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.

The post Introducing Ellumen’s Blog Series on AI Innovation in Medical Imaging and Roundup of Three Recent Articles appeared first on Todd McCollough's Website.

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.

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.

The post Advances in Microwave Near-Field Imaging: Publication in IEEE Microwave Magazine appeared first on Todd McCollough's Website.

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.

The post Phase Confocal Method for Near-Field Microwave Imaging Patent named co-inventor on Issued appeared first on Todd McCollough's Website.

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.

The post A Phase Shift and Sum Method for UWB Radar Imaging in Dispersive Media appeared first on Todd McCollough's Website.

I have included an excerpt from the accepted version of the paper below. DOI: https://doi.org/10.1109/TMTT.2018.2801862 © 2018 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.

Figure 2 in the paper shows the system as was set up at Ellumen Inc. along with a PVC cylinder placed in the middle tray. A reconstructed image from data collected using the setup in Figure 2 using the delay multiply and sum (DMAS) imaging algorithm is shown in Figure 9. In Figure 10(a) the object was changed to a metallic object and a long wood square object both placed in the middle tray. A reconstructed image produced using DMAS is shown in Figure 10(b). Also note that the DMAS algorithm was programmed on eight nVidia Tesla GPUs which allowed images to be produced in under 1 minute. A comparison between the time domain system and frequency domain system was performed in the paper but is not included in the above excerpt. This analysis showed that both methods of data collection can allow for accurate reconstructed images to be obtained. The software to control the data collection was also updated as presented in this paper so that it takes 20 minutes to complete both incident and total field data collections. I encourage you to download and read the full “A Time-Domain Measurement System for UWB Microwave Imaging” paper from IEEE for full details and analysis.

The post A time-domain measurement system for UWB microwave imaging: publication in IEEE MTT appeared first on Todd McCollough's Website.