Tag: MRI

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?

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).

Do Imaging Costs Scare Patients?

Posted on by Brian Casey

A new study in JACR reveals an uncomfortable reality about medical imaging price transparency: Patients who knew how much they would have to pay for their imaging exam were less likely to complete their study. 

Price transparency has been touted as a patient-friendly tool that can get patients engaged with their care while also helping them avoid nasty billing surprises for out-of-pocket costs. 

  • Price transparency is considered to be so important that CMS in 2021 implemented rules requiring hospitals to disclose their standard charges online, as well as post a user-friendly list of their services that includes prices. 

But given that the rules were implemented relatively recently, not much is known about how they might affect patient behavior, such as compliance with recommended follow-up imaging exams.

  • Indeed, a recent study by some of the same authors found that patients are largely unaware of how much their imaging exams will cost them. 

So researchers analyzed data from two previously published studies of patients who either completed or were scheduled for outpatient imaging exams in Southern California. 

  • Patients were asked if they had been told how much their exam would cost them out-of-pocket when they scheduled it. 

Of the 532 patients who were surveyed, researchers found …

  • Only 15% said they knew about their out-of-pocket costs before their imaging exam. 
  • Fewer patients who completed their exams knew their costs compared to those who canceled (12% vs. 22%).
  • Patients who knew their costs were 67% less likely to complete their appointment than those who didn’t (OR=0.33).

So what’s the solution? The researchers suggested that healthcare providers may need to take a more proactive approach to disclosing price information to patients.

  • One possibility would be to integrate pricing discussions into patient-provider communications when ordering imaging exams, rather than relying on patients to seek pricing information on their own. 

The Takeaway

The findings show that medical imaging price transparency is more complicated than just posting a list of prices online and expecting patients to do the rest of the work. Imaging providers may need to get more involved in pricing discussions – the question is whether many of them are ready for it.

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 with AI

Posted on by Brian Casey

A homegrown AI algorithm was able to detect clinically significant prostate cancer on MRI scans with the same accuracy as experienced radiologists. In a new study in Radiology, researchers say the algorithm could improve radiologists’ ability to detect prostate cancer on MRI, with fewer false positives.

In past issues of The Imaging Wire, we’ve discussed the need to improve on existing tools like PSA tests to make prostate cancer screening more precise with fewer false positives and less need for patient work-up.

  • Adding MRI to prostate screening protocols is a step forward, but MRI is an expensive technology that requires experienced radiologists to interpret.

Could AI help? In the new study, researchers tested a deep learning algorithm developed at the Mayo Clinic to detect clinically significant prostate cancer on multiparametric (mpMRI) scans.

  • In an interesting wrinkle, the Mayo algorithm does not indicate tumor location, so a second algorithm – called Grad-CAM – was employed to localize tumors.

The Mayo algorithm was trained on a population of 5k patients with a cancer prevalence similar to a screening population, then tested in an external test set of 204 patients, finding …

  • No statistically significant difference in performance between the Mayo algorithm and radiologists based on AUC (0.86 vs. 0.84, p=0.68)
  • The highest AUC was with the combination of AI and radiologists (0.89, p<0.001)
  • The Grad-CAM algorithm was accurate in localizing 56 of 58 true-positive exams

An editorial noted that the study employed the Mayo algorithm on multiparametric MRI exams.

  • Prostate cancer imaging is moving from mpMRI toward biparametric MRI (bpMRI) due to its faster scan times and lack of contrast, and if validated on bpMRI, AI’s impact could be even more dramatic.

The Takeaway
The current study illustrates the exciting developments underway to make prostate imaging more accurate and easier to perform. They also support the technology evolution that could one day make prostate cancer screening a more widely accepted test.

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.

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