AI Imaging Enhancement: Picture Perfect in Half the Time?
This is part of Elion’s weekly market map series where we break down critical vendor categories and the key players in them. For more, become a member and sign up for our email here.
MRI scanners are a vital diagnostic tool in modern medicine, yet they come with significant financial and operational challenges. New machines can cost upward of $1 million, and each scan can take anywhere from 15 to 45 minutes, limiting patient throughput and straining busy imaging centers.
AI imaging enhancement solutions aim to tackle these obstacles by leveraging advanced deep learning algorithms to improve image quality and reduce scan times—ultimately boosting efficiency and patient satisfaction.
Addressing MRI Tech Pain Points
Prolonged MRI sessions aren’t just inconvenient; they can compromise image quality if a patient can’t stay still for the entire duration. Historically, upgrading to faster or more powerful machines was the only way to reduce scan times, but that often came with a hefty price tag. On the other hand, AI-powered enhancement refines images after they’re captured, effectively extending the lifespan of existing MRI hardware.
How AI Imaging Enhancement Works
The AI workflow typically starts when clinicians opt for a shorter or lower-resolution scan sequence to save time. That noisier or partial dataset is then processed by machine learning software, which has been trained on extensive repositories of high-fidelity “gold-standard” MRI images.
Using techniques like convolutional neural networks (CNNs) or generative adversarial networks (GANs), the software cleans up artifacts, restores sharp details, and outputs near-high-resolution images ready for diagnosis.
Differentiating AI Imaging Enhancement Vendors
Though these AI solutions share a common goal, each offers distinct capabilities. As you consider vendors, keep in mind:
Body part and condition approvals: Some solutions are cleared only for specific body regions (e.g., brain and spine), while others extend coverage to musculoskeletal, pediatric, and abdominal scans.
Interoperability: Assess whether the tool works smoothly with your existing MRI manufacturer, including older machines or specific model generations.
Reduction in scan time: This can vary widely—from a 20% reduction in certain scans to a 50% decrease or more in others. Clarify if the AI can maintain (or improve) diagnostic quality at each new efficiency threshold.
Testing and contrast settings: Some products may only be validated for certain field strengths (1.5T vs. 3T) or particular contrast protocols.
Regulatory approvals & certifications: FDA 510(k) in the U.S., CE Mark in Europe, and other region-specific approvals can be pivotal for clinical adoption.
Vendors we’re currently tracking in this space include:
AIRSMed SwiftMR focuses on compatibility with legacy scanners, accelerating MRI scans by up to 50% without requiring hardware upgrades.
Medic Vision iQMR is a vendor-neutral platform that claims to reduce scan times by about 40%, with FDA clearance for all body parts and applications across both MRI and low-dose CT imaging.
Subtle Medical integrates directly into workflows, processing images to reduce noise and sharpen details, with deployment options that include both cloud and on-premise setups.
Looking Ahead
As AI algorithms continue to evolve, we expect these solutions to tackle more complex imaging scenarios—from contrast-enhanced studies to specialized protocols for neurology or cardiology. We’re also likely to see deeper integration with hospital IT systems, electronic health records, and advanced analytics that guide radiologists on optimal scan protocols for each patient, potentially bringing AI imaging enhancement and CDS under one umbrella.
While adoption will hinge on factors like regulatory approvals and reimbursement structures, solutions in this category have the potential to reshape the MRI and broader imaging landscape, delivering speedier, sharper scans—and a better overall experience for both clinicians and patients.