Category: CT Scanners

GE’s Photon-Counting CT Clearance

Posted on by Brian Casey

GE HealthCare this week announced FDA clearance for Photonova Spectra, the company’s first photon-counting CT scanner. While GE isn’t the first vendor with a commercially available PCCT scanner, it’s hoping to differentiate the system by highlighting the combination of ultrahigh-resolution scanning with spectral imaging.

Photon-counting CT represents a huge leap forward in CT instrumentation that’s not only driving new clinical applications but is also helping radiologists perform routine CT exams with better resolution and lower radiation dose. 

  • PCCT scanners directly convert photons to digital data, instead of using conventional CT’s two-step energy-integrating technique, resulting in images with less noise and supporting acquisition protocols with lower radiation dose. 

Siemens Healthineers brought the first photon-counting CT scanner to market with the 2021 FDA clearance of Naeotom Alpha.

  • Since then, Siemens has had the market for whole-body PCCT to itself, with only niche photon-counting scanners getting FDA clearance.

But we’re here to talk about GE’s Photonova Spectra, so let’s get to it. The system is based on GE’s Deep Silicon detector technology, which uses a novel semiconductor detector material that’s particularly suited for spectral imaging.

  • Spectral CT acquires images at different energy levels, which is useful for detecting disease because malignant and benign tissue respond differently to different energy spectra.    

GE is highlighting Photonova Spectra’s 8-bin energy resolution, which means the scanner separates incoming photons into eight distinct energy ranges – or bins – rather than grouping them into one or two. 

  • This enables Photonova Spectra to deliver much more precise spectral imaging than previously possible, with better quantitative accuracy and improved differentiation between materials like bone and soft tissue, according to GE CT executive Chad Rowland.

Spectral CT has developed a reputation as a technology that’s powerful but complex, and GE addressed this issue with workflow tools that make spectral imaging “always on” and easier than ever to perform. 

  • GE is banking on the combination of spectral imaging with Photonova Spectra’s ultrahigh-resolution images being a game-changer for many sites considering adopting their first PCCT scanner.

The Takeaway

FDA clearance for GE HealthCare’s Photonova Spectra photon-counting CT scanner is great news for the vendor that puts it on a level competitive footing with Siemens as a CT innovator. But it’s also good news for imaging providers, giving them another option for delivering to patients the benefits of PCCT – lower radiation dose and better image quality. 

CT Supports Better Stroke Care

Posted on by Brian Casey

When it comes to stroke, time is brain. And the faster stroke patients can be diagnosed, the sooner brain-saving treatment can start. Researchers in Germany found that sending stroke patients to hospitals equipped with CT scanners and telemedicine connections might be more effective than transferring them directly to specialized stroke centers.

CT is critical for assessing stroke patients and determining whether they should receive intravenous thrombolysis with clot-busting drugs or endovascular thrombectomy with catheter-guided devices.

  • It’s particularly important that patients be treated within the “golden hour” of stroke symptom onset, as every 10 minutes of delay results in eight weeks of healthy life lost.

Specialized stroke centers outfitted with dedicated equipment have sprung up to deliver better care, but they’re not that common and patient transfers can take extra time.

  • Far more common are hospitals with CT scanners, giving rise to the suggestion of a hub-and-spoke model in which patients are sent first to a hospital equipped with CT and telemedicine for diagnosis and initial thrombolysis (the spoke), and then on to a specialized center (the hub) if necessary.

This approach is tested in a new study in The Lancet Regional Health – Europe, in which German researchers performed a modeling study to see how hub-and-spoke stroke treatment compared to direct transfer to specialized stroke centers.

  • They developed a map of CT-equipped hospitals and dedicated stroke centers in Germany, and calculated minimum travel and time benefits in 10-minute thresholds.

The researchers found that of Germany’s population…

  • 76% were within 15 minutes of at least one hospital with on-site CT, and 99% were within 30 minutes.
  • 51% were within 15 minutes of a stroke-ready hospital (hospitals that treat a set number of stroke patients but aren’t yet certified), and 90% within 30 minutes.
  • Only 46% lived within 15 minutes of a stroke-certified hospital, a figure that grew to 85% within 30 minutes.
  • 36% would reach a CT-equipped hospital at least 10 minutes faster than a certified stroke unit.

Not surprisingly, there were geographic differences in accessibility, with urban areas having good access to specialized stroke centers but rural and underserved areas less so (90% vs. 55%).

  • So the hub-and-spoke model might be better suited for rural areas while the direct transfer approach would still work for urban zones. 

The Takeaway

While this study was conducted in Germany, its lessons could be applied to any country that has to juggle healthcare resources with clinical demands. The question is how much the findings might be impacted by new technologies like mobile stroke units and AI-based stroke assessment. 

Canon Celebrates 50 Years of CT Innovation: Redefining Healthcare with Meaningful AI

Posted on by Partner Content

This year marks a historic milestone for Canon – five decades of pioneering CT innovation that has transformed the landscape of healthcare. From introducing industry-first technologies to setting new standards in diagnostic imaging, Canon continues to lead the way in delivering solutions that matter.

Canon’s legacy is built on breakthroughs such as its three-time award-winning wide-area CT systems, deep learning reconstruction that brings 1K resolution to CT imaging, and automation improving workflow. 

  • These innovations have consistently elevated diagnostic confidence, patient safety, and operational efficiency.

In today’s world, AI is everywhere – but Canon’s AI is Meaningful AI. It’s not about AI for the sake of technology; it’s about creating real-world impact on patient care. 

  • Canon’s portfolio of scanner-integrated AI applications is designed to enhance image quality, streamline workflows, and improve consistency – ultimately delivering better care, better experience, and better efficiency for patients and providers alike.

Canon is redefining CT by making AI a core component across its portfolio. Key innovations include…

  • AI-Assisted Scanner Workflow Automation. Canon’s INSTINX platform introduces intuitive, intelligent, and integrated AI technologies that enable autonomous CT operations. By simplifying complex workflows, INSTINX helps technologists focus on patient care while improving throughput and reducing variability.
  • AI-Assisted Post-Processing. Canon’s Automation Platform offers a zero-click, AI-driven solution that accelerates image post-processing. By delivering fast, actionable insights, this platform ensures time-critical results reach care teams when they need them most.
  • AI-Assisted Reconstruction. Advanced algorithms such as AiCE DLR and PIQE DLR leverage deep learning to reveal critical diagnostic information – contrast and resolution – while optimizing dose efficiency. These tools empower clinicians to make confident diagnoses and reduce the need for additional downstream studies. Additionally, CLEARMotion, a DCNN-based algorithm, compensates for patient motion, reducing blur and delivering high-quality results even in challenging cases.

The Takeaway 

As Canon celebrates 50 years of CT innovation, its commitment remains clear: harnessing AI to make imaging smarter, faster, and more meaningful. With these advancements, Canon is not just shaping the future of CT – it’s setting a new benchmark for patient-centered care.

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. 

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.

Malpractice Reform Linked to Less Imaging Use

Posted on by Brian Casey

We all know it happens – medical imaging scans of questionable clinical value, performed not to improve patient diagnosis but to defend clinicians in the event of malpractice litigation. A new study in AJR supports the idea that defensive medicine is driving up imaging use by finding a link between malpractice reform and lower emergency imaging utilization. 

The proliferation of imaging technology throughout the healthcare enterprise – and especially in the emergency setting – gives clinicians a powerful tool that’s just too tempting not to use.

  • Head CT scans can quickly rule out patients who might have a hemorrhagic stroke, for example, while cardiac CT angiography is showing its value for working up patients with chest pain. 

But with great power comes great responsibility. Unnecessary imaging not only drives up healthcare costs but can expose patients to additional radiation as well as complications from working up suspicious findings.

  • Medical-legal experts speculate that malpractice reform through tools such as damage caps could tamp down defensive medicine by limiting physicians’ legal exposure to lawsuits in the event they make a mistake.

In the new study, researchers from the ACR’s Harvey L. Neiman Health Policy Institute tested the idea by analyzing 630k Medicaid encounters for patients with headache presenting to the emergency department in 2019. 

  • They then correlated head and neck imaging volume to various factors that could influence utilization, including whether states had implemented tort reform. 

Their analysis discovered that emergency imaging utilization was less likely to occur…

  • In states with laws on “several liability” (in which parties are only responsible for their own share of damages) (OR = 0.68).
  • In states with malpractice damage caps (OR = 0.79).
  • In states with greater mean malpractice payment (although the effect size was minimal; OR = 0.99).

A couple other interesting findings included…

  • Referring physicians other than emergency medicine were far more likely to order more imaging (OR = 8.45).
  • Facilities with fewer than 100 beds were less likely to order imaging (OR = 0.65).

The Takeaway

The new findings linking malpractice reforms with lower emergency imaging use confirm what many of us have already suspected. Whether they lead to health policy reforms remains to be seen. 

Doubling Lung Screening Rates with Patient Outreach

Posted on by Brian Casey

Low CT lung cancer screening rates have disappointed medical imaging professionals and public health advocates alike since the test received USPSTF recommendation over 10 years ago. But a new study shows how one health system doubled its lung cancer screening rates – to levels approaching those of more established cancer screening exams. 

USPSTF recommended low-dose CT lung cancer screening in 2013, but 10 years later patient screening rates languished in the mid-teens, compared to rates of around 75% for breast and cervical cancer and above 72% for colorectal cancer. 

  • That means many lung cancer patients are showing up with late-stage disease, when it’s more difficult to cure. Perhaps as a result, lung cancer is expected to cause almost 125k deaths in the U.S. in 2025.

Breaking that cycle was the goal of researchers at the University of Rochester Medical Center in New York, who wrote about their experiences in a study published in NEJM Catalyst. 

  • They wanted to boost lung cancer screening adherence across their network of 42 locations in western New York. 

So how did they do it? Success came through a combination of IT innovation and old-fashioned legwork in patient outreach. Clinicians…

  • Provided evidence on lung cancer screening to primary care providers.
  • Updated their EHR software to identify patients eligible for lung screening based on the daily schedule to provide screening prompts during patient visits.
  • Created dashboards to guide outreach to patients due or overdue for screening exams.
  • Developed an extensive follow-up program with patient navigators to facilitate recall for annual exams.
  • Created a centralized pulmonary team to provide referrals for smoking cessation, conduct shared decision making for screening exams, and manage pulmonary nodules.

The program produced immediate results. In an analysis comparing screening rates in March 2022 to June 2025, researchers found…

  • Lung screening rates doubled (from 33% to 72%).
  • On-time completion of annual LDCT screening exceeded 94%.
  • 78% of lung cancer cases in 2023 and 2024 were diagnosed at an early stage.
  • There were no statistically significant differences in screening rates by patient race.

The Takeaway
The new results match up with recent findings – such as those presented at WCLC 2025 in September – underscoring the importance of reaching out to potential lung cancer screening candidates to bring them into the fold. Despite CT lung screening’s halting history, these studies show that it can be done.

Reducing CT Radiation Dose System-Wide

Posted on by Brian Casey

CT radiation dose has been one of the top radiology headlines this year due to the publication of several studies linking radiation to cancer risk. But new research offers hope that CT radiation dose can be reduced, even across large healthcare systems. 

CT’s link to cancer risk has been controversial, but most established models connect low-level radiation to cancer formation.

There are lots of great technologies for reducing CT radiation dose, from photon-counting CT to adjusting scanner parameters like mA and kVp, while image reconstruction algorithms can upscale noisy low-dose images to look like higher-quality exams.

  • But the problem has always been getting these technologies into the hands of clinicians – and then making sure they use them, especially across large multi-center health systems, where dose can vary even within the same network.  

Taking a crack at the problem were cardiologists from Lee Health Heart Institute in Fort Myers, Florida, in a new paper in JACC: Case Reports. 

  • They specifically looked at radiation dose for coronary CT angiography exams, determining that based on the literature an optimal radiation dose for CCTA should be ≤ 4 mSv – lower than the system’s 6.2 mSv median dose. 

So they implemented several strategies for reducing CCTA dose…

  • Standardizing scanning protocols that emphasized prospective ECG gating, reduced field of view, BMI-tailored tube voltage (kVp), and elimination of redundant imaging phases.
  • Setting parameters for single-source CT at 100 kVp for patients with BMI <30 and 120 kVp for BMI ≥30, with prospective scanning for 60-80% of the cardiac cycle.
  • Using similar kVp settings for dual-source CT scanners, but implementing systolic imaging between 250-450 milliseconds.

How well did it work? After reviewing the program, researchers found…

  • System-wide radiation dose fell 23% (4.8 vs. 6.2 mSv).
  • Diagnostic quality improved as measured by the acceptance rate for FFR-CT exams (93% vs. 91%). 
  • Dose consistency was achieved across locations despite differences in scanner models and practices.

The Takeaway

The new study on CCTA radiation dose shows that dose can be reduced system-wide while maintaining – and even improving – diagnostic image quality. Is it a problem that the research was led by cardiologists and not radiologists? Not if you’re a patient. 

Does BMI Affect AI Accuracy?

Posted on by Brian Casey

High body mass index is known to create problems for various medical imaging modalities, from CT to ultrasound. Could it also affect the accuracy of artificial intelligence algorithms? Researchers asked this question as it pertains to lung nodule detection in a new study in European Journal of Radiology. 

X-ray photons attenuate as they pass through body tissue, which can decrease image quality and produce more noise.

  • This is particularly a challenge for CT exams that don’t use a lot of radiation, like low-dose CT lung screening. 

At the same time, AI algorithms are being developed to make LDCT screening more efficient, such as by identifying and classifying lung nodules.

  • But if high BMI makes CT images noisier, will that affect AI’s performance? Researchers from the Netherlands tested the idea in 352 patients who got LDCT screening as part of the Lifelines study.

Researchers compared patients at both the high end of the BMI spectrum (mean 39.8) and low end (mean 18.7). 

  • Lung nodule detection by both Siemens Healthineers’ AI-Rad Companion Chest CT algorithm and a human radiologist was performed and compared. 

Across the study population, researchers found…

  • There was no statistically significant difference in AI’s sensitivity between high and low BMI groups (0.75 vs. 0.80, p = 0.37). 
  • Nor was there any difference in the human radiologist’s sensitivity (0.76 vs. 0.84, p = 0.17).
  • AI had fewer false positives per scan in the high BMI group than low BMI (0.30 vs. 0.55), a difference that was statistically significant (p = 0.05). 
  • While the difference in false positives with the human radiologist was not statistically significant (0.05 vs. 0.16, p = 0.09).

The study authors attributed AI’s lower performance to more noise in the high BMI scans.

  • They recommended that AI developers include people with both high and low BMI in datasets used for training algorithms.

The Takeaway

The results offer some comfort that patient BMI probably doesn’t have a huge effect on AI performance for nodule detection in lung screening, but it suggests a possible effect that might have achieved statistical significance with a larger sample size. More study in the area is definitely needed given the rising importance of AI for CT lung cancer screening. 

Obesity Drives CT Radiation Dose Higher

Posted on by Brian Casey

The proportion of patients getting CT scans with high radiation doses more than tripled over a five-year period. That’s according to a new study in British Journal of Radiology that found the rate of high-dose CT rising along with growing obesity rates – despite technical advances in CT instrumentation.

CT radiation dose has been closely watched due to its potential to cause cancer.

  • A controversial paper published earlier this year in JAMA Internal Medicine estimated that all the CT scans performed in a year in the U.S. would cause 100k cancers.
  • And another recent paper made a connection between the number of CT scanners installed in a country and the number of patients with high cumulative radiation exposure (over 100 mSv) over five years.

In the new paper, a research team led by radiation safety expert Madan Rehani, PhD, tracked radiation exposure to patients who got CT exams at Massachusetts General Hospital from 2013 to 2022. 

  • They defined high-dose CT exams as those in which individual exam radiation dose exceeded 50 mSv. 

Over a 10-year period, nearly 1.4 million CT exams were performed on 382k patients, revealing that the rate of CT exams with effective doses ≥ 50 mSv…

  • Was less than 1%, but more than tripled from 2017 to 2022 (0.25% to 0.86%).
  • 59% of high-dose exams were multiphase studies (≥ 3 phases).
  • The rate of high-dose CT exams rose 7X faster in overweight and obese patients than in their underweight or normal-weight counterparts in a subset of 5k patients with available BMI data.

There was a close association between obesity and radiation dose, as patients with larger body habitus require more radiation to penetrate deeper for diagnostic-quality images. 

  • Ironically, the introduction of CT scanners with higher table weight capacity and larger gantry diameter may have contributed to the increase by making it possible to scan patients who previously were too large to be imaged.

Researchers also believe the rise in radiation dose starting in 2018 occurred around the same time as MGH’s introduction of more advanced CT scanners with more powerful X-ray tubes.

The Takeaway

The new findings on CT radiation dose illustrate the balancing act that imaging providers face between radiation safety and achieving optimal image quality. With obesity rates steadily rising, it’s a choice that will become increasingly common. 

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