Here is the list of the most popular articles and videos on the Imaging Technology News (ITN) magazine website from the month of February 2019. This is based on website analytics of the month’s 205,192 pageviews:

1. Virtual Reality Boosts Revenues and Patient Understanding

2. 5 Key Trends in New Ultrasound Technology

3. MRI and Computer Modeling Reveals How Wrist Bones Move

4. Stereotactic Radiotherapy Improves Long-Term Survival in Stage-IV Cancers

5. Congress Directs FDA to Establish Federal Breast Density Inform Standard

6. Siemens Healthineers Demonstrates Artificial Intelligence, Healthcare Digitalization at HIMSS19

7. Philips Launches Epiq Elite Ultrasound Series

8. Study Assesses Risk of MRI Exams for Patients With Tattoos

9. 360 View of the Gantry Room at the Northwestern Medicine Chicago Proton Center

10. New Pathology Guideline Advances Accuracy in Breast Cancer Testing

Technology is reshaping not only our understanding of cardiac disease but what motivates patients to visit doctors. Both trends are likely to intensify in the coming years, vastly expanding our understanding of cardiac disease and indelibly changing the practice of medicine, according to Partho P. Sengupta, M.D., chief of cardiology at West Virginia University in Morgantown, W. Va.

In an ITN podcast, Sengupta, who also directs cardiovascular imaging and chairs the Center for Innovation at the University’s Heart and Vascular Institute, described how the analysis of big data by smart algorithms is uncovering truths about cardiac disease. Many of these truths, he said, have been buried for years in the volumes of data generated by ultrasound based echocardiography, a widely practiced form of cardiac imaging.

“Echo is full of information that we have not even mined,” Sengupta said in the ITN podcast. In the Podcast, he discusses the impact of smart algorithms, as well as other technologies on medical practice now and in the years ahead. 

Sengupta is scheduled to speak about related topics at #ACC.19, the annual meeting of the American College of Cardiology, which will run from March 16 to 18.  He will be speaking March 16 in sessions titled:

• “Man vs. Machine: Current and Future Applications of Machine Learning in Cardiovascular Imaging;” and
• “Imaging Innovation Hub for Congenital Heart Disease.”

Also that day, he will moderate a panel discussion at Future Hub entitled “Are Clinical Virtual Reality and Augmented Reality Ready for Prime Time?” On March 17, he will be a presenter during the session entitled “Current Controversies in Echocardiography.”

Sengupta is listed on ten posters scheduled for display at ACC.19. Many will describe how echo may enhance physicians’ ability to diagnose cardiac disease.

Impact Of Machine Learning On Cardiology

Sengupta and colleagues are currently researching machine-learning algorithms in echocardiography. ML algorithms can analyze data sets that would otherwise overwhelm people, he noted. The information that can be pulled by them from medical data might lead to new knowledge about heart disease. And there is plenty more to learn. “We are just scraping the surface of what we understand of cardiology,” he said.

Meanwhile technologies other than smart algorithms are affecting other facets of medical imaging, Sengupta acknowledged in the ITN podcast. The output of wearable and home sensors that monitor vital signs, for example, is already changing the doctor-patient relationship. This is seen in the questions that patients pose to physicians, he said.  Rather than seeing a doctor because of shortness of breath, for example, a patient today might ask about the significance of what these devices have reported.

“It is a new type of reason the patient wants to see a doctor. The whole taxonomy of how we define cardiac disorders is going to change. People are not going to present with symptoms,” Sengupta said in the ITN podcast, noting that this “is opening up myriad other ways that we need to look at disease.”

Greg Freiherr is a contributing editor to Imaging Technology News (ITN). Over the past three decades, Freiherr has served as business and technology editor for publications in medical imaging, as well as consulted for vendors, professional organizations, academia, and financial institutions.

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Carestream Health has signed an agreement to sell its healthcare information systems (HCIS) business to Philips Healthcare.

Carestream’s HCIS business unit provides imaging IT solutions to multi-site hospitals, radiology services providers, imaging centers and specialty medical clinics around the world. The business has developed strong customer relationships in attractive, high-growth healthcare segments and is positioned for continued growth and success.

As a result of this acquisition, Philips’ expanded healthcare IT business will feature Carestream’s enterprise imaging platform— including best in class vendor neutral archive (VNA), diagnostic and enterprise viewers, multimedia reporting, Workflow Orchestrator and clinical, operational and business analytics tools — as part of its broad portfolio.

“We have had global success in providing radiology and enterprise imaging IT systems to help medical professionals provide quality care and enhance their operations,” said Ludovic d’Aprea, Carestream’s general manager for halthcare information solutions. “By becoming part of Philips, the HCIS business will have a greater opportunity to thrive and grow. Both organizations share a commitment to meaningful innovation which is deeply embedded in each company’s culture. Customers will have access to a broader portfolio of healthcare IT solutions to simplify medical image management, enable effective collaboration and enhance patient care.”

Like Carestream, Philips has built a strong, global business based on customer focus, world-class technical excellence and continuous innovation.

“Philips partners with global healthcare providers to connect people, information and technology with the commitment to deliver on the Quadruple Aim of improved patient experiences, better health outcomes, improved staff experiences, and lower costs of care,” said Robert Cascella, chief business leader precision diagnosis at Royal Philips. “This acquisition will enhance our ability to provide flexible solutions to hospitals and health systems. The combination of our successful innovations in imaging system platforms, workflow optimization and artificial intelligence-enabled informatics, combined with Carestream’s cloud-based enterprise imaging informatics platform and complementary geographic footprint will provide a solid foundation to deliver on the promise of precision diagnosis.”

Carestream will retain its medical imaging systems, dental and industrial films, non-destructive testing, and precision coating businesses which are not impacted by the sale. “These established businesses have solid financial foundations, innovative technology platforms and have earned the trust of loyal customers around the world,” said David C. Westgate, chairman, president and CEO of Carestream. “Our focus will be on delivering innovation that is life changing — for patients, customers, channel partners, communities and other stakeholders — and we will grow the company for long-term success.”

Following receipt of all regulatory and applicable government approvals, input from works councils and unions, and meeting all pre-conditions, the two companies will work towards closing the sale in the second half of this year and will continue to operate independently until closing.

Additional terms of the transaction were not disclosed.

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Computed tomography (CT) and magnetic resonance imaging (MRI) scans are chock full of information that might be used to personalize treatment. Unfortunately, this information is not readily available. The latest type of artificial intelligence (AI) algorithms, however, might change that, according to Albert Hsiao, M.D., assistant professor of radiology at the University of California San Diego.

During a scientific session March 16 of this year’s annual meeting (#ACC19) of the American College of Cardiology, Hsiao said he plans to discuss how smart algorithms might extract this information from CT and MRI scans and how its availability could change patient management.

A key takeaway of his presentation, to be given as part of a scientific session titled “Man vs. Machine: Current and Future Applications of Machine Learning in Cardiovascular Imaging,” will be that the “explosive growth” of machine learning has raised “opportunities for tying (this information) directly to patient management,” he said. “This is in large part because of the amazing democratization of machine learning that this new area of ‘deep learning’ has enabled.”

Physicians might use this information to create “imaging phenotypes” of patients. These phenotypes, in turn, might be used to personalize the treatment of patients.

“Information like how much body fat you have, how fat is your liver, how much is your muscle mass — those are rich pieces of information that if quantified could really help us guide and make more specific patient recommendations,” said Hsiao, who seven years ago co-founded Arterys, a modern day provider of cloud-based AI software for medical imaging.

Imaging phenotypes would characterize the physical aspects of a patient, seen in medical images. For the time being the information remains embedded in these images, he said. But smart algorithms might extract and quantify it. Doing so could give clinicians insights into exactly what their patients need, allowing an unprecedented degree of personalization.

DL Growth To Be Cited at #ACC19

Deep learning (DL), also called machine learning or unsupervised learning, allows algorithms to discover rules for distinguishing certain features of images. In the early days of AI, DL allowed some algorithms to distinguish pictures of cats from those of dogs. Rather than coding the rules for distinguishing these pictures, the algorithms “learned” them.

Today’s DL algorithms might similarly learn how to pick out and extract phenotypic information from CT and MR images. They would do so by leveraging convolutional neural networks (CNNS), which are typically used to analyze images.

Deep learning — and CNNs — might be applied in any medical imaging modalities, including echocardiography and X-ray angiography. But the three-dimensional nature of CT and MRI make these two modalities attractive, especially when it comes to AI efforts aimed at quantitation, Hsiao said, for example, determining the fat fraction of a liver.

Over the past eight years, Hsiao has co-authored articles primarily on smart algorithms in cardiac MRI. But there is also “quite a bit of open terrain” regarding CT, Hsiao said: “There's some interesting work for coronary CT and estimating CT-FFR (fractional flow reserve) with machine learning.”

How To Generalize Algorithms

The challenge facing AI developers is to validate these algorithms across multiple patient populations. Algorithms typically are developed and tested in a single population type. This may be exemplified by a cohort of 200 to 300 patients. Achieving “generalizability” may be especially important when trying to dig information out of CT and MR images — information needed to classify patients according to “imaging phenotypes” — because different types of patients may express different types of imaging phenotypes.

The challenge is not, however, as difficult as might be imagined, Hsiao said: “We just basically need the data from these other demographics.”

The data are required, he said, not only to refine the algorithms but to prove that they work on different types of patients. This proof is needed if the algorithms are to have wide clinical utility — and, consequently, to establish the value of unused data now buried in CT and MR images.

Greg Freiherr is a contributing editor to Imaging Technology News (ITN). Over the past three decades, Freiherr has served as business and technology editor for publications in medical imaging, as well as consulted for vendors, professional organizations, academia, and financial institutions.

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Smart algorithms could make cardiology “more fun and less burdensome,” said Anthony Chang, M.D., a pediatric cardiologist at the Children’s Hospital of Orange County in California (CHOC), internationally acknowledged as an expert in artificial intelligence.

Artificial intelligence (AI) might reduce or even eliminate the hours spent processing medical records information or even taking notes “while he or she is talking to the patient,” said Chang, whose nickname is Dr. A.I.

Needed will be algorithms that do the tedious work now done by cardiologists, he said in a podcast published by Imaging Technology News (ITN). The burden of doing this grunt work is one reason cardiologists are burning out, Chang said in the ITN podcast: “That needs to go away.”

At the 2019 annual meeting of the American College of Cardiology, the pediatric cardiologist, who also serves as CHOC’s chief intelligence and innovation officer, will describe how AI can boost the quality of cardiological practice and how its use can help prevent burnout. At noon on Monday March 18, Chang is scheduled to kick off the session entitled “Rise of Intelligent Machines: The Potential of AI in Cardiovascular.”

Deep Learning Is Both Plus and Minus

Algorithms created through deep learning, in which algorithms get smart about the rules governing medical practice, often through unsupervised learning, will be featured prominently. But deep learning (DL) is both boon and bane for cardiologists, according to Chang, who described DL in the ITN podcast as a kind of black box whose opacity threatens the credibility – and, consequently, adoption – of AI.

“We have to do our best to make it a glass box – not a black box … Maybe (cardiologists won’t) fully understand” what’s in the box, but they definitely have to “see things as more transparent,” he said in the podcast.

Cardiologists must do their part by trying to understand the fundamental of AI tools, said Chang, who recommended that cardiologists “at least take a few courses (or) watch a few videos” to better understand the basics of data science and become comfortable using tools that he predicted will be “here to stay for medicine.”

The pediatric cardiologist said AI will “get to the point where it is a very, very nice augmentation of (cardiologists’) human skills.” Chang went on to predict that AI will complement human cognition. While stopping short of forecasting the end of deep learning, he said “deep learning is certainly going to start to recede into the background a little bit – and human cognition ultimately is going to be needed. That is when AI matures.”

In the meantime, cardiologists should take advantage of smart algorithms when and where they can, he recommended. Doing so will be necessary if cardiologists are to stay ahead of the mounting wave of information. “Only a computer can put all of the analytics together with multilayered data and very few if any humans can really do that well and consistently without fatigue,” he said.

People And AI To Form Synergy

Chang forecasted “a great synergy” between cardiologists and machine intelligence, one that will get increasingly strong. But cardiologists have a ways to go to reach this point. He described cardiology as being generally at “a relatively low trajectory” on the curve needed to take advantage of AI. This can – and will – be corrected, he said in the podcast, “because cardiologists inherently are data savvy and very innovative. I think we just need to bend that curve up.” Doing so, however, is going to take “a lot of education” and “a lot of application demonstrations,” he said.

If cardiologists are to reap the full benefits of AI, they must reorganize their data, Chang said in the ITN podcast. He compared the needed organization to that of a pyramid: “The bottom layer – the foundational layer – is the data. Then you have information as the next layer; then you have knowledge; then you have wisdom and intelligence.”

The topic of AI will be increasingly prominent at cardiology meetings, Chang predicted in the ITN podcast: “I think within a year cardiologists will be talking about this fairly routinely at meetings, particularly ACC, because it is known for innovation.”

Greg Freiherr is a contributing editor to Imaging Technology News (ITN). Over the past three decades, Freiherr has served as business and technology editor for publications in medical imaging, as well as consulted for vendors, professional organizations, academia, and financial institutions.

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As a noninvasive technology, computed tomography (CT) is brimming with possibilities, particularly as an angiographic test for coronary artery disease (CAD).  But whether it should be used depends on the patient and surrounding circumstances, Rami Doukky, M.D., told podcast listeners of a on the Imaging Technology News website.

If the risk factors for CAD are low, coronary CT angiography (CTA) might rule out the presence of disease, thanks to the negative predictive value of the test, Doukky said on the podcast, which is hosted on the Imaging Technology News (ITN) and Diagnostic & Interventional Cardiology (DAIC) websites.  But when deciding whether to do the test, physicians should consider the relative capabilities of the institution to do CTA and functional testing.

“No matter how good the modality is, it is only as good as you can use it at your particular institution,” he said in the podcast. “So if, for example, your site has a very strong nuclear lab or echo lab and a weak CT lab …functional testing is the way to go for you.”

Headliner In A Great Cardiac Debate at ACC

In one of the “great cardiac debates” scheduled for Saturday, March 17 at #ACC19, this year’s annual meeting of the American College of Cardiology, Doukky will argue that coronary CT should not be routinely performed on every patient suspected of CAD. CT-based angiography should have a role, however, in the assessment of heart patients.

“I think cardiac CT is very well-suited for patients on the lower end of the spectrum with pretest likelihood of chronic disease,” he said in the podcast. “On the other hand, functional tests are better suited to patients who are in the intermediate to higher range of likelihood of disease.”

In a YouTube interview, Doukky, who not only leads cardiology at Cook County Hospital but is on the faculty of Rush University in Chicago, Illinois, said he “tends to offer patients what is right for them and not necessarily what is the most cool or what’s most expensive or what’s most modern.”

In the ITN/DAIC podcast, he said the Diamond-Forrester method can be used to determine a patient’s pretest likelihood of CAD.  Diamond-Forrester has been used successfully for decades to estimate the likelihood of coronary artery disease.

Functional tests have long been the mainstay for diagnosing CAD. “They have been the gatekeepers for coronary angiography,” Doukky said in the podcast.

One of the earliest such tests was exercise treadmill testing.  Others include stress echocardiography, which uses ultrasound to assess the heart wall for abnormalities, and myocardial perfusion with radioisotopes, initially with gamma cameras in planar imaging, then SPECT and most recently PET (positron emission tomography).

Coronary CT angiography became an option relatively recently. Doukky described this technique in the podcast as essentially an anatomic test that can determine stenosis and calcium build up in the coronary arteries.

Why Having Good Equipment Matters

Mitigating the choice of CTA, in addition to the patient’s likelihood of CAD, is the availability of a CT scanner capable of delivering the necessary performance.  The Illinois cardiologist said in the podcast that a 64-slice scanner is the bare minimum to perform coronary CTA. Although such tests might be formed with a scanner capable of as few as 16 slices per rotation, “a lot of patients cannot undergo these tests” because of the need for long breath holds, he said. Entry- and mid-level CT scanners typically are available in 16-, 64- and 128-slice versions.

Doukky gave two examples in the podcast – the first of a patient who would be well served by CTA; the second of someone who would not be.

“If you have a 40-year-old gentleman, let’s say, who has non-anginal type chest pain where the pretest likelihood of disease is at the lower end of the spectrum, coronary CTA could be a quick way to demonstrate that there is no evidence of obstructive disease,” he said in the podcast.   “On the other hand, if you have a 65-year-old gentleman or lady with typical anginal symptoms with a pretest likelihood of disease (being) fairly high, functional testing -- particularly with myocardial perfusion imaging  -- might be a better test.”

The ACC assigned Doukky to argue that coronary CT is not superior to functional testing in patients with suspected CAD.  He indicated in the podcast that coronary CTA is inferior to functional testing under many – but not all – conditions. The patient and surrounding circumstances should dictate the choice, he said. 

“That goes for any diagnostic testing -- not just cardiac diagnostic testing,” he said in the podcast.

Greg Freiherr is a contributing editor to Imaging Technology News (ITN). Over the past three decades, Freiherr has served as business and technology editor for publications in medical imaging, as well as consulted for vendors, professional organizations, academia, and financial institutions.

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Machine learning is already having an enormous impact on cardiology, automatically calculating measurements in echocardiograpy. And machine learning, which is part of artificial intelligence (AI), is just getting started.

“Machine learning (ML) is completely changing the landscape of echocardiography as we speak,” Partho P. Sengupta, M.D., told Imaging Technology News/Diagnostic and Interventional Cardiology (ITN/DAIC), before his presentation at an American College of Cardiology (ACC) session on the current and future applications of machine learning in echocardiography. “It has already been embedded in systems. It works seamlessly in the background without even people knowing about its existence. People just do their work and it takes care of interpretation.”

Sengupta, chief of the cardiology at West Virginia University in Morgantown, W. Va., is widely recognized as an expert in echocardiography. Before joining WVU, he directed interventional echocardiography and cardiac ultrasound research at the Zena and Michael A. Wiener Cardiovascular Institute at the Mount Sinai School of Medicine in New York City.

ML algorithms, also referred to as smart algorithms due to the AI on which they are based, are already having a major impact on the practice of cardiology, he said in the scientific session Saturday, March 16 at #ACC19, the 2019 annual meeting of the American College of Cardiology.

“Machine learning algorithms are being integrated into the routine workflow of these systems,” he said. “So they are already here.”

How Machine Learning Improves Echo

Embedded in modern echo systems, today’s smart algorithms make data interpretation more efficient, thereby improving the ability of physicians to diagnose rare conditions and problems, Sengupta said. Cardiac diagnostics often begin with echocardiography, he noted.  In recent years, echo has evolved to deliver imagery in multiple dimensions (e.g., 3-D and 4-D) and from smaller packages, epitomized by smart phone. Machine learning is the IT expression of this development.

Sengupta and colleagues concluded in a 2016 paper that machine-learning (ML)  algorithms may help standardize assessments and support the quality of interpretations.  Although “everybody benefits,” he said, from the availability of these algorithms, novice readers with limited experience have the most to gain.

“It takes away the knowledge burden required to diagnose a condition,” Sengupta said, noting that ML algorithms provide decision support “as good as a very senior physician.”

These algorithms may automatically calculate measurements such as ejection fraction. Some may even hint at the underlying anomaly. In the future, machine learning algorithms may be able to make calculations that indicate heart failure, stroke and atrial fibrillation– possibly with percentages indicating future risks, based on the patient’s phenotype, he said.

Predictive Analytics Changing Cardiology

“We already use some calculators in cardiology for estimating risk, but in the very near future, it will be even more precise and more individualized,” Sengupta said. “My take is that within the next couple of years you will see a total transformation of predictive analytics.”

Echo machines might forecast that a particular patient has “a 90 percent probability of having a heart failure admission in the next three days,” he said. Such predictions would allow clinicians to focus on high-risk patients.

In the more distant future, ML algorithms may automatically recognize clinical disorders, Sengupta said. But physicians will likely be given the option whether to accept these algorithms’ conclusions. Although embedded in the workflow of the echo system, ready to be executed, their conclusions will be “suggestions that physicians will have the ability to override, if they don’t agree with them,” Sengupta said.

While speculative, this view of the future is an extension of what can already be done and what will soon be possible.

Greg Freiherr is a contributing editor to Imaging Technology News (ITN). Over the past three decades, Freiherr has served as business and technology editor for publications in medical imaging, as well as consulted for vendors, professional organizations, academia, and financial institutions.

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Virtual reality (VR) and its less immersive kin, augmented reality (AR), are gaining traction in some medical applications. But a new entrant, “mixed reality,” may have the inside track in the interventional suite or operating room.

Mixed reality allows the user to interact with both the real world and digital data, according to Jennifer N. A. Silva, M.D., a pediatric cardiologist at Washington University School of Medicine in Saint Louis, Mo., who spoke during Future Hub at this year’s annual meeting (#ACC19) of the American College of Cardiology.

With ready access to both digital and physical reality, a surgeon or interventionalist can simulate a procedure on a digitally created model that is updated in real time – then perform the actual procedure on the patient.

“Mixed reality affords the ability to remain in your natural environment,” Silva told Imaging Technology News (ITN) and Diagnostic and Interventional Cardiology (DAIC). Yet the users of mixed reality can take advantage of a digital world otherwise found only in VR.

Mixed reality is somewhere in the middle of what Silva described as a spectrum of “extended realities.” At one end is virtual reality (VR), which completely immerses the user in a digital world. Because VR is a completely separate and alternative reality, it “does not allow you to have a meaningful interaction with your natural environment,” she said.

At the other end of the spectrum is reality, characterized by what we sense with our eyes.  In between these two ends are augmented reality (to which mixed reality is closely related). In AR, the user cannot interact with but rather only look at digital data imported into the natural environment, Silva said.

“Mixed reality allows you to have a meaningful interaction with digital data and with your natural environment,” she said. “So instead of just looking at a hologram, you can do something to it -- you can touch it; rotate it; turn it upside down.”

Fitting Round Pegs Into Round Holes

The technology – and the type of reality it harnesses – “has to fit what you are trying to do,” she told ITN and DAIC. “There has to be a match between the technology and the need.”

Different types of reality may be useful in one or more of the four major application areas: education, pre-procedural planning, rehabilitation and intraprocedural visualization. VR offers little value during procedures, because it immerses the user in a completely digital world and, therefore, blocks out the natural environment. For this same reason, however, VR may be useful in rehabilitation, as it may replace the user’s perception of the real world with a digital one.

VR may also be useful when practicing a surgical or interventional procedure. Augmented reality has proven useful in actual interventions, presenting data that a surgeon or interventionalist can use during the actual procedure. But mixed reality offers the ability to interact with digital data and with the real world in the same context and time frame.

“If I’m doing a procedure, I can’t fully remove myself from that environment (to interact in VR). I need something that will allow me to interact with my natural environment and with the digital data,” said Silva, who was one of the moderators during March 16 ACC Future Hub session titled “Are Clinical Virtual Reality and Augmented Reality Ready for Prime Time?”

“The ability to manipulate and control your data is perhaps the single greatest value add of the extended realities,” she told ITN and DAIC. Theoretically, mixed reality allows this manipulation and control.

“We are on the first step of what I think will be a long road,” said Silva, who is one of the founders of a mixed reality company, called SentiAR.

Silva and her colleagues right now are struggling to achieve connectivity among the different pieces of equipment found in an EP interventional lab. Examples of this equipment are fluoroscopy systems, vital signs monitors and cardiac mapping systems. “A lot of them are made by different vendors, so it lends itself to poor interoperability,” she said. The goal is to integrate the data from the equipment, as well as their workflows.

“I want to make the platform the glue that starts to allow me to interact with all my pieces of equipment,” she said. 

How Mixed Reality Might Impact EP

Mixed reality may prove particularly useful during electrophysiological procedures, when interventionalists map electrical activity inside the patient’s heart, then ablate tissues associated with electrical signals that cause arrhythmia. The device being developed by SentiAR creates a 3-D model of the patient’s heart that shows the map of electrical signals in real-time, said Silva who specializes in pediatric cardiology. The mixed reality platform being developed at SentiAR can be used on patients of any age, she noted.

The user can see 3-D images and other digital data, as well as the physical world, by looking through a head-mounted technology called HoloLens, which is made by Microsoft.  Silva described it as “a computer on your head” that allows the user to see digitally created structures superimposed on a view of the natural environment.  Simultaneously, sensors built into the head-mounted technology record head movements that allow the user to click through digital menus projected onto the user’s field of vision or to control different pieces of equipment in the interventional lab.

Although HoloLens has proven useful, the company is not inextricably tied to the Microsoft technology. Instead, the SentiAR is keeping its options open to take advantage of new technologies that may come along. “There are many mixed reality headsets out there, and there are many more in development,” she said. 

Silva said the product being developed by SentiAR is a long way from finalization. “As good technology does, it will continue to evolve,” she said, noting that future technologies may harness new types of reality that represent different points on the “extended realities” spectrum.

“This (spectrum) is where innovation is going to happen.”

Greg Freiherr is a contributing editor to Imaging Technology News (ITN). Over the past three decades, Freiherr has served as business and technology editor for publications in medical imaging, as well as consulted for vendors, professional organizations, academia, and financial institutions.

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As many as one in four patients who undergo cath lab interventions can benefit from a technology that identifies the location of stenoses not resolved by initially performed percutaneous coronary interventions (PCIs) to relieve restricted blood flow.

According to results from an international study presented during the annual meeting (#ACC19) of the American College of Cardiology, as many as 25 percent of patients undergoing coronary interventions have unresolved stenoses in their coronary vessels. These stenoses may be caused by lesions that can be localized using a technology called iFR (instantaneous wave-free ratio, also referred to as instant wave-free ratio or instant flow reserve).

Invented by Justin Davies, Ph.D., a consultant cardiologist in the Division of Cardiology Cardiothoracic and Thoracic Surgery at Imperial College Healthcare NHS Trust in London, the iFR technology is already being used in cath labs around the world to determine the severity of coronary lesions. The international study, dubbed DEFINE PCI, whose results were presented at #ACC19, indicates that this technology can also be leveraged to determine the locations of stenoses that remain after initial PCI.

Use of the iFR technology for this purpose adds time to the intervention, but the patient deserves the extra effort, Davies said. “Doing the very, very best for our patients is really important,” he said, particularly considering that these patients have undergone the inconveniences and the radiation associated with PCI.

How iFR Can Be Used To Find Residual Lesions

Pressure sensors located at different points along the iFR wire may show differences in pressure.  These differentials may be apparent, when the wire is pulled back from the initial treatment site.  If the pressure drops, the interventionalist knows that residual stenosis lies somewhere between the two pressure sensors, Davies explained. The Philips Healthcare SyncVision iFR Co-registration maps the pressure readings onto the angiogram.

Patients may undergo PCI after complaining of angina, which may be caused by severe blood flow restrictions in the coronary arteries. The stenoses associated with angina reduce blood flow, causing ischemia in heart tissue. Under interventional X-ray guidance, clinicians maneuver a balloon catheter and coronary stent to the treatment area. These are then deployed in efforts to restore arterial blood flow.

“The pressure wire pullback (identifies) where in the artery there are still potential zones where we could be treating,” Davies told ITN and DAIC in an interview at Philips’ booth on the ACC exhibit floor.

Latest Study Identifies Further Value Of iFR

Conducted at major U.S. and European medical centers, the DEFINE PCI study showed that the positive effects of coronary interventions in patients suffering from coronary artery disease may be limited by residual lesions. The study suggests that patient outcomes might be improved if the locations of these residual stenoses could be determined.

Philips, which sponsored the DEFINE-PCI prospective study of 500 patients, has licensed the iFR technology from the Imperial College, Davies said. The study, which determined the utility of iFR readings in identifying the locations of these lesions, was conducted “to the very highest levels of integrity. I feel very confident to stand by the results,” he said, noting that the study was conducted by the U.S.-based Cardiovascular Research Foundation and involved prestigious medical facilities including those at the Imperial College in the United Kingdom, as well as Duke Clinical Research Institute and Columbia University Medical Center, both of which are in the U.S.

The DEFINE-PCI study also confirmed the results of past studies, namely the iFR-SWEDEHEART and DEFINE-FLAIR trials, which had determined that a substantial minority of patients undergoing cardiac cath interventions do not benefit as much as they could.  Results from the most recent study underscored the findings of these previous studies, emphasizing “that there is further room for improvement,” Davies said.

“What this means is that we could get even better blood flow down the vessel; we could have even better relief of angina symptoms,” he continued. “Ultimately the patient will be the big winner from this.”

Greg Freiherr is a contributing editor to Imaging Technology News (ITN). Over the past three decades, Freiherr has served as business and technology editor for publications in medical imaging, as well as consulted for vendors, professional organizations, academia, and financial institutions.

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Reflecting a trend toward the increased use of computed tomography (CT) in cardiology, Siemens Healthineers launched a CT scanner optimized for cardiac scanning at the American College of Cardiology conference (#ACC19). The new Siemens’ CT, a version of its Go platform, follows GE Healthcare’s launch two years ago of the CardioGraphe, a CT scanner that GE highlighted at this year’s ACC conference.

Both systems are intended primarily for sale to outpatient centers. Compact and optimized for cardiac application, both are priced under $800,000, according to sources at the companies.

Results of the ADVANCE study, which were released in the closing hours of ACC19, emphasized the undercurrent that had flowed through ACC scientific sessions and across the exhibit floor — that the utility of CT was increasing within cardiology. The one-year outcome from the ADVANCE registry for CT-based fractional flow reserve (FF) “show low rates of events in all patients, with less revascularization and a trend toward (a lower incidence of major adverse cardiac events) and significantly lower cardiovascular death or MI in patients.”

This means patients can use CT to determine if they have a low risk of adverse cardiovascular events. And the CT-based results will apply for as long as one year. The implication is that patients can safely skip invasive testing achieved through cardiac catheterization during that period.

Siemens Cardiac CT On ACC Exhibit Floor

Introduced on the ACC.19 exhibit floor and available for sale April 1, Siemens’ cardiac system is built on a platform initially unveiled at RSNA 2017. Siemens’ go.Top CT platform was cleared for marketing in the U.S. in early 2018 and has been in production ever since. “Because the production line exists today, there will be no delay in delivery (after sales of the cardiovascular edition begin April 1),” said Matthew Dedman, Siemens director of CT product marketing.

The CV version, shown for the first time at ACC.19, features applications designed exclusively for cardiological scanning, according to Dedman. 

“Historically cardiac CT was limited to the hospital environment because the technology had been expensive,” he said. By contrast, the CV-optimized go.Top allows “low total cost of ownership of our go platform and enables a high quality CT to be installed and utilized in the outpatient environment. This increases access to very valuable procedures, such as cardiac DT and additional FFR analysis.”

The 128-slice scanner is built particularly for outpatient cardiology offices, but might be installed in hospitals, as well, according to Dedman, who emphasized the ability of the scanner to gather data for coronary CTA, as well as for advanced tests, including CT-based FFR analyses by HeartFlow. Siemens developed a technical solution for the transmission of CT data from go.Top cardiovascular edition to HeartFlow, said Dedman, “for them to perform their analysis and transmit the results back to the physician.”

The system maintains, however, “the full radiological capability of the underlying go.Top,” he said. “So your traditional neuro, chest, abdomen and pelvis imaging could be performed on this machine as well.”

The tablet mounted on the side of the go.Top gantry allows technologist to set up scans from the CT table sides. This enables the tech to stay close to the patient — to help keep the patient calm and to achieve a low heart rate. “We know that probably the biggest factor in the success of the exam is patient cooperation,” Dedman said

The company’s CARE kV feature balances radiation dose and image contrast, automatically selecting the optimal kV setting for patients in increments of 10kV. In real-time, the system’s Check&GO algorithm helps identify problems with anatomical coverage and the distribution of media contrast.

GE Showcases Dedicated Cardiac CT At ACC

Although GE’s CardioGraphe was unveiled at an ACC meeting two years earlier, the company focused attention again on the CT, noting its dedication to cardiology and cost-effectiveness. “It is designed to serve the needs of the cardiologist and the radiologist who does cardiac CT,” Philippe Karam, GE’s global sales and marketing director, said. “We took all the features needed for true cardiac CT and put them in this scanner.”

The CardioGraphe, which evolved from a strategic partnership between GE Healthcare and Arineta LTD, was “built from the ground up,” Karam said. The compact system can be installed in 15 square meters. Dose efficiency is achieved using GE’s ASiR-CV. And it can be used to plan procedures including TAVR (transcatheter aortic valve replacement), according to Kira Behrens, GE’s director of premium CT for the U.S. and Canada.

The system is built for point-of-care scanning in an emergency department, a clinic (such as “a large interventional lab,” Behrens said, “where nuclear tests are frequently done”) or even a physician’s office, according to the company. In a single heartbeat, the CardioGraphe can create a 3-D image of the coronaries, valves, chambers and myocardium, as well as an angiogram of the aorta. Rotation speed is 0.24 seconds; single-beat heart coverage 140 mm, resolution 0.28 mm, according to the company.

Greg Freiherr is a contributing editor to Imaging Technology News (ITN) and Diagnostic and Interventional Cardiology (DAIC). Over the past three decades, Freiherr has served as business and technology editor for publications in medical imaging, as well as consulted for vendors, professional organizations, academia, and financial institutions.

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April 2, 2019 — Here is the list of the most popular content on the Imaging Technology News (ITN) magazine website from the month of March 2019. This is based on the website’s 200,349 pageviews for the month:

1. VIDEO: The Role of the Physicist in Proton Therapy — Interview with Mark Pankuch, Ph.D.

2. Carestream Health To Sell its Healthcare IT Business To Philips

3. How AI Can Unlock Data in CT and MRI Scans

4. FDA Grants Breakthrough Designation to Paige.AI

5. 360 View of the Inclined Beam Room at the Northwestern Medicine Chicago Proton Center

6. Six Key Health Information Technology Trends at HIMSS 2019

7. VIDEO: Managing a Multi-site Radiology Practice With AI-based Workflow — Interview with Andrew Deutsch, M.D.

8. Siemens Joins GE with Launch of Cardiac CT Scanner

9. What to Expect from the Proton Therapy Market in 2019-2020

10. Surgery Versus Radiation Therapy in Non-small Cell Lung Cancer

11. FDA Proposes New Rules for Mammography Reporting and Quality Improvement

12. New Consensus Document Explores Ethical Use of AI in Radiology

13. Canon Medical Systems introduces 33 MHz Ultra-High Frequency Ultrasound Transducer

14. Siemens Healthineers Announces First U.S. Install of Magnetom Sola 1.5T MRI

15. Siemens Healthineers Announces First U.S. Install of Biograph Vision PET/CT

16. Canon Medical Introduces Deep Learning-Based CT Image Reconstruction

17. Three Resolutions Worth Keeping for a More Data-driven Radiology Practice

18. Augmented Reality is Taking Over Radiology Training

19. Non-Contrast MRI Effective in Monitoring MS Patients

20. VIDEO: Economics of Proton Therapy — Interview with Bill Hartsell, M.D.

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May 1, 2019 — Here is the list of the most popular content on the Imaging Technology New (ITN) magazine website from the month of April 2019. This is based on the website’s 186,476 pageviews for the month:

1. FDA Clears GE's Deep Learning Image Reconstruction Engine

2. VIDEO: Editor’s Choice of the Most Innovative New Artificial Intelligence Technologies at RSNA 2018

3. Interventional Radiology Treatment for Tennis Elbow Reduces Pain and Inflammation

4. Is Artificial Intelligence The Doom of Radiology?

5. How Artificial Intelligence Could Affect Breast Cancer Screening

6. VIDEO: A Discussion on Proposed FDA Rules for Mammography Reporting — Interview with Wendie Berg, M.D.

7. Advances in Neuro-oncology

8. Artificial Intelligence Helps Detect Breast Cancer and Saves Time

9. Artificial Intelligence Performs As Well As Experienced Radiologists in Detecting Prostate Cancer

9. Varian Discloses First Preclinical Results of Flash Therapy in Cancer Treatment

10. AI Algorithm Detects Breast Cancer in MR Images

11. VIDEO: Artificial Intelligence in Radiology — Are We Doomed? — Interview with Rasu Shrestha, M.D.

12. Development of Artificial Intelligence in Women’s Health Emphasizes Value

13. How Risk Stratification Might Affect Women’s Health

14. FDA Proposes New Rules for Mammography Reporting and Quality Improvement

15. Uterine Fibroid Embolization Safer and as Effective as Surgical Treatment 

16. Radiology Publishes Roadmap for AI in Medical Imaging

17. Siemens Joins GE with Launch of Cardiac CT Scanner

18. VIDEO: Advances in MRI Technology — Interview with Max Wintermark, M.D.

19. Using Artificial Intelligence to Reduce Gadolinium Contrast 

20. PACS Software Provides Follow Up on Mammo Recalls and Biopsy

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May 8, 2019 – United Imaging Healthcare (UIH), an international leader in advanced medical imaging and radiotherapy equipment, announced its U.S. Food and Drug Administration (FDA) clearance of the uPMR 790 HD TOF PET/MR. uPMR 790 redefines clinical routine imaging for PET/MR with the capability to scan a whole body within 20 minutes, balancing patient comfort with high-quality imaging. With the uPMR 790, United Imaging Healthcare demonstrates its commitment to advance precision medicine in the fields of neurology, oncology, and cardiology.

The next generation platform of the uPMR 790 delivers state-of-the-art PET and MR performance that rises above the current technology standards. The HD TOF PET platform is based on digital silicon photomultipliers (SiPMs) and lutetium-yttrium oxyorthosilicate (LYSO) crystals, offering a 2.8 mm NEMA PET spatial resolution with time-of-flight (TOF) technology and a large 32cm axial field-of-view. When this technology is combined with compressed sensing for whole body isotropic 3D MR imaging, it results in fast simultaneous whole-body PET and MR scans. This technology redefines PET/MR imaging by offering clinicians high isotropic spatial resolution to visualize small lesions while accelerating acquisition times to maximize patient comfort.

uPMR 790 also offers cutting-edge imaging for research, including theranostics and neuroscience which benefit from an increase in sensitivity, resolution, and coverage. United Imaging Healthcare’s uSync Research platform enables additional opportunities for research in areas such as simultaneous tracking of PET and MRI tracers, real-time cardiac PET/MR, functional neurological PET/MR, and multi-parametric radiometrics.

“The uPMR 790 sets a new standard for simultaneous PET and MR performance by not only enhancing the precision and resolution of scans, but by also dramatically changing the patient experience,” said Jeffrey M. Bundy, Ph.D., Chief Executive Officer of UIH Solutions. “Today’s announcement again delivers on our mission to embed breakthrough innovation across our portfolio. The uPMR is yet another proof point of our commitment to provide more patients in the United States with access to advanced technology and higher standards of care.”

For more information: www. usa.united-imaging.com

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May 8, 2019 — Carestream introduced its ImageView Software Platform Windows 10 operating system to deliver enhanced security. ImageView software was first introduced with the Carestream OnSight 3D Extremity Imaging System and will be further expanded across Carestream’s entire portfolio, including rooms, retrofits and additional mobile imaging X-ray systems in the future.

“ImageView software delivers an intuitive interface and consolidated screen views to boost productivity as well as new capabilities that can improve both workflow and security,” said Jill Hamman, Carestream’s Worldwide Marketing Manager for Global X-ray Solutions. “This software uses Eclipse, our advanced image processing engine, to deliver exceptional image quality and enhanced diagnostic confidence, while providing a foundation for new applications in the future.”

The software platform supports image processing and workflow capabilities including: 

• Enhanced Visualization Processing Plus software that delivers multi-band frequency processing to provide better noise control, sharpness, contrast and density while minimizing artifacts;
• Tube and Line Visualization that uses a companion image created from the original exposure with optimized processing for clearer, easier visualization of PICC lines and tubes to help increase confidence that tubes and lines are placed correctly;
• Pneumothorax Visualization that uses a companion image created from the original exposure to accentuate the appearance of free air in the chest cavity;
• Bone Suppression software that uses a companion image created from the original exposure to reduce the appearance of bone and enable better visualization of soft tissue;
• Pediatric Image Optimization and Enhancement software that acquires default acquisition techniques and image processing parameters optimized specifically for each patient's body size, from the smallest neonatal patient to the largest adolescent;
• SmartGrid software that provides image quality comparable to images acquired with an anti-scatter grid and offers reduced patient dose for bedside chest imaging; and
• Access to RIS and PACS platforms that streamlines exam completion for increased productivity.

ImageView software also provides the ability to manage an imaging department’s productivity and quality. The Administrative Analysis and Reporting option can help improve performance with a digital dashboard that allows users to track average exposure rates by technologist, rejected images with reasons and other statistics including detector drops. The Total Quality Tool package provides objective quality control image tests and collection of detector performance data.

For more information: www.carestream.com

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The DigitalDiagnost C90 is Philips newest premium digital radiography (DR) system, introduced here at the Radiological Society of North America (RSNA) 2018 meeting. It is the industry’s first radiography unit with a live camera image directly displayed at the tube head to provide a clear view of the anatomical area being scanned during the patient positioning process. It helps clinicians be confident that the right area is captured with a low X-ray dose exposure. The system also incorporates Philips’ UNIQUE 2 image processing and Riverain Technologies’ ClearRead Bone Suppression software to allow clearer images with a lower chance of a costly and timely rescan. The system was cleared by the U.S. Food and Drug Administration (FDA) in February 2019. 

This X-system was featured in the VIDEO: Editor's Choice of the Most Innovative New Technology at RSNA 2018.

Read more about the system.

The integration of artificial intelligence (AI) into medicine has by far been the hottest topic at nearly all medical conferences for radiology, cardiology and several other subspecialties over the past two years. This has led to the creation of an organization AIMed to help spearhead collaboration and dissemination of information on AI developments to speed its adoption. 

The 2019 AIMed Radiology/Cardiology conference in June is in Chicago is aimed at those interested in the use of artificial intelligence (AI) and deep learning in advanced medical imaging. The conference also allows attendees to experience the future of radiology using augmented and virtual reality and to understand the future of medical education and training with AI. 

The conference is June 18-19, 2019, at the Ritz-Carlton Chicago. The event is expected to draw 1,800 attendees, 350 speakers and about 1,500 companies, including start-ups and some of the key players bringing AI into Healthcare.

This is the largest event of its type in the United States focused on AI in medicine. The conference offers two days dedicated to the transformative impact that AI-inspired technology is having on radiology. Artificial Intelligence has the power to transform radiological and cardiology practice. AIMed said the benefits of machine learning and deep neural networks in identifying disease earlier and more accurately will greatly aid patient care. The integration of this technology at scale is, however, more challenging. AIMed said some key questions it hopes to answer is how do we invest in building the necessary frameworks to validate these tools in the clinical setting? How do we ensure we are working in unison as an industry and not in a siloed and duplicative fashion? And, how do we ensure we maintain physician oversight/domain expertise when these tools are being developed?

AIMed is a collaborative organization that aims to bring together radiologists, hospital leaders and technology experts so they can start a revolution in today’s medicine and healthcare for a data-smart tomorrow. In order to achieve that goal, AIMed is are constantly on the look out for anyone moving the needle in this field.

The event is targeting radiologists, hospital leaders, data scientists and Health AI innovators.

For more information on the event: https://aimed.events/aimed-radiology-2019/

Registration for the event: https://aimed.eventscase.com/attendance/event/index/34570/EN

Event Agenda: https://aimed.events/wp-content/uploads/2019/02/AIMed-Radiology.pdf

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