Tag: radiologists

CT Lung Screening Leads RSNA’s First Day

Posted on by Brian Casey

Day 1 of RSNA 2025 is in the books, and new research into CT lung cancer screening dominated the scientific sessions at Chicago’s McCormick Place.

Lung cancer screening is drawing attention as screening programs go into effect internationally.

  • In the U.S., lung screening is hampered by low completion rates (18-19%), but providers are finding that participation can be improved with aggressive identification and outreach to eligible patients.

Some highlights from Sunday (with handy session numbers to help you follow along) include…

  • The ScreenLungNet AI model predicted three-year lung cancer risk from CT lung screening scans with AUCs from 0.93-0.94 (S4-SSCH02-1).
  • In a study of 2.6k patients with lung cancer, only 36% met 2021 USPSTF lung cancer screening criteria, and just 5% actually got screened. Only 23% had data on their smoking history in the EMR (S4-SSCH02-2).
  • Risk assessment scores were used to perform CT lung screening of lower-risk people every two years rather than annually, reducing screening’s harms without missing many cancers (S4-SSCH02-4).
  • Compared to the landmark NLST study, a real-world CT lung screening program had fewer benign surgeries (12% vs. 18%), lower complication rates (24% vs. 32%), and better recurrence-free survival (HR = 0.60) (S4-SSCH02-5).
  • CT radiation dose was reduced 51% and contrast iodine use 61% through a triple-optimized protocol that included 80-kVp scanning, GE HealthCare’s TrueFidelity deep learning reconstruction, and low-iodine adaptive contrast injection (S2-SSCA01-1).
  • Using AI for automated patient positioning and scan range in CT exams cut positioning time 41% with 10-13% lower radiation dose and no discernible impact on image quality (S4-SSIN01-1).
  • Measures of adiposity acquired opportunistically from coronary artery calcium CT scans using HeartLung Technologies’ AI-CVD algorithm predicted adults at risk of diabetes in a study of 2.9k people (S5-SSCA02-6). 
  • The Promedius AI algorithm for osteoporosis assessment of chest radiographs had an AUC of 0.84 in a study of 1k adults from three countries (M3-SSCH03-1).
  • A real-world study of 2.1k patients found that DeepTek’s chest X-ray AI algorithm had an AUROC of 0.95 for detecting any of 13 clinically significant findings (M3-SSCH03-2).
  • Researchers presented a feasibility study of a compression-free spectral DBT mammography system, finding spatial resolution close to state-of-the-art systems (S4-SSPH02-6).
  • Researchers presented their protocol for MRI scanning of patients with cardiac implanted electronic devices. Over 10 years they scanned 7.3k patients with no major adverse events (S5-SSCA02-1).
  • Adding MRI data to a multimodal transformer AI model improved its ability to predict five-year breast cancer risk in intermediate- and high-risk women (S2-SSBR01-6).

The Takeaway

RSNA 2025 is off to a great start. Be sure to check back with Thursday’s newsletter for more radiology news from Chicago, and follow along on our social media channels for ongoing video updates. 

GE to Buy Intelerad in Massive $2.3B Acquisition

Posted on by Brian Casey

In what could be the biggest radiology IT acquisition in years, GE HealthCare will acquire medical image management software company Intelerad in a purchase valued at $2.3B. The acquisition will bolster GE’s position in the outpatient image management segment, which is rapidly shifting from on-premises PACS models to cloud-based environments.

Intelerad was founded in Montreal in 1999 as a PACS developer and has grown through acquisitions of its own in recent years.

  • U.K. private equity firm Hg took a controlling interest in Intelerad in 2020, and the company soon embarked on a series of acquisitions that rolled up smaller imaging IT companies like Digisonics (2020), Ambra Health (2021), Insignia (2021), Lumedx (2021), Life Image (2022), and PenRad Technologies (2022). 

After taking a few years to digest the new companies, Intelerad began focusing on moving its technology and customers to cloud-based architecture, such as by releasing a cloud-native version of its InteleHeart software and by moving its PACS, VNA, and image-sharing applications to AWS cloud hosting.

GE needs no introduction, of course, but the company clearly sees the attraction of Intelerad’s core market in outpatient imaging, which complements GE’s focus on larger hospitals and health systems. 

In a conversation with The Imaging Wire, Scott Miller, president and CEO, Solutions for Enterprise Imaging at GE HealthCare, explained several of the acquisition’s advantages …

  • Imaging exams are moving from hospitals to outpatient centers due to lower costs.
  • Outpatient facilities are following hospitals in moving their data to the cloud, putting Intelerad at the intersection of two major trends.
  • Intelerad’s geographic focus has been on English-speaking countries, giving GE the opportunity to plug Intelerad products into its international distribution network. 

GE estimates that Intelerad will generate $270M in revenue in its first full year under GE ownership. 

  • Intelerad’s sales have been growing at a rate in the low double digits, and GE expects that pace to accelerate. 

Is the new acquisition a sign of growing consolidation in the radiology AI and image management sectors? 

  • Other recent purchases in 2025 include Radiology Partners’ purchase of Cognita Imaging, Lunit’s acquisition of Prognosia, and GE’s own purchase of icometrix, completed earlier this month. RadNet also acquired iCAD earlier in the year.

The Takeaway

GE’s acquisition of Intelerad offers multiple benefits to the multimodality OEM, from Intelerad’s presence in the outpatient imaging sector to its experience in cloud-based image management and broad product portfolio. The question is whether the purchase spurs other big iron vendors to answer with acquisitions of their own. 

Next-Generation AI Platform Redefines Radiology Workflow Standards

Posted on by Partner Content

AI is no longer being viewed as a diagnostic aid but as essential medical infrastructure. Nowhere is that more apparent than in lung screening, with Germany and other European Union countries increasingly embedding AI into their lung cancer screening guidelines and pilot programs.

This evolution will be on display at RSNA 2025, where Coreline Soft will introduce its groundbreaking chest AI platform AVIEW 2.0.

  • The solution demonstrates how unified AI automation is fundamentally transforming radiology workflows and elevating diagnostic precision across pulmonary, cardiac, and airway pathologies.

AVIEW 2.0 represents a paradigm shift from task-specific tools to an integrated diagnostic ecosystem. 

  • The platform seamlessly combines lung-cancer screening (LCS), coronary-artery calcium (CAC) scoring, and COPD quantification into a single, continuous analytical pipeline. 

Clinical validation shows radiologists using AVIEW 2.0 achieve 89% increase in case throughput and 60% reduction in interpretation time compared to the previous generation. 

  • This effectively consolidates multi-disease CT assessment into one streamlined, automated workflow.

AVIEW’s clinical foundation extends far beyond pilot studies. The platform has processed over 2.5M cases across 19 countries, establishing itself as a proven solution in diverse healthcare ecosystems. 

  • Most notably, AVIEW has been selected as the AI platform for major government-led lung cancer screening pilots and programs in Germany, France, and Italy.

Beyond Europe, AVIEW solutions are already integrated into major U.S. medical centers, where their clinical reliability has been independently validated in real-world settings…

  • UMass Memorial Medical Center has deployed the system as an integrated platform for LCS, CAC, and COPD diagnosis, supporting full-spectrum thoracic screening in daily radiology operations.
  • Temple Lung Center, 3DR Labs, and ImageCare Radiology have incorporated AVIEW products into their research and diagnostic environments – each adapting AI functions to site-specific workflows and physician preferences.

SOL Radiology, a fast-growing radiologist-owned practice serving communities across California and Illinois, has deployed AVIEW LCS Plus across its outpatient centers and hospital network, leveraging the platform for high-confidence nodule detection, rapid turnaround, and integrated COPD/CAC assessment. 

  • The group reports significant gains in diagnostic efficiency and consistency within one week of implementation, supporting its vision for technology-driven, high-quality community radiology.

With national-scale validation in Europe, clinical adoption across top-tier U.S. institutions, and 2.5M cases processed globally, Coreline Soft is positioning AVIEW 2.0 as the new benchmark for AI-driven thoracic imaging – where efficiency, accuracy, and scalability converge.

The Takeaway

Coreline Soft will conduct an end-to-end AI workflow demonstration in the “Radiology Reimagined” demo zone at RSNA 2025, using real-world clinical scenarios. With AVIEW and HUB, the full pathway – from triage and interpretation to reporting and quality management – will be validated against standards such as IHE and FHIR, allowing attendees to experience integrated flow firsthand. Learn more or book an appointment on Coreline Soft’s website.

RP Acquires Vision AI Firm Cognita Imaging

Posted on by Brian Casey

Radiology Partners ramped up its investment in AI by acquiring Cognita Imaging, a startup that’s developed AI vision language models for analyzing CT and X-ray images and drafting initial radiology reports. RP executives see the acquisition as going beyond traditional point-source AI models and toward a future where AI automates much of the traditional image interpretation process.

The $80M acquisition expands on an equity stake RP already had in Cognita, which had been operating in stealth mode since its spin-off from Stanford University’s Center for Artificial Intelligence in Medicine and Imaging lab.

  • Cognita was formed by a team led by CEO Louis Blankemeier, PhD, to commercialize Stanford research on vision language models, a type of generative AI that’s far more versatile than the traditional point-source models being commercialized to analyze medical images.

Instead, Cognita’s technology is able to analyze text as well as CT or X-ray images and produce first drafts of radiology reports that just need a radiologist’s review and signature to be complete.

  • Extremely positive clinical tests with Cognita’s VLM models spurred RP to acquire the rest of the company it didn’t already own, said Rich Whitney, chairman and CEO of Radiology Partners. 

Cognita’s technology powers Mosaic Drafting, RP’s new application for helping radiologists draft reports that operates under the company’s recently launched Mosaic Clinical Technologies branding. Early clinical testing has found that Mosaic Drafting…

  • Increases radiologist detection rates by 52%.
  • Results in a fourfold decline in radiologist errors.
  • Reduces radiologist reading times by up to 76%.

RP plans to deploy Mosaic Drafting through Mosaic Clinical Technologies, which the company launched in July as the technological foundation for a massive rollout of AI across its physician practices. 

  • Mosaic Chief Medical AI Officer Nina Kottler, MD, said Mosaic Drafting is currently being used within Radiology Partners under IRB approval, but the company will pursue an FDA authorization – most likely under a de novo pathway – that probably will come sometime in 2026.

In a broader sense, RP sees Mosaic Drafting and other VLM tools as key to the growing mismatch between rising imaging volume and stagnant radiologist supply – a mismatch that can only be solved through greater automation. 

  • And as the largest private radiology organization in the U.S., Radiology Partners has the organizational heft to make VLMs work on a wide scale.

The Takeaway 

RP’s acquisition of Cognita is a major development in putting vision language models on the fast track to real-world clinical use. Unlike point-source AI, VLMs could hold the key to really solving radiology’s volume overload dilemma.

An All-in-One Radiology Platform Built for the AI Era

Posted on by Partner Content

Early in the COVID pandemic, software engineer Shiva Suri found himself working from home alongside his radiologist mother in his parents’ basement. What he saw would lead him to build New Lantern, an AI-native platform set to disrupt the legacy radiology software market.

Suri witnessed his “world-class radiologist” mom wasting far too much time switching between five different PACS platforms and repeating the same cumbersome reporting processes with each case.

“I thought a radiologist’s job was supposed to be playing Sherlock Holmes in images,” Suri recalls, “not constantly mouse-clicking all over their PACS and tab-dictating endlessly in their reporting software.”

That imperfect workflow is an unfortunate reality for today’s radiologists, who’ve seen their processes become more tedious, while their caseloads grow in both volume and complexity.

Rads Don’t Need Another Widget

Suri’s time spent working from home became the foundation for New Lantern’s bold mission:  keep radiologists’ eyes on their images and let AI do the rest. 

  • That mission evolved over time, as Suri’s first attempt at solving radiology’s efficiency problem was a widget to automate report impressions.
  • Radiologists loved it, but… each wave of praise came with requests for more automation, leading Suri to realize that radiology’s problems weren’t going to be solved with another widget. The solution had to be fundamentally different.

The Time Is Right for an All-in-One Solution

Developing radiology’s go-to reading and reporting platform had to start with radiologists’ dream state, with their eyes on the viewer, reading image after image. 

  • It had to be based on the understanding that this dream can’t be achieved while radiologists are navigating a loosely integrated software stack.
  • The good news is, now is the perfect time to solve radiology’s software problem. The radiologist shortage and surging imaging volumes are finally driving radiology practices to look for new tech partners, and the emergence of generative AI is allowing startups to gain traction in segments that have long been dominated by entrenched legacy players. 

Enter New Lantern Curie

This perfectly timed mix of tech and market readiness set the stage for Curie, New Lantern’s all-in-one platform that combines a smart worklist, cloud PACS viewer, and AI reporter to produce AI-automated radiology report drafts.

Radiology report automation is no small task, and there’s a lot that goes into Curie’s ability to automate over 75% of non-diagnostic radiology work…

  • Streamlined Dictation – Radiologists free-dictate positive findings (no punctuation or commands), and the AI weaves them into complete sentences, generates guideline-based impressions (calculating BI-RADS, etc.), and flags errors.
  • No Tech Translations – Curie uses OCR technology to decipher technologist worksheets, applies clinical context via an LLM, and intelligently places data in the right report sections.
  • Remove Repetition – Radiologists no longer need to dictate measurements or enter prior dates. Curie handles these and a long list of other duplicative tasks for them.

The Numbers Tell the Story

All of these automations really add up, giving radiologists over 100 minutes back per shift, so they can get more done and get their lives back.

Here’s one real-world example presented at SIIM 2025 of a radiologist’s process for reading a pulmonary embolism CTA chest exam, before and after Curie…

  • Words dictated — 205 vs. 57
  • Punctuation marks & commands — 19 vs. 0
  • Fields navigated — 32 vs. 1
  • Metadata entries — 8 vs. 0 

In this example, Curie produced the same complete, accurate report with 72% fewer dictated words and 97% less navigation through dictation fields and hanging protocol changes. That’s one type of “AI taking radiologists’ jobs” that just about every radiologist would welcome.

The Takeaway

As imaging volumes surge and antiquated platforms push radiologists to the breaking point, New Lantern Curie offers them a way to work like it’s 2025 instead of 2005 – automating the fragmentation and duplication out of their days so world-class radiologists like Shiva Suri’s mom can focus on what they do best: reading images.

Learn more about New Lantern and its all-in-one approach to radiology workflow in this Imaging Wire Show video interview. 

Mammo Screening Deserts Limit Access

Posted on by Brian Casey

It’s no secret that there are sharp regional differences in healthcare access in the U.S. But a new report puts a price on the access problem as it pertains to mammography – nearly 10k additional cases of breast cancer a year due to limited access in “cancer screening deserts” that don’t have mammography equipment. 

Mammography has been a success story among population-based cancer screening tests. 

  • The widespread implementation of breast screening in the 1980s is generally credited – along with improved treatments – with reducing breast cancer mortality by 44% from 1982 to 2022.

But breast cancer is still a lethal disease, killing 42k women a year in the U.S.

  • And screening’s benefits have not been distributed equally, with women in rural areas and those with lower socioeconomic status having lower completion rates.

What would it take to even out the differences? To answer this question, researchers from the Milken Institute analyzed the U.S. mammography installed base at the county level. 

  • They then correlated machine distribution with county population as well as cancer detection rates to find out how efficiently different counties were performing. 

They discovered…

  • High regional variation in mammography machine distribution.
  • The lowest distribution was in the Southwest and southern Midwest while the highest was in major urban areas, particularly on the coasts.
  • 890 counties did not have mammography machines.
  • Counties with the most mammography machines had 7.5% higher breast cancer incidence rates per 100k women compared to counties with no machines (329 vs. 306) – a sign they were detecting more cancers. 
  • There were 155 counties where mammography machine deployment would have the biggest return. 
  • And 9.6k breast cancer cases would be detected if counties with low or no mammography capacity detected breast cancer at the same rate as high-detection counties.

The new results track with another recent study that also revealed the presence of cancer screening deserts in the Southwest.

So what can be done? The Milken researchers proposed that low-resource counties be targeted for investment, but simply installing new machines won’t by itself cure the access problem. 

  • It’s also important to address barriers such as language, transportation, and cost-sharing in order to achieve equal access. 

The Takeaway

The new report shows that mammography access isn’t just an abstract issue – it’s one that is claiming the lives of thousands of U.S. women a year. Fortunately, the Milken researchers have done much of the legwork in identifying the specific areas that deserve attention. 

Opportunistic Screening Takes Big Step Forward

Posted on by Brian Casey

Opportunistic screening took a big step forward this week with new research in Nature Scientific Reports showing how an AI algorithm from Riverain Technologies was able to calculate coronary artery calcium scores from non-contrast CT scans – with performance close to that of radiologists. 

Opportunistic screening gives radiologists the chance to detect clinical conditions other than those for which the original scan was ordered. 

  • Potential use cases include calculating cardiovascular risk from mammograms or undiagnosed osteoporosis from CT exams.

One of the opportunistic applications with the most potential is CAC scoring from CT scans. 

  • CAC scores are a good marker for future cardiovascular risk. But it can be time-consuming to perform separate cardiac CT scans just to acquire CAC data when thousands of abdominal and thoracic CT studies are conducted every day and could serve just as well.

Riverain’s ClearRead CT CAC algorithm uses AI to analyze non-contrast CT exams and produce Agatston scores, the reference standard for CAC analysis. 

  • Previous research found Agatston scores to be predictive for both cardiovascular and all-cause mortality, but generating the scores requires some manual involvement from clinicians. 

In the new study, Mass General Brigham researchers compared ClearRead CT CAC’s performance to ground-truth calculations from radiologists in 491 patients who got non-contrast CT scans at five U.S. hospitals in 2022 and 2023. Researchers found…

  • CAC score agreement between AI and radiologists was high, with a kappa of 0.959 (1.0 is perfect agreement).
  • The association remained strong regardless of sex, age, race, ethnicity, and CT scanner model, with kappa higher than 0.90 for all groups except “other race.” 
  • The AI model’s CAC scores from non-gated CT scans were similar to those from gated cardiac CT exams (kappa = 0.906), which are generally considered the gold standard for cardiac CT but are more complex to perform.
  • The model’s kappa for gated CT exams compared favorably to recent research conducted with other commercially available algorithms.

The results are a boost for opportunistic screening but in particular for Riverain, which got FDA clearance for ClearRead CT CAC in December 2024 and offers the solution as part of its ClearRead CT suite.

The Takeaway

The new results show that opportunistic screening is moving beyond the research phase and that the opportunity could be now for real-world clinical use. 

CAC Research Leads Imaging at AHA 2025

Posted on by Brian Casey

The 2025 American Heart Association annual conference wraps up today, and cardiac imaging has been a major focus in New Orleans. In particular, research has highlighted imaging’s power to predict future cardiac events – and guide treatment to prevent them. 

Coronary artery calcium scoring with CT is a great example, as CAC scores can predict not only cardiovascular but also all-cause mortality. 

  • Another common theme at AHA 2025 has been opportunistic screening, in which data from imaging exams acquired for other clinical indications can be used to detect osteoporosis, cardiovascular disease, and other issues. 

Check out the items below for some of the hottest imaging topics at AHA 2025, and for a deeper dive into non-imaging news from New Orleans, be sure to visit our Cardiac Wire sister site. 

News from the show’s first three days include…

  • A massive study of 40k people found that those with CT-derived CAC scores greater than 0 were 2X-3X more likely to die from any cause than people without any CAC – and more died of causes other than cardiovascular disease. Also, 8.5% of patients had other significant findings. 
  • Community health personnel on a Native American reservation were trained to perform point-of-care screening echocardiography assisted by Us2.ai’s AI algorithms. 
  • Us2.ai’s algorithm was also used with transthoracic echo in the SCAN-MP study to detect transthyretin amyloid cardiomyopathy, a cause of heart failure. 
  • Treadmill stress tests fell short compared to CCTA in screening older master’s athletes for ischemia that could lead to sudden cardiac death.
  • A program in Brazil that used echocardiography to screen schoolchildren for latent rheumatic heart disease led to lower prevalence rates after 10 years (2.5% vs. 4.5%). 
  • Patients with hypertrophic cardiomyopathy who had higher levels of myocardial fibrosis on cardiac MRI were almost 6X more likely to have adverse events over eight years.
  • HeartLung Technologies’ AI tool predicted CAC presence on CT scans in 2.1k participants in the MESA study with higher AUC than other tools (AUC = 0.73 vs. 0.68).
  • Another study used HeartLung’s AI to analyze CAC scans to detect myosteatosis – a sign of systemic metabolic dysfunction – which predicted atrial fibrillation and heart failure. 
  • A program promoting CAC scoring to an urban population brought in people for screening who might have been missed through physician referral. 

The Takeaway

This week’s news from AHA 2025 shows medical imaging’s contribution to early detection of cardiovascular disease – the leading cause of death worldwide. CT-based CAC scoring has especially promising potential, not only for heart disease but also other conditions through opportunistic screening.

Medicare Payment Pushback to 2026 Physician Rates

Posted on by Brian Casey

CMS gave U.S. medical specialists a fright on Halloween with the publication of its final 2026 Medicare Physician Fee Schedule. The new MPFS rates lock in a controversial “efficiency adjustment” for specialist physicians (including radiologists) and continue a decline in Medicare payment rates for specialists.

Physicians have long complained about low reimbursement rates in the Medicare and Medicaid programs, which are tasked with providing healthcare services to an aging population under a budget that’s, by law, limited to a fixed amount.

  • The situation creates a zero-sum game: increased healthcare spending in one area has to be offset by reductions in another.

Physicians thought they won a victory in summer 2025 with the passage of the One Big Beautiful Bill Act, which included a 2.5% increase in the Medicare conversion factor, the complicated formula governing physician payments.

  • But it didn’t take long for the bill to come due. Within weeks of OBBBA’s passage, CMS issued its proposed 2026 MPFS rates, which included the conversion factor bump but also what the agency called a 2.5% “efficiency adjustment” payment reduction.

CMS justified the reduction by stating that it applied to medical services “that have likely become able to be furnished more efficiently over time but still retain valuations based on outdated assumptions” – including medical image interpretation.

  • But the subtext is that the adjustment continues the agency’s long shift away from medical specialties – which CMS believes are overpaid – and toward primary care physicians.

Organized medicine’s response illustrates the rule’s uneven impact. 

Indeed, an ACR analysis of the final rule estimates an overall impact of the MPFS changes to be -2% for radiology, -1% for nuclear medicine, +2% for interventional radiology, and -1% for radiation oncology.

  • That may not sound like a lot, but the reductions come on top of years of similar declines that some observers have likened to “death by a thousand cuts.”

The Takeaway

By finalizing the 2026 MPFS, CMS is locking in a physician reimbursement schedule that continues to shift payments away from medical specialties like radiology and toward primary care. It’s a trend that’s been happening for decades, and is one that this year’s change in administration has done little to reverse. Radiology should buckle up. 

New CT Protocols Reduce Radiation Dose

Posted on by Brian Casey

With patient safety top of mind these days, radiology professionals are correct to focus on performing CT scans with less radiation. To that end, three recently published research studies highlight new protocols to do just that.

Radiation safety has been one of the top radiology stories in 2025 following several studies underscoring the links between medical radiation and cancer. 

  • The irony is that patient radiation exposure can be reduced dramatically using protocols that already exist – it’s just a matter of applying them consistently in the real world. 

In the first paper, published in European Journal of Radiology, researchers share their MINDS-CAD protocol for coronary CT angiography. 

  • MINDS-CAD relies on tailoring contrast dose to patient weight and CT scanner tube voltage using a five-step process. 

MINDS-CAD was tested with 112 obese patients getting clinically indicated CCTA with Siemens Healthineers’ Somatom Force dual-source CT scanner and Bayer’s Ultravist 370 contrast agent. Researchers found that compared to a conventional tube voltage-adapted protocol, MINDS-CAD…

  • Achieved superior image quality according to cases rated “good” or “excellent” (86% vs. 75%).
  • Generated fewer poor-quality scans (3.5% vs. 8.8%).
  • Produced sharply lower radiation dose (99 vs. 386 mGy•cm).
  • Saw no link between vascular attenuation and BMI or tube voltage.

In a second EJR paper, researchers from India tested the ability of an AI-based reconstruction algorithm to reduce dose in cerebral CTA exams.

  • They used Philips’ Precise Image AI-based reconstruction protocol, which produces images resembling traditional filtered back projection scans while reducing noise like advanced iterative reconstruction methods.

In tests with 68 patients who got cerebral CTA at 100 kVp, compared to iterative reconstruction, Precise Image…

  • Improved contrast-to-noise ratio 26%, signal-to-noise ratio 22%, and visual noise 16%.
  • Generated higher image quality scores from radiologists.
  • Generated an extremely low median effective dose of 0.785 mSv.

Finally, a third study – this one in Clinical Radiology – used a “double low” technique of low-energy 50 keV images on GE HealthCare’s Revolution Apex dual-energy CT scanner with TrueFidelity deep learning image reconstruction on 60 patients with cirrhotic liver disease. 

  • Compared with a conventional protocol, the double-low technique had 48% lower radiation entrance dose (4.10 vs. 7.88 mSv) and 32% lower contrast dose (67.3 vs. 99.1 mL), while image quality was rated higher.

The Takeaway

Taken together, the new papers show that radiology’s radiation dose challenge is eminently solvable thanks to the ingenuity of clinicians and researchers who are pioneering new ways to scan.

Get every issue of The Imaging Wire, delivered right to your inbox.