Tag: MRI

MRI Reveals Junk Food’s Toll on Carotid Arteries

Posted on by Brian Casey

As the U.S. government weighs a regulatory crackdown on ultra-processed food, a new study indicates the feds may be on to something. Researchers used MRI to discover that people who consumed more ultra-processed food had higher levels of carotid arterial plaque – a risk factor for cardiovascular disease. 

The FDA and the USDA on July 23 announced the start of a new initiative to investigate the risks of ultra-processed foods and their relationship to chronic diseases such as obesity, heart disease, and cancer. 

  • The project is widely seen as a priority of HHS Secretary Robert F. Kennedy, Jr. and his Make America Healthy Again movement. 

Meanwhile, arterial plaque buildup is a sign of atherosclerosis and has been linked to multiple clinical conditions, from stroke to intraplaque hemorrhage. 

In the new paper in American Journal of Preventive Cardiology, researchers noted the established association between adverse cardiovascular events and consumption of ultra-processed food and beverages, but the association with subclinical disease hasn’t been explored. 

  • So researchers reviewed carotid MRI scans of 768 participants from the Atherosclerosis Risk in Communities study, in which subjects also described their dietary intake with a 148-item questionnaire. 

MRI scans were correlated with dietary habits, finding that compared to the lowest quartile, people in the highest quartile of ultra-processed food consumption had…

  • Greater total arterial wall volume. 
  • Greater total lipid core volume and maximum lipid core area.
  • Higher maximum segmental wall thickness.

No correlation was found between arterial plaque and other types of diet measures, such as carbohydrate and fat intake or glycemic load index.

  • The findings suggest that much of the negative health effect from ultra-processed foods comes from their contribution to arterial plaque buildup, which could occur through their unfavorable nutrient profile leading to alterations in blood lipids.

The Takeaway

In today’s hyperpolarized political environment – in which scientific inquiry is often subordinated to already-solidified beliefs – the new findings connecting MRI measurements of carotid artery plaque to ultra-processed foods offer a foundation for public policy changes that could indeed improve the health of Americans.

MRI Accident Turns Deadly

Posted on by Brian Casey

A tragic MRI accident in Long Island, New York, has turned deadly. A man who was pulled into a mobile MRI scanner by a heavy chain he was wearing died of his injuries. 

Keith McAllister was waiting outside a mobile MRI trailer operated by Nassau Open MRI on Long Island as his wife received a knee scan.

  • McAllister was wearing a weight-training chain around his neck that weighed some 20 pounds.

When he entered the trailer to help his wife get off the scanner table, the system’s powerful 1.5T magnetic field drew him against the magnet. It took staff an hour to free him.

Investigators are still looking into the details of the episode, but it underscores the shortcomings in how MRI safety is regulated in the U.S., where fatal MRI accidents are extremely rare but still do occur.  

  • That’s according to MRI safety expert Tobias Gilk, vice president at architectural firm Radiology Planning and founder of Gilk Radiology Consultants, who spoke to The Imaging Wire about the accident.

The U.S. has some of the most comprehensive and sophisticated guidelines on MRI safety, encapsulated in the ACR Manual on MR Safety.

  • What’s more, the radiology community including ACR, ISMRM, ASRT, and others are currently observing their annual MR Safety Week to promote safe MRI scanning – an event that started just a few days after McAllister died.

But despite the great leaps in knowledge about MRI safety, Gilk believes that keeping patients safe is complicated by the exponential growth in the modality’s complexity, while actual enforcement of safety standards is lacking. 

  • Many state health departments don’t even address MRI safety as they focus more aggressively on regulating ionizing imaging modalities like CT and X-ray, and healthcare certification bodies like the Joint Commission lack enforcement teeth.

Instead, MRI safety often becomes the responsibility of technologists who frequently must juggle multiple tasks as they manage both patients and scanner operations.

  • This can be particularly challenging in mobile MRI coaches, often staffed by a single MRI technologist where the only barrier between the outside world and the scanning environment is just a single – often unlocked – door. 

The Takeaway

The tragic death of Keith McAllister in a mobile MRI trailer shows that all the guidelines and safety events in the world won’t keep patients safe unless accompanied by stronger enforcement of the knowledge the radiology community already has. We can do better.

Radiology Workforce Shortage Tightens

Posted on by Brian Casey

Radiologist attrition rates have jumped 50% since 2020, and new workforce projections suggest the shortage will only worsen as imaging demand continues to outpace supply. The report – from staffing firm Medicus Healthcare Solutions – projects a worsening supply of radiologists by 2037.

It’s no news to anyone that healthcare is being squeezed by rising volumes from an aging population and chronic staff shortages caused by a training system that simply isn’t turning out enough qualified medical professionals.

  • In radiology, both radiologists and radiologic technologists are in short supply, and there have been only 29 diagnostic radiology PGY-1 training positions added since 2021. 

The Medicus report mostly assembles data acquired from other sources such as a recent study in JACR on radiologist supply, but taken together the numbers paint a sobering picture …

  • Imaging utilization is projected to grow 17-27% by 2055.
  • Radiologist attrition rates have grown 50% since 2020. 
  • Radiologist distribution per 100k population is uneven across the U.S., ranging from 25 radiologists in Minnesota to 9 radiologists in some other states.
  • Reimbursement is falling, with the Medicare conversion factor for 2025 dropping -2.83% for diagnostic radiology and -4.83% for interventional radiology.

What’s to be done? On the positive side, at least one new radiologist residency program started up this year, and legislation was recently introduced that would add 14k residency training slots over seven years. 

  • The report also recommends teleradiology as a possible solution, with 92% of radiologists in a recent survey saying their institution offered remote work options and 73% of radiologists participating in remote work. 

Medicus also advised health systems to take several compensation-focused steps to attract and retain radiologists …

  • Offer flexible, hybrid work schedules.
  • Provide competitive compensation packages and signing bonuses.
  • Improve vacation policies and time-off benefits.

The Takeaway

It’s hard to see short-term Band-Aids like better salary and benefits solving healthcare’s workforce shortage. And some are even questioning whether AI will really help make radiologists more efficient. In the end, systemic changes like a sharp expansion in residency training slots are what’s needed to effect a long-term solution to the staffing dilemma. 

All-Star AI for Prostate MRI

Posted on by Brian Casey

An AI model for prostate MRI that combines the best features of five separate algorithms helped radiologists diagnose clinically significant prostate cancer in a new study in JAMA Network Open. 

The Prostate Imaging-Cancer AI consortium was formed to address a nagging problem in prostate cancer screening.

  • Studies have shown that MRI can reduce biopsies and minimize workup of clinically insignificant disease, but it also has high inter-reader variability and requires a high level of expertise. 

The PI-CAI challenge brought together researchers from multiple countries with a single goal: develop an AI algorithm for prostate MRI that would improve radiologists’ performance.

  • Results were presented at RSNA and ECR conferences, as well as in a 2024 paper in Lancet Oncology that showed that individually the algorithms improved radiologist performance and generated fewer false positives.

But what if you combined the best of the PI-CAI algorithms into a single all-star AI model? 

  • Researchers did just that in the new study, combining the top five algorithms from the PI-CAI challenge into a single AI model in which each algorithm’s results were pooled to create an average detection map indicating the presence of prostate cancer. 

To test the new algorithm, 61 readers from 17 countries interpreted 360 prostate MRI scans with and without the model. 

  • Patients in the test cohort had a median age of 65 years and a median PSA level of 7.0 ng/mL; 34% were eventually diagnosed with clinically significant prostate cancer.

Results of PI-CAI-aided prostate MRI were as follows …

  • Radiologists using the algorithm had higher diagnostic performance than those who didn’t (AUROC=0.92 vs. 0.88).
  • PI-CAI working on its own had the highest performance (AUROC=0.95).
  • Sensitivity improved for cases rated as PI-RADS 3 or higher (97% vs. 94%).
  • Specificity also improved (50% vs. 48%).
  • AI assistance improved the performance of non-expert readers more than expert readers, with greater increases in sensitivity (3.7% vs. 1.5%) and specificity (4.3% vs. 2.8%).

The Takeaway

The new PI-CAI study is an important advance not only for prostate cancer diagnosis but also for the broader AI industry. It points to a future where multiple AI algorithms could be combined to tackle clinical challenges with better diagnostic performance than any model working alone.

Molecular MRI Adds Certainty to Cancer Diagnosis

Posted on by Partner Content

MRI has become an important tool in the detection, diagnosis, and treatment planning for many cancers, especially solid tumors. However, up until now, a lack of specificity has held back the full potential of MRI.  

While MRI is very good at identifying areas of interest, factors such as infection, benign tumors, post-traumatic areas, and inflammation can all increase vascularity and, therefore, enhancement of contrast and signal changes.  

  • As a result, MRI has a high rate of false positives – findings that may be flagged as something of concern but that are not necessarily malignant lesions.  

This lack of accuracy results in clinical care teams performing too many confirmatory biopsies, with most being benign.

Now a novel class of molecular imaging contrast agents developed by Imagion Biosystems brings a new level of specificity to MRI. 

  • The company’s MagSense imaging agents have the potential to improve the clinical utility of the large installed base of MRI systems across the globe through improved accuracy of interpretation, avoiding biopsies of benign lesions, driving earlier intervention and improving outcomes and quality of life.

Unlike gadolinium-based agents that non-specifically enhance tissue vascularity regardless of cause, MagSense imaging agents target receptors on cancer cells.  

  • By combining magnetic nanoparticles that have high susceptibility and r2 relaxivity with cancer-specific biomarkers, molecular MRI becomes possible.

Imagion’s superparamagnetic iron oxide nanoparticles are coated with a cancer-specific targeting moiety, such as an antibody or peptide.

  • The cancer biomarker molecule causes the particles to bind to target-specific cancer cells, if present. If the lesion in question is not the target cancer, the particles do not bind.

Where the imaging agent has become attached to the tissue, the nanoparticles produce an identifiable change in MRI signal. 

  • This signal is easily detected by radiological review and can be quantitatively assessed.

Imagion has developed cancer-specific contrast imaging agents for HER2 breast cancer, prostate cancer, and ovarian cancer, and the MagSense platform can be adapted for any type of cancer for which there is a targeting moiety.  

  • Imagion is now preparing to initiate a multisite phase 2 study in the U.S. in HER2+ breast cancer patients to optimize imaging parameters and compare MagSense imaging to the standard of care.  

The Takeaway

Molecular-specific imaging agents like the MagSense technology from Imagion Biosystems create the opportunity for molecular MRI to fundamentally change how radiologists detect and monitor cancers. 

The company is publicly traded (ASX:IBX) and is looking to expand its U.S. investor base as it advances through its clinical programs. To become involved as an investigator or investor or to learn more visit their website.

Imaging Workload Jumps with Higher Use of CT, MRI

Posted on by Brian Casey

Radiology’s shift to more advanced modalities like CT and MRI is increasing the burden on radiologists to interpret more complex exams. A new study in JACR documents the trend, finding that radiologist workload for inpatient imaging has risen sharply over the last 10 years. 

Like many physicians, radiologists are feeling burned out from rising patient workload, personnel shortages, and declining reimbursement. 

  • But radiology has the added burden of being one of healthcare’s most technology-focused specialties, with new imaging modalities giving them cooler tools to work with, but at the cost of steadily increasing exam complexity.

Researchers from Brigham and Women’s Hospital have been tracking inpatient imaging utilization for the past 40 years, and the new paper provides the latest update. 

  • They calculated inpatient imaging volume at Brigham and Women’s from 2012 to 2023, during which 896k imaging exams were performed.  

Results for the study were as follows …

  • Total annual inpatient imaging volume grew 17% over 10 years (102k to 119k exams).
  • Total imaging exams per patient admission (adjusted by case mix and disease severity) fell 20% due to declines in X-ray, ultrasound, and nuclear medicine.
  • But imaging exams per patient admission grew for CT (19%) and MRI (21%).
  • Leading to growth in CT and MRI’s combined share of all radiology global RVUs (62% to 75%).
  • Hospital length of stay rose 32% (5.6 to 7.4 days), possibly due to the COVID-19 pandemic. 

What does it all mean? Basically, the number of inpatient imaging exams per patient is declining when adjusted for disease severity, but radiologists are still having to work harder because the studies are more complex. 

  • Imaging could also be shifting from the inpatient setting to outpatient centers due to reimbursement changes aimed at shifting exams to lower-cost settings than hospitals.

One big question with the new study is the degree to which the COVID-19 pandemic skewed the results compared with previous years. 

  • The pandemic may have spurred more use of CT, especially given its value in providing a definitive diagnosis of SARS-CoV-2 infection. 

The Takeaway

If you feel like you’re working harder than ever, the new findings show that you’re not crazy. And given radiology’s breakneck pace of innovation, it’s not likely the trends revealed in the new study will let up any time soon.

MRI in Paradise – News from ISMRM 2025

Posted on by Brian Casey

The global MRI community this week traveled to paradise to convene its annual meeting of the International Society for Magnetic Resonance in Medicine. If you were one of the lucky ones to be in attendance in Honolulu, Hawaii for ISMRM 2025, you were treated to some of the latest news in radiology’s most powerful modality. 

As has been the case at other radiology meetings, AI took center stage in Honolulu. 

  • AI has multiple use cases in MRI, from helping radiologists interpret images more efficiently to accelerating scans and upscaling lower-field images to resemble high-field exams.

Just a few of the news highlights from ISMRM 2025 are below …

  • Using AI to interpret prostate MRI reduced reading times by 48% (250 to 120 seconds) while improving the diagnostic performance of both experienced and less experienced radiologists. 
  • AI of thyroid T2-weighted neck MRI scans demonstrated good accuracy (87%) for nodules larger than 1 cm, indicating a possible role for screening and monitoring.
  • Researchers presented progress in creating brain charts of white matter based on MRI scans of 24k cognitively healthy people that can be used to track normal and abnormal brain development.
  • Brain MRI showed that lower brain volumes in people with coronary artery disease were associated with worse aerobic fitness and higher BMI, revealing a link between cardiovascular and brain health. 
  • Chinese researchers showed their work on PMEEN, a multimodality brain scanner that combines PET, MRI, EEG, eye-tracking, and functional near-infrared spectroscopy. 
  • A Spanish team demonstrated research on a low-field PET/MRI scanner with focused ultrasound capability for therapeutic applications.
  • AI could be used during abbreviated breast MRI screening scans to convert women mid-exam to a full MRI protocol if abnormalities are detected.
  • 7T MRI was used to detect iron deposits in the brain, which could be a marker for Alzheimer’s disease.
  • MRI with an ultrashort echo time protocol could be an alternative to CT for following up lung nodules.
  • Researchers presented a deep learning-based approach to generating synthetic contrast-like MR images without gadolinium. 
  • MGH researchers showed progress in developing a 136mT portable MRI scanner for bedside brain scanning of preterm neonates.

The Takeaway

The rapid proliferation of news about AI-based MRI at ISMRM 2025 suggests its own vision of paradise – a world in which MRI can be deployed more widely than ever before, where radiologists with AI assistance detect disease in many cases before symptoms even occur. We can only dream.

MRI Recon Gets Real with AI-Driven Protocols

Posted on by Brian Casey

AI-based data reconstruction for MRI scans took a step forward this week with studies showing how to generate 3T-like images from ultralow-field scanners, and improve scanner efficiency by cutting energy consumption.

MRI is radiology’s premier modality, but MRI scanners are cumbersome to install and expensive to operate. 

  • Ultralow-field scanners could help but some believe they lack the image quality for some clinical applications. 

Enter AI-based image reconstruction. Deep learning protocols are being developed for a wide range of imaging modalities, from PET to CT to MRI. 

  • These algorithms take images acquired with lower-quality input data – be it less CT radiation dose or lower MRI field strength – and upscale them to resemble full-fidelity images.

This trend is illustrated by research published this week in Radiology in which researchers tested a generative adversarial network algorithm called LowGAN for reconstructing data acquired on Hyperfine’s Swoop 0.064T portable ultralow-field MRI scanner.

  • Their goal was to enable Swoop to generate images resembling those acquired on a 3T system. 

After training LowGAN on paired 3T and 0.064T images, they tested the algorithm in 50 patients with multiple sclerosis and further validated it with a separate 13-patient cohort. They then judged LowGAN against several measures of MR image quality, finding that it …

  • Showed the biggest improvement on synthetic FLAIR and T1 images.
  • Improved conspicuity of white matter lesions, without introducing false lesions.
  • Increased consistency of cortical and subcortical volume measurements with 3T images.
  • But was unable to reveal brain lesions that were missed in the original low-field scans. 

AI-based data reconstruction also has environmental implications. Medical imaging is a major contributor to greenhouse gas emissions, and anyone who’s managed an MRI operation knows how much energy these massive scanners consume. 

  • A second paper published this week in Radiology described how MRI acceleration – scans acquired at a faster speed and then reconstructed for better image quality – reduced energy use, lowering carbon emissions while boosting imaging capacity. 

Researchers tried three techniques for speeding MRI acquisition – parallel acceleration, simultaneous multi-slice, and a deep learning algorithm. 

  • All three reduced energy consumption 21% to 65% and increased daily capacity by one to seven scanning slots, with deep learning showing the biggest effect.

The Takeaway

The new papers demonstrate an exciting future in which less powerful data acquisition technologies can be upscaled with AI to produce images that more closely resemble state-of-the-art scanning. The benefits will be enjoyed by both patients and the planet.

AI Helps Radiologists Read Prostate MRI

Posted on by Brian Casey

MRI is changing how prostate cancer is detected, diagnosed, and followed up. But even a technology as powerful as MRI could use a little help, as evidenced by a new study in Radiology showing that a commercially available AI algorithm could help radiologists diagnose clinically significant prostate cancer. 

Workup of suspicious prostate lesions is being reshaped by MRI in meaningful ways.

  • For example, MRI-guided biopsy is replacing systemic prostate biopsy without guidance, especially for patients with low to intermediate risk of prostate cancer. 

But prostate MRI isn’t perfect – yet. Radiologist performance can vary due to differences in experience, as well as variations in MRI acquisitions, tumor location, and cancer prevalence. Could AI help even out these variations? 

  • To find out, researchers from South Korea tested Siemens Healthineers’ syngo.via Prostate MR algorithm in 205 patients suspected of prostate cancer who were scheduled for biopsy based on clinical information (including previous MRI scans).

The AI algorithm’s performance was compared to that of experienced radiologists, and researchers also estimated its impact on radiologist interpretation if used as a reading aid, finding that for clinically significant prostate cancer… 

  • AI had lower sensitivity versus radiologists (80% vs. 93%).
  • But higher positive predictive value (58% vs. 48%).
  • Adding AI to radiologists’ interpretation more than doubled specificity (44% vs. 21%).
  • There were no cancer cases among lesions rated by both the algorithm and radiologists as not likely to be cancer (PI-RADS 1 or 2).

AI’s higher PPV indicates that it could help reduce unnecessary prostate biopsies, while also detecting clinically significant cancer that might have been missed by radiologists.  

The Takeaway

The new findings echo previous studies that demonstrate the value of AI for MRI of prostate cancer, but differ in that they investigate a commercially available algorithm – indicating that tools for better prostate MRI are becoming accessible to radiologists. 

AI Enables Single-Click Cardiac MRI

Posted on by Brian Casey

Cardiac MRI is one of the most powerful imaging tools for assessing heart function, but it’s difficult and time-consuming to perform. Could automated AI planning offer a solution? A new research paper shows how AI-based software can speed up cardiac MRI workflow. 

Cardiac MRI has a variety of useful clinical applications, generating high-resolution images for tissue characterization and functional assessment without the ionizing radiation of angiography or CT.

  • But cardiac MR also requires highly trained MR technologists to perform complex tasks like finding reference cardiac planes, adjusting parameters for every sequence, and interacting with patients – all challenges in today’s era of workforce shortages. 

Cardiac MRI’s complexity also increases the number of clicks required by technologists to plan exams. 

  • This can introduce scan errors and produces inter-operator variability between exams. 

Fortunately, vendors are developing AI-based software that automates cardiac MR planning – in this case, Siemens Healthineers’ myExam Cardiac Assist and AI Cardiac Scan Companion. 

  • The solution enables single-click cardiac MR planning with a pre-defined protocol that includes auto-positioning to identify the center of the heart and shift the scanner table to isocenter, as well as positioning localizers to perform auto-align without manual intervention. 

How well does it work in the real world? Researchers tested the AI software against conventional manual cardiac MR exam planning in 82 patients from August 2023 to February 2024, finding that automated protocols had … 

  • A lower mean rate of procedure errors (0.45 vs. 1.13).
  • A higher rate of error-free exams (71% vs. 45%).
  • Shorter duration of free-breathing studies (30 vs. 37 minutes).
  • But similar duration of breath-hold exams (42 vs. 44 minutes, p=0.42).
  • While reducing the error gap between more and less experienced technologists. 

In their discussion of the study’s significance, the researchers note that most of the recent literature on AI in medical imaging has focused on its use for image reconstruction, analysis, and reporting.

  • Meanwhile, there’s been relatively little attention paid to one of radiology’s biggest pain points – exam preparation and planning. 

The Takeaway

The new study’s results are exciting in that they offer not only a method for performing cardiac MR more easily (potentially expanding patient access), but also address the persistent shortage of technologists. What’s not to like?

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