Don't miss ITN's latest "One on One" video interview with AAWR Past President and American College of Radiology (ACR) RAN and RADPAC Chair, Amy K. Patel, MD, discussing advocacy initiatives and innovations in artificial intelligence (AI) for breast imaging. 

Dr. Patel is a breast imaging trailblazer and radiology advocacy leader. In this video,  learn how radiologists can support key initiatives, ways AI is improving patient care, and more.

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Agfa Radiology Solutions is committed to enhancing clinical outcomes and operational efficiency, underscoring its innovative edge in the dynamically evolving healthcare landscape. 

AT RSNA23, ITN met with Karol Wesolowski, Global Commercial Excellence Leader, Agfa Radiology Solutions, and Jeroen Spruyt, Head of BU DR, VP Product Supply and Operations, Agfa Radiology Solutions, to learn more about how the company is transforming radiology practices.

Find more RSNA23 conference coverage here

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A discussion on processing, and then understanding, how to implement data workflow and orchestration in medical imaging with Jef Williams, managing partner, Paragon Health IT, and Imaging Technology News (ITN) editorial advisory board member. 

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One on One ... with Curtis P. Langlotz, MD, PhD

This summer, the Philips Radiology Experience Tour has been bringing Philips imaging modalities directly to the professionals that use them.

This tour offers physicians, technologists and administrators a unique opportunity to see equipment demonstrations and virtual simulations up close and in person!

Imaging Technology News spoke with Flavia Parisi and Tammie Rupnik to learn more about the Philips Radiology Experience Tour and how Philips continues to drive innovation.

If you weren't able to make it to any of the 2023 tour stops, stay tuned because the Tour will return in 2024! 

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Bayer Calantic and Blackford Analysis have teamed up with new artificial intelligence (AI) innovations to address the radiology challenges of today and tomorrow.

Imaging Technology News recently spoke with Thanos Karras, Head of Bayer Digital Solutions Business Americas, and Ben Panter, Founder and CEO of Blackford Analysis, to learn more about this new relationship and their strategy to drive radiology advancements with artificial intelligence.

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Healthcare is constantly evolving, finding new ways to innovate and advance digital tools and technology. With this comes the need for transformation to keep up with these advancements. ITN spoke with Anne Snowdon, RN, PhD, FAAN, professor of strategy entrepreneurship, Odette Business School, University of Windsor, CEO of SCAN Health and Chief Scientific Research Officer, HIMSS Analytics, to find out more about the steps needed for this transformation and what we can expect to see in the future of healthcare.

Dr. Anne Snowdon is a Professor of Strategy and Entrepreneurship at the Odette School of Business, University of Windsor.  Currently, Dr. Snowdon is the Chief Scientific Research Officer for HIMSS, Vice Chair of the Board of the Directors for Alberta Innovates, and member of the Health Futures Council of Arizona State University (ASU).  She is an Adjunct Faculty at the Department of Computer Science at the University of Windsor, the School of Nursing at Dalhousie University and the Centre for Innovative Medical Technology (CIMT), at the University of Southern Denmark.  Dr. Snowdon is leading a national Community of Practice to advance supply chain resilience across Canada, she has published more than 150 research articles, papers and cases, has received over $24 million in research funding, holds patents and has commercialized a highly successful booster seat product for children traveling in vehicles and is a Fulbright Scholar. She holds a PhD in Nursing from the University of Michigan, an MSc from McGill University, and BScN from Western University.  

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With the help of artificial intelligence, cutting-edge technology is being developed that will help improve patient outcomes and build efficiencies in healthcare, which will help transform the future of healthcare delivery. ITN sat down with Julius Bogdan, a leading expert in Digital transformation, Data and analytics, and Artificial intelligence and machine learning, to find out more about the inroads AI is making.

Julius is Vice President and General Manager, Digital Health Advisory Team for the Healthcare Information and Management Systems Society (HIMSS). In that role, he leads sales, business development, product management, product marketing and advisory services teams across the continent on digital health transformation. He is responsible for the growth of the HIMSS Analytics portfolio adoption, channel strategy, and cultivating relationships across the provider, payer and public sector health landscape. He also serves on the advisory council of various start-ups and early stage firms in finance and healthcare on technology trends, architecture and market analysis.

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Philips Radiology Operations Command Center (ROCC) is a vendor-neutral, multi-modality, multi-site telepresence tool that provides advanced tele-acquisition capabilities and connects imaging experts at a Command Center with technologists at scanning locations across an organization.  

Imaging Technology News recently met with Tanuj Gupta, Business Category Leader, Operational Informatics, and Omkar Phanse, Market Leader, Radiology Workflow Solutions, to learn more about this powerful tele-presence tool. 

Find more RSNA22 coverage here

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A discussion on macro trends and the future of medical imaging with Jef Williams, managing partner, Paragon Consulting Partners LLC, and Imaging Technology News (ITN) editorial advisory board member. 

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Artificial intelligence and general consolidation were two top cardiology trends at RSNA22. ITN/DAIC spoke with Val Kapitula, partner, Paragon Consulting Partners LLC, about some of the trends he is seeing emerge in this field. 

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Software automation can help improve many processes, including verifying eligibility for patient exams, navigating the patient responsibility landscape, and meeting the upcoming CDSM mandate for Appropriate Use Criteria. ITN recently spoke with with Kevin Borden, Vice President of Product, HCIT, for Konica Minolta Healthcare Americas about how Konica Minolta is leveraging automation to enhance productivity and efficiency in these areas. 

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Rik Primo, principle at Primo Medical Imaging Informatics Consultants and former health IT developer with Siemens, Philips and Agfa, explains the difference cloud-native versus cloud-enabled PACS and radiology enterprise imaging systems. He spoke with ITN during RSNA 2021.the Radiological Society of North America (RSNA) 2021 annual meeting.

Find more RSNA news and video

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Cynthia McCollough, Ph.D., director of Mayo Clinic's CT Clinical Innovation Center,  explains how photon-counting computed tomography (CT) detectors work and why it is a better technology over conventional CT systems. She helped Siemens develop the Naeotom Alpha, the first photo-counting CT system to be approved by the FDA in the fall of 2021. She spoke to ITN at the Radiological Society of North America (RSNA) 2021 annual meeting.

Read more about the first commercial photon-counting scanner 

The device uses the emerging CT technology of photon-counting detectors, which can measure each individual X-ray photon that passes through a patient's body, as opposed to current systems which use detectors that measure the total energy contained in many X-rays at once. By "counting" each individual X-ray photon, more detailed information about the patient can be obtained and used to create images with less information that is not useful, such as image noise. 

Current CT technology uses a two-step conversion process to convert X-ray photons into visible light using a scintillator layer in the detector. Then, photo diode light sensors turn the visible light into a digital signal. Due to this intermediate step, important information about the energy of the X-rays is lost and no longer available to aid in diagnosis. Also, contrast is reduced and images are not as clear.

Photon-counting detectors use a single step of direct conversion of X-rays into electrical current, and skips the step of converting X-rays into visible light. This allows the energy thresholds of each pulse to be collected and binned based on different kilovolt (kV) energy levels. This creates data to improve contrast and enable dual-energy, spectral imaging. The direct conversion also helps improve image quality without information loss. This improves image sharpness and contrast.

Photon-counting detectors have already been used for several years in high-energy physics and nuclear imaging. However, these previously generation photon-counting detectors could not be used with a clinical CT scanner because they could not keep up with the high higher rate of photons reaching the detector. The detector on the Naeotom Alpha was designed for this increased speed.

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Steve Holloway, company director at Signify Research, explains the trends he has seen over the past couple years in enterprise imaging. He spoke to ITN at the 2021 Radiological Society of North America meeting.

Holloway shared how medical imaging systems are expanding to include all departments in healthcare system enterprises that generate data, images and waveforms, so these items can be stored in a central location, rather than disparate silos or in separate systems requiring multiple logins or specific workstations. Most of these systems are are web enabled or web based, allowing users to work from anywhere as long as they have an internet connection. Most enterprise imaging systems also use a web-based vendor neutral archive, allowing DICOM and non-DICOM images to be stored there. All of these features allow easier and faster access to patient information and images.

He said these systems are becoming more inclusive of ologies outside of radiology and cardiology. Most notably is digital pathology, which was featured by many enterprise imaging vendors at RSNA 2021. 

Enterprise imaging systems are also accepting point-of-care ultrasound (POCUS), which has exploded in use over the past two years with COVID, Holloway said.

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Elizabeth Hawk, M.D., Ph.D., director of innovation Engagement at Rad Partners, a regional president for Matrix Teleradiology, and assistant professor of medicine at Stanford, explains how the COVID-19 pandemic has helped advance home reading and changed radiology.

While teleradiology and remote reading is not new, its expansion was greatly accelerated in 2020-2021 due to COVID. Early in the pandemic, hospitals tried to get as many of their employees as possible to work remotely, and radiologists who wanted to read from home were allowed to do so in large numbers. The past two years has taught many people that remote reading from home is possible and it also can aid the balance between work and family life. Hawk said remote reading will likely be the new normal even after the pandemic.

Hawk presented in a session on this topic at the 2021 Radiological Society of North America (RSNA) annual meeting. She said many radiologists from her practice were already reading from home prior to the pandemic, so they had the experience to quickly ramp up expansion during COVID. She offers advice to hospitals that want to introduce or expand home radiology reading. 

Find more teleradiology news

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An example of the Varian Noona software used by clinicians to interface with oncology patients demonstrated at the American Society of Radiation Oncology (ASTRO) 2021 meeting. It allows bi-direction communication between the care team and the patient’s smartphone. This included reporting complains about side effects, pain, questions for the physician and surveys. The data the interfaces with the patient record so anyone on the care team can access it or reach out to the patient.

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Several radiology IT vendors at 2021 Healthcare Information Management Systems Society (HIMSS) conference demonstrated computed tomography (CT) imaging advanced visualization software software to help automatically identify and quantify COVID-19 pneumonia in the lungs. These tools can help speed assessment of the lung involvement and serial tracking can be used to assess the patient's progress in the hospital and during long-COVID observation. 

Examples of COVID analysis tool shown in this video include clips from booth tours at: 
   • Fujifilm
   • Siemens Healthineers 
   • Canon (Vital)

Canon received FDA clearance for its tool under and emergency use authorization (EUA).

Siemens said its tool was part of its lung analysis originally developed for cancer but modified and prioritized to aid in COVID assessments. 
 

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Alex Towbin, M.D., Cincinnati Children’s Hospital Medical Center CMIO, Radiology Department associate chief of clinical operations and informatics, and chair of radiology informatics, spoke in an enterprise imaging session at the Healthcare Information Management Systems Society (HIMSS) 2021 meeting and highlight the importance of a standardizing body part labeling to enable imaging consumption, image sharing, greater levels of interoperability and image-based artificial intelligence (AI) research. 

He described the process by which existing body part ontologies were evaluated, how the HIMSS-SIIM Enterprise Imaging Community raised awareness of the issues caused by the lack of an industry-standard body-part ontology, and the process by which an industry standard will be selected. Finally, the speakers will discuss how the HIMSS-SIIM Enterprise Imaging Community plans to advocate for the selected ontology to be incorporated as part of existing standards such as DICOM and HL7 FHIR.

In the video he outlines three metadata elements needed to selection of a relevant comparison imaging examination. He also explains how the HIMSS-SIIM EIC convened experts to select a standard body part ontology for use in enterprise imaging
Describe the HIMSS-SIIM EIC’s plan to foster adoption of a standard body part ontology for use in enterprise imaging
 

Advances in PACS and Cardiology Information Systems at HIMSS 2021

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This is an overview of trends and technologies in radiology artificial intelligence (AI) applications in 2021. Views were shared by 11 radiologists using AI and industry leaders, which include:

• Randy Hicks, M.D., MBA, radiologist and CEO of Reginal Medical Imaging (RMI), and an iCAD Profound AI user.

• Prof. Dr. Thomas Frauenfelder, University of Zurich, Institute for Diagnostic and Interventional Radiology, and Riverain AI user. 

• Amy Patel, M.D., medical director of Liberty Hospital Women’s Imaging, assistant professor of radiology at UMKC, and user of Kios AI for breast ultrasound. 

• Sham Sokka, Ph.D., vice president and head of innovation, precision diagnosis, Philips Healthcare.

• Ivo Dreisser, Siemens Healthineers, global marketing manager for the AI Rad Companion.

• Bill Lacey, vice president of medical informatics, Fujifilm Medical Systems USA.

• Karley Yoder, vice president and general manager, artificial intelligence, GE Healthcare.

• Georges Espada, head of Agfa Healthcare digital and computed radiography business unit.

• Pooja Rao, head of research and development and co-founder of Qure.ai.

• Jill Hamman, world-wide marketing manager at Carestream Health.

• Sebastian Nickel, Siemens Healthineers, global product manager for the AI Pathway Companion. 

There has been a change in attitudes about AI on the expo floor at the Radiological Society of North America (RSNA) over the last two years. AI conversations were originally 101 level and discussed how AI technology could be trained to sort photos of dogs and cats. However, in 2020, with numerous FDA approvals for various AI applications, the conversations at RSNA, and industry wide, have shifted to that of accepting the validity of AI. Radiologists now want to discuss how a specific AI algorithm is going to help them save time, make more accurate diagnoses and make them more efficient.

With a higher level of maturity in AI and the technology seeing wider adoption, radiologists using it say AI gives them additional confidence in their diagnoses, and can even help readers who may not be deep experts in the exam type they are being asked to read. 

With a myriad of new AI apps gaining regulatory approval from scores of imaging vendors, the biggest challenge for getting this technology into hospitals is an easy to integrate format. This has led to several vendors creating AI app stores. These allow AI apps to integrate easily into radiology workflows because the apps are already integrated as third-party software into a larger radiology vendors' IT platform.  

There are now hundreds of AI applications that do a wide variety of analysis, from data analytics, image reconstruction, disease and anatomy identification, automating measurements and advanced visualization. The AI applications can be divided into 2 basic types — AI to improve workflow, and AI for clinical decision support, such as diagnostic aids.

On the workflow side, several vendors are leveraging AI to pull together all of a patients' information, prior exams and reports in one location and to digest the information so it is easier for the radiologist to consume. Often the AI pulls only data and priors that relate to a specific question being asked, based on the imaging protocol used for the exam. One example of this is the Siemens Healthineers AI Clinical Pathway and Siemens AI integrations with PACS to automate measurements and advanced visualization.

AI is also helping simplify complex tasks and help reduce the reading time on involved exams. One example of this is in 3-D breast tomosythesis with hundreds of images, which is rapidly replacing 2-D mammography, which only produces 4 images. Another example is automated image reconstruction algorithms to significantly reduce manual work. AI also is now being integrated directly into several vendors' imaging systems to speed workflow and improve image quality.

Vendors say AI is here to stay. They explain the future of AI will be automation to help improve image quality, simplify manual processes, improved diagnostic quality, new ways to analyze data, and workflow aids that operate in the background as part of a growing number of software solutions. 

Several vendors at RSNA 2020 noted that AI's biggest impact in the coming years will be its ability to augment and speed the workflow for the small number of radiologists compared to the quickly growing elder patient populations worldwide. There also are applications in rural and developing countries were there are very low numbers of physicians or specialists.

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This is an example of Canon's Advanced intelligent Clear-IQ Engine (AiCE) AI-driven image reconstruction software that is now being used to improve image quality on the Canon Celesteion Prime PET/CT nuclear imaging system. The deep learning is used to enhance the iterative reconstruction used to reduce noise and sharped high contrast resolution on positron emission tomography (PET) images from the digital PET detector used on the system. 

This example is a whole-body FGD PET scan of a patient with a large BMI with lung cancer.

The Cartesion Prime PET/CT is the industry’s only air-cooled digital PET/CT, provides variable bed time (vBT) acquisition as a standard feature. This and the new FDA 510(k)-pending AiCE technology were highlighted at the 2020 Radiological Society of North America (RSNA) virtual meeting. 

Find more RSNA news