When an early beta test site commissioned a new MRI scanner with a 32-channel head coil, they expected nothing but the best in diagnostic capabilities.
On July 18 2024, Gold Standard Phantoms arrived at the site to demonstrate VERIFLUX. This innovative solution promised to enhance MRI quality assurance processes.
The results showed a poor Signal-to-Noise Ratio (SNR) and Signal Fluctuation-to-Noise Ratio (SFNR), both significantly lower than what would be expected from a high-quality MRI system. Additionally, the Radius of Decorrelation (RDC) was alarmingly low.
In stark contrast, the standard fMRI test performed on a volunteer that same afternoon showed no apparent issues. The discrepancy was puzzling, but it highlighted a crucial point: sometimes, what we don't see can be just as important as what we do.
As Carl Sagan famously said:
This situation perfectly exemplified the wisdom of those words. Intrigued by VERIFLUX's findings, the hospital staff decided to dig deeper. They called in an engineer to conduct a thorough Quality Assurance (QA) check on the 32-channel head coil. The results were conclusive:
What makes this discovery even more remarkable is that the MRI manufacturer had conducted their own tests, which didn't indicate any need for coil replacement. Without VERIFLUX, this critical hardware issue might have gone undetected, potentially compromising patient diagnoses and treatment plans.
Our VERIFLUX gel:
Automated QA Testing:
Provides immediate Green light - Red light feedback and a graph with scanning history upon DICOM image upload.
Enhanced Accuracy:
Utilizes a synthetic polymer gel phantom for reliable imaging, reducing bubbles and mechanical fragility.
Cost-Effectiveness:
Minimizes service interventions and downtime.
Minimized Clinical scientist time:
Enables to assess MRI QA in one simple step
Compliance:
Meets rigorous standards for accurate diagnosis and research.
Full Detailed Analysis available on demand:
VERIFLUX provides a fully detailed datasheet of the scan, on-demand using a set number of requests based on subscription model.