transmitted and received by antennas to estimate a phase shift caused by an object in the path of the electromagnetic waves. The phase is reversed to allow for an image to be constructed.

The patent provides protection for a system and method for producing microwave images that calculates phase shifts based on a propagation distance from a receiver to a transmitter, compensating a phase using the phase shift, and calculating a variance of the phase shift using an inverse summation. Further, the patent provides protection for a method for producing images that calculates phase shifts based on a propagation distance from a receiver to a transmitter, compensating a phase using the phase shift, and utilizing complex-number detected microwave signals as unit vectors when producing an image. Additionally, the patent provides protection for a method for producing images that calculates phase shifts based on a propagation distance from a receiver to a transmitter and compensating a phase using the phase shift along with information from a phase change in a connector on both the transmitter and receiver end and a phase change in the transmitter and receiver. The patent also provides protection for methods for utilizing multiple frequencies. The high efficiency of the method allows for real-time imaging.

Below is a patent certificate that was created to celebrate the accomplishment of having the patent granted. This is the first patent I have had issued since after the USPTO celebrated the issuance of 10 million patents and changed the patent cover design.

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