Category: MR Scanners

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.

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 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?

MRI Predicts Cognitive Decline

Posted on by Brian Casey

Early detection of cognitive decline is becoming increasingly important as new therapies become available for conditions like Alzheimer’s disease. A new 20-year study in JAMA Network Open shows that MRI can detect structural brain changes indicating future cognitive decline – years before symptoms occur. 

Longitudinal research has shown that subtle changes in body structure – be they in the heart, brain, or other organs – can predict future disease risk, in some cases decades in advance.

  • That enables the possibility of targeted treatments or behavioral interventions to reduce risk before sick patients experience a cascade of expensive and invasive therapies. 

Mild cognitive impairment is an excellent example. MCI can be a transition to more serious diseases like Alzheimer’s, and previous research has connected it to vascular risk factors that are signs of brain atrophy. 

  • In the current paper, researchers analyzed MRI scans acquired as part of the BIOCARD cohort, a longitudinal study started in 1995 in which cognitively normal participants got baseline brain MRI scans and follow-up exams. 

In a group of 185 BIOCARD participants, researchers tracked how many transitioned to MCI over a mean follow-up period of 20 years, then compared structural brain changes on MRI, finding …

  • 60 participants (32%) progressed to MCI, eight of whom later developed dementia (4.3%).
  • Those with white-matter atrophy on MRI had an 86% higher chance of progression to MCI, the highest rate of any variable studied.
  • Participants with enlargement of the ventricles on MRI had 71% higher risk.
  • Other variables like diabetes and amyloid pathology also had higher risk, but not at the rate of the MRI-detected variables. 

The findings indicate that white-matter volume is closely associated with cognitive function in aging, and that people with higher rates of change are more likely to develop MCI. 

  • The association of diabetes with MCI was not a shock, but researchers said they were surprised there was no association from risk factors like hypertension, dyslipidemia, and smoking.

The Takeaway

The new findings demonstrate the power of MRI to predict pathology years in advance – the question is how and whether to put this knowledge into clinical practice. One could almost see structural brain scans incorporated into whole-body MRI screening exams (if anyone’s listening).

MRI Reduces Prostate Biopsies

Posted on by Brian Casey

New research provides additional support for MRI’s role in making prostate screening more effective. In a new study in NEJM, researchers found that MRI can help reduce unnecessary biopsies more than 50%, with a very low chance of missing high-risk disease. 

As we’ve discussed in previous newsletters, prostate cancer screening based on PSA levels is an imprecise test. 

  • Many men with suspiciously high PSA (typically 3-4 ng/mL or higher) undergo biopsies that detect clinically insignificant disease that would never present a health risk during their lifetimes – the classic definition of overdiagnosis. 

Adding MRI can help make prostate screening more precise by directing biopsy-based workup to only those men with clinically significant cancer – but questions still abound about exactly when it should be used. 

In new results from the GÖTEBORG-2 trial in Sweden, researchers compared prostate screening protocols in men with PSA levels 3 ng/mL and higher who got MRI scans:

  • One group automatically got systemic biopsy and then MRI-targeted biopsy based on MRI results.
  • The other group only got MRI-targeted biopsy if they had a suspicious MRI scan.

In 13.2k men who were followed up for a median of four years, researchers found that those in whom systemic biopsy was omitted …

  • Had 57% lower risk of clinically insignificant cancers.
  • Had lower relative risk of clinically insignificant cancers in subsequent screening rounds (RR=0.25 vs. 0.49).
  • Had 16% lower risk of detecting clinically significant cancers.
  • Had 35% lower risk of advanced or high-risk cancers.

On the down side, the protocol eliminating systemic biopsy could lead to later diagnoses for higher-risk disease for 3 in 1k men – but given the slow-growing nature of prostate cancer it’s not clear how significant this is. 

  • Also, the data indicate that “most prostate cancers become visible on MRI” before they are incurable, which increases the likelihood that they would at least be detected on subsequent screening rounds and could be treated effectively.

The Takeaway

The new findings should help clinicians hone in on the best prostate screening protocols for maximizing detection of clinically significant cancer while minimizing unnecessary workup. Hopefully, the addition of new technologies like AI can move this process along.

Better Prostate MRI Tools

Posted on by Brian Casey

In past issues of The Imaging Wire, we’ve discussed some of the challenges to prostate cancer screening that have limited its wider adoption. But researchers continue to develop new tools for prostate imaging – particularly with MRI – that could flip the script. 

Three new studies were published in just the last week focusing on prostate MRI, two involving AI image analysis.

In a new study in The Lancet Oncology, researchers presented results from AI algorithms developed for the Prostate Imaging—Cancer Artificial Intelligence (PI-CAI) Challenge.

  • PI-CAI pitted teams from around the world in a competition to develop the best prostate AI algorithms, with results presented at recent RSNA and ECR conferences. 

Researchers measured the ensemble performance of top-performing PI-CAI algorithms for detecting clinically significant prostate cancer against 62 radiologists who used the PI-RADS system in a population of 400 cases, finding that AI …

  • Had performance superior to radiologists (AUROC=0.91 vs. 0.86)
  • Generated 50% fewer false-positive results
  • Detected 20% fewer low-grade cases 

Broader use of prostate AI could reduce inter-reader variability and need for experienced radiologists to diagnose prostate cancer.

In the next study, in the Journal of Urology, researchers tested Avenda Health’s Unfold AI cancer mapping algorithm to measure the extent of tumors by analyzing their margins on MRI scans, finding that compared to physicians, AI … 

  • Had higher accuracy for defining tumor margins compared to two manual methods (85% vs. 67% and 76%)
  • Reduced underestimations of cancer extent with a significantly higher negative margin rate (73% vs. 1.6%)

AI wasn’t used in the final study, but this one could be the most important of the three due to its potential economic impact on prostate MRI.

  • Canadian researchers in Radiology tested a biparametric prostate MRI protocol that avoids the use of gadolinium contrast against multiparametric contrast-based MRI for guiding prostate biopsy. 

They compared the protocols in 1.5k patients with prostate lesions undergoing biopsy, finding…

  • No statistically significant difference in PPV between bpMRI and mpMRI for all prostate cancer (55% vs. 56%, p=0.61) 
  • No difference for clinically significant prostate cancer (34% vs. 34%, p=0.97). 

They concluded that bpMRI offers lower costs and could improve access to prostate MRI by making the scans easier to perform.

The Takeaway

The advances in AI and MRI protocols shown in the new studies could easily be applied to prostate cancer screening, making it more economical, accessible, and clinically effective.  

MRI Makes Prostate Screening More Precise

Posted on by Brian Casey

Prostate cancer screening isn’t a guideline-directed screening test yet, but this could change with the use of MRI and other tools. A series of papers published in several JAMA journals late last week indicates the progress that’s being made. 

As we’ve discussed in previous issues, prostate screening with PSA tests hasn’t met the threshold for clinical benefit achieved by other population-based screening exams.

  • PSA-based screening has been characterized by lower mortality benefits and relatively high rates of overdiagnosis and complications from follow-up procedures. 

But some researchers believe that PSA screening could be made more effective by using additional diagnostic tools like imaging and blood tests to focus on potentially high-risk disease for biopsy while active surveillance is used for less threatening prostate lesions. 

In the ProScreen trial in Finland, researchers tested the combination of PSA, a kallikrein four-panel blood test, and MRI in selecting patients for biopsy. 

  • Patients were sent to MRI if they had PSA scores of 3.0 ng/mL or higher and kallikrein scores of 7.5% or higher; those with abnormal MRI scans got targeted biopsy. 

The researchers tested the ProScreen protocol in a study of 61.2k men, with 15.3k invited to screening and 7.7k getting screened. Over a preliminary three-year follow-up period, researchers found …

  • 9.7% of men met the PSA threshold for a suspicious lesion; this fell to 6.8% after the kallikrein test and 2.7% after MRI, illustrating the protocol’s ability to reduce biopsies
  • Biopsy yield for high-grade cancer was 1.7%, which an editorial called a “remarkably high yield”
  • Overdetection of low-grade disease was 0.4%, compared to 3.2% in a comparable previous study

In a second study, this one in JAMA Oncology, researchers performed a meta-analysis of 80.1k men from 12 studies in which MRI was used to direct patients to prostate biopsy after PSA testing, finding that MRI-directed protocols had …

  • Higher odds of detecting clinically significant prostate cancer (OR=4.15) compared to PSA screening alone
  • Lower odds ratio for biopsy (OR=0.28)
  • Lower odds ratio for detecting clinically insignificant cancer (OR=0.34)

Finally, a secondary analysis in JAMA of a large UK trial illustrates the challenges of prostate screening without MRI guidance. Researchers reviewed 15-year outcomes of the Cluster Randomized Trial of PSA Testing for Prostate Cancer (CAP), a study of 415k men,196k of whom were screened from 2002 to 2009 without the use of MRI, finding … 

  • PSA screening increased detection of low-grade cancer (2.2% vs. 1.6%) but not intermediate or high-grade disease
  • Screening reduced prostate cancer mortality by a small amount (0.69% vs. 0.78%)

The Takeaway

Taken together, new studies offer a roadmap toward making MRI an integral part of prostate screening, such that perhaps in years to come it can join other cancer tests as a population-based screening tool.

AI Speeds Up MRI Scans

Posted on by Brian Casey

In our last issue, we reported on a new study underscoring the positive return on investment when deploying radiology AI at the hospital level. This week, we’re bringing you additional research that confirms AI’s economic value, this time when used to speed up MRI data reconstruction. 

While AI for medical image analysis has garnered the lion’s share of attention, AI algorithms are also being developed for behind-the-scenes applications like facilitating staff workflow or reconstructing image data. 

  • For example, software developers have created solutions that enable scans to be acquired faster and with less input data (such as radiation dose) and then upscaled to resemble full-resolution images. 

In the new study in European Journal of Radiology, researchers from Finland focused on whether accelerated data reconstruction could help their hospital avoid the need to buy a new MRI scanner. 

  • Six MRI scanners currently serve their hospital, but the radiology department will be losing access to one of them by the end of the year, leaving them with five. 

They calculated that a 20% increase in capacity per remaining scanner could help them achieve the same MRI throughput at a lower cost; to test that hypothesis they evaluated Siemens Healthineers’ Deep Resolve Boost algorithm. 

  • Deep Resolve Boost uses raw-data-to-image deep learning reconstruction to denoise images and enable rapid acceleration of scan times; a total knee MRI exam can be performed in just two minutes. 

Deep Resolve Boost was applied to 3T MRI scans of 78 patients acquired in fall of 2023, with the researchers finding that deep learning reconstruction… 

  • Reduced annual exam costs by 399k euros compared to acquiring a new scanner
  • Enabled an overall increase in scanner capacity of 20-32%
  • Had an acquisition cost 10% of the price of a new MRI scanner, leading to a cost reduction of 19 euros per scan
  • Was a lower-cost option than operating five scanners and adding a Saturday shift

The Takeaway

As with last week’s study, the new research demonstrates that AI’s real value comes from helping radiologists work more efficiently and do more with less, rather than from direct reimbursement for AI use. It’s the same argument that was made to promote the adoption of PACS some 30 years ago – and we all know how that turned out.

MRI’s Value for Prostate Screening

Posted on by Brian Casey

Among cancer screening tests, prostate screening could be the most problematic. But a new study published this week in JAMA Network Open offers guidance on the role that MRI can play in making prostate screening more effective – and opening the door to population-based screening.

The problem with prostate screening is that PSA tests often discover disease that’s either indolent or slow-growing. 

  • This can lead to a cascade of interventions that are expensive and have harms of their own. 

But prostate cancer remains a common – and deadly – cancer, with 1.5M cases globally in 2022, and it’s the second most commonly occurring cancer in men after lung cancer.

  • Given these statistics, there has to be a way to perform prostate screening more effectively.

MRI offers one such alternative, and a clinical consensus has emerged that performing a single MRI scan after a positive PSA result can help stratify men before biopsy. 

  • In this scenario, men might not be referred to biopsy if their MRI scan is negative, and adoption of this protocol has helped reduce prostate biopsies in PSA-positive men while still detecting clinically significant cancer.   

But if one MRI scan is good, are repeat MRI scans even better? In the new study, Swedish researchers investigated this question in a secondary analysis of the STHLM3-MRI trial, which involved repeat screening of 1.5k men 2-3 years after an original prostate screening.

Of the group who got repeat PSA and MRI screening, 667 men had PSA levels of 3 ng/mL or higher, the threshold for MRI testing, with the repeat scans finding … 

  • 51 men (7.6%) had equivocal lesions (PI-RADS score of 3)
  • 33 men (4.9%) had suspicious lesions (PI-RADS score of 4)
  • Only 10 men (1.5%) had lesions with PI-RADS scores of 4 or greater

The findings led the authors to conclude that cancer detection was “limited” in the second round of PSA and MRI prostate screening, and detection of low-grade tumors was low.

The Takeaway

At first blush, STHLM3-MRI may seem like a negative study, but it actually helps frame the debate over prostate cancer screening and MRI’s role by omitting the need for multiple repeat scans. The results also give clinicians confidence that it’s safe to omit prostate biopsies in men who have a single negative MRI result – a key finding in reducing the downstream costs of any population-based screening program.

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