So far three articles I have contributed to have appeared on the Ellumen website. In case you missed it below is a brief summary of these articles. Be sure to check them out for additional details.

Could AI Be the Radiologist’s Best Friend?

The Could AI Be the Radiologist’s Best Friend? article published on February 17, 2021. The article discusses how AI has the potential to alleviate the demand on radiologists by doing preliminary evaluations on medical images and organizing imaging workflows to improve efficiency. The article mentions the five most common use cases of AI in radiology today: 1) optimizing workflow for productivity, 2) tagging images so critical patients are the first reviewed, 3) automating part of the image analysis, 4) enhancing imaging quality, and 5) providing decision support, and presents an excellent graphic to accompany this to improve understanding. The article further discusses how by positioning AI technology as a useful and supplemental tool, radiologists and clinicians can reap the benefits while their confidence in using AI grows and skepticism fades.

AI for Medical Imaging Research: A Guide to Accessible Tools and Resources

The AI for Medical Imaging Research: A Guide to Accessible Tools and Resources article published on March 22, 2021. The article discusses numerous tools and resources that currently exist that researchers can use to help develop AI algorithms for medical imaging. The article also provides an extensive list of medical imaging datasets with high quality images and annotations that already exist. Further, it is discussed how it is hoped with improved awareness of the numerous tools and data sources available to AI researchers today, participation in medical imaging research and progress to accelerate AI in medical imaging transformation will be made.

AI Tools in Triage Lead to Faster Diagnoses

The AI Tools in Triage Lead to Faster Diagnoses article published on May 4, 2021. This article discusses utilizing AI as a triage mechanism and in support of more efficient workflows for medical imaging diagnosis. It is known that patient outcomes are directly correlated with speed and in many cases patient care is extremely time sensitive. The article discusses how AI can be be utilized to reduce the time required for an MRI scan from one hour to 15 minutes and by doing so reduce the noise in images and allow more patients to be scanned by the same MRI machine each day. The article also presents details on how a deep learning neural network can be trained using labeled images of diseases and normal conditions present and shows a graphic to further understanding. The neural network can be leveraged to provide radiologists with a screening tool before they look at a series of images, allowing them to more quickly move through the series and form an opinion on a diagnosis.

Future Articles for Ellumen’s Blog Series on AI Innovation in Medical Imaging

It is believed that AI in medical imaging can lead to better outcomes for patients. Radiologists who recognize the importance of AI’s medical imaging transformation will lead to improvements in patient care and accuracy of a diagnosis. Be sure to keep an eye out for new forthcoming articles on the Ellumen website related to AI innovation in medical imaging and feel free to reach out to experts at Ellumen to help explore the potential of AI to solve medical imaging research needs.

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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 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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The “Microwave Imaging Device” patent resulted from wanting an automatic way to acquire microwave imaging data pertaining to some object and/or body part from both a movable transmitting and receiving antenna. In addition, there was desire to be able to collect not just 2D data but also 3D data and also acquire the surface information of what was placed inside the scanner. To accomplish this, a system was built that: 1) contained an object support to hold an object on, 2) contained a transmitter antenna, 3) contained a receiver antenna, 4) had both an inner and outer ring where either the transmitter or receiver was mounted on, 5) contained a controller to independently rotate both the inner and outer ring, 6) contained a computation processor to receive the collected data, and in one embodiment 7) contained a controller to move the object support up and down, and 8) contained an object surface position sensor mounted to either the inner or outer ring to collect the surface of the object. It is important to note that the inner and outer ring are concentric to each other but have different radii. In some embodiments, gears, pinions, and motors are used to help rotate the inner and outer rings, while a feedback monitor can determine if any potential mismatch in positioning occurs. The system further allows for the object surface position data to be used as a seed in the reconstruction of an image represented in dielectric values. In one embodiment, stored data of a prior image reconstruction that closely matches data of the object is used in combination with surface position data as a seed in the reconstruction. The patent also allows for the transmitter and receiver antenna to be mounted in such a way that they can radially translate to and from the center of the device. In addition, the patent covers some aspects of the controller and its module including positions to move both the transmitter and receiver antenna to, the names and locations of the collected data for storage, any necessary instrument parameters, and a calibration of the initial positions of the transmitter and receiver antenna.

The Celadon Research Division of Ellumen Inc. built a prototype of the robotic microwave imaging device as described in the patent that communicates with laboratory instruments (arbitrary waveform generator, oscilloscope, and vector network analyzer) and an infrared sensor and acquires data at different positions for the transmitting and receiving antenna and sensor. I helped program instrument commands to talk to the laboratory instruments using Virtual Instrument Software Architecture (VISA) to automatically acquire data. I collaborated on development of the graphical user interface (GUI) using VB.NET, MATLAB, and a dynamic-link library (DLL). The device can collect data in both the time and frequency domains and be operated remotely with monitoring by a camera. I helped collect data and programmed code to process the data including quickly loading in many data sets, plotting the data, performing analysis, and performing surface reconstruction. I also helped program and generate image reconstruction results from the data collected by the device. The Celadon Research Division of Ellumen Inc., presented a discussion of the device and imaging results in the journal publication IEEE Transactions on Microwave Theory and Techniques and at the IEEE AP-S Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting in San Diego, CA, in July 2017. See the paper titled “A Phase Confocal Method for Near-Field Microwave Imaging” and the paper of the poster presentation titled “Experimental Microwave Near-field Detection with Moveable Antennas” for some additional details. I was a co-author on the published paper and helped participate in the presentation. A few photos from the conference in San Diego were previously published in the post titled “IEEE AP-S Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting in San Diego, CA, in July 2017.”

It is exciting to work on new technology and devices that can have a real impact on the health of patients. Below is a patent certificate that was created to celebrate the accomplishment of having the patent granted.

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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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