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.

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/.

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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/.

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The first and third patents titled “Dielectric Encoding of Medical Images” resulted from wanting a way to allow for doctors to easily read and understand images produced using electromagnetics represented in dielectric values. To accomplish this I worked with the chief executive officer (CEO) of Ellumen Inc. to explore the microwave imaging modality while also allowing for easy adaptability by doctors and hospitals. I researched the modality, developed algorithms, and developed programs to convert medical images in dielectric values to Hounsfield units, which are present in computed tomography (CT) scans, and to MRI intensity values, which are present in magnetic resonance imaging (MRI) scans. The code successfully worked for single frequencies and over a range of frequency values (using a Debye model). This allows for doctors to understand images producing using electromagnetics in readily understood CT and/or MRI formats without requiring any additional training, leading to timely and accurate medical diagnosis. The conversion method developed allows for existing medical diagnostic tools and analysis techniques to be used directly with microwave imaging. In addition, the method for conversion from an image in Hounsfield units to dielectric values and conversion from an image in dielectric values to Hounsfield units can go in both directions. Furthermore, the method for conversion from an image in dielectric values to MRI intensity values includes creating a water content map and a T1 map as an intermediary step. The patent also included a method to convert medical images in Hounsfield units to dielectric values using a frequency dependent model. Deriving dielectric models from CT scans is often useful when solving complex problems in computational electromagnetics.

The second patent titled “Distributed Microwave Image Processing System and Method” resulted from the need to want all imaging centers, radiology groups, and/or doctor’s offices to be able to have access to images produced using electromagnetics without having to upgrade their computer hardware. A method was developed to allow for the majority of image processing and image reconstruction of microwave images to occur in a centralized computing environment. Instead of performing image processing and image reconstruction at the imaging centers, radiology groups, and/or doctor’s offices, these remote sites send the microwave data they collect to the the centralized computing environment.  The centralized computing environment also offers another distinct advantage; the data and results acquired at all the remote sites can be stored and used to enhance processing and reconstruction of microwave images. The centralized computing environment takes advantage of multiple processors to perform iterative reconstruction and seeds the reconstruction using prior data. In one embodiment of the invention, the seed is generated by first comparing collected and stored scattering fields to find a best or closest match and then using stored data of a prior reconstructed image reconstructed corresponding to the stored scattering fields of the best or closest match. In another embodiment of the invention, the seed is generated by both of (1) using the collected microwave data and (2) using stored data of a prior reconstructed image of a different patient which closely matches data of the current patient. The centralized computing environment also has the capability to convert medical images in dielectric values to Hounsfield units. The method developed and described allows for more accurate image reconstructions to occur in less time than if they were performed at remote sites.

It is exciting to work on new technology and methods that can have a real impact on the health of patients. Below are three patent certificates that were created to celebrate the accomplishment of having these three patents granted.

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A popular feature this year at RSNA, was a deep learning classroom presented by the NVIDIA Deep Learning Institute (DLI), designed for attendees to engage with machine learning tools, write algorithms, and improve their understanding of emerging machine learning technology. In one of these sessions, attendees trained a deep neural network to recognize handwritten digits. In another session, attendees trained convolutional neural networks (CNNs) to create biomarkers to identify the genomics of a disease without the use of an invasive biopsy. In yet another session, attendees segmented magnetic resonance imaging (MRI) images to measure parts of the heart.

Another feature this year was a separate section for machine learning showcase exhibitors. This section allowed those interested in machine learning to easily network with those in the field. This section featured a machine learning theatre with presentations from industry leaders. For example, in one presentation, Google Cloud talked about machine learning in imaging and how to build your own models on the cloud. In another presentation, Siemens Healthineers discussed artificial intelligence solutions for clinical decision making by turning medical images into biomarkers to help increase effectiveness of care. There was also a 3D printing theater with many posters and actual 3D printed parts nearby. In addition, there were several virtual reality demos setup to allow attendees to try themselves.

I was able to attend many interesting courses on machine learning, radiomics, 3D printing, virtual reality, and predictive analytics. For example, in one course I attended there was discussion of how to use KNIME to incorporate radiology data sources into predictive modeling and interpret the results and make visualizations. There was an interesting talk in another course I attended about using virtual reality in medical education and how it can greatly decrease the amount of time needed to teach students when compared to PowerPoint presentations. In yet another course I attended, instructors walked attendees through using Mimics and 3-matic from Materialise. In this course participants were taught how to segment out musculoskeletal, body, neurological, and vascular systems from DICOM files into a Standard Tessellation Language (STL) file for use with a 3D printer.

I was also able to attend the plenary session by Michio Kaku titled “The Next 20 Years: How science and technology will revolutionize business, the economy, jobs, and our way of life.” In the talk Dr. Kaku discussed the next wave of wealth generation in our modern economy which he believes is advancements at the molecular level including in artificial intelligence, nanotechnology, and biotechnology linked together by the cloud. He believes that information will be everywhere and computers will become like the word electricity today, where it is not mentioned in language as it is ubiquitous. Dr. Kaku recognized robots will replace jobs in the future but said robots are weak in three areas: 1) pattern recognition, 2) common sense, and 3) human interactions. Thus he believes in many cases artificial intelligent systems will aid humans and not replace them.

Below are some of the pictures I took while at the RSNA annual meeting in 2017, in Chicago, IL.

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