Join us in establishing the world’s first quality standard for diffusion-weighted MR imaging.
The Journal of Neurosurgery estimates that, globally each year, more than 13.8 million neurosurgeries are performed. It’s a clinical specialty where many procedures are high risk.
Positive outcomes can help patients return to a high quality of life and increase operational efficiency for healthcare systems. Yet target tissues, such as cancers, are often surrounded by healthy tissue that must be travelled through (or traversed) to access and remove diseased tissue.
Unfortunately, neural tissue does not heal and repair itself like muscle. If errors are made in a given procedure, their effects can be permanent, profound and debilitating. The negative effects of these errors diminish quality of life for patients and leave neurological teams demoralised, at the same time as raising costs and slowing throughput for healthcare systems.
Creating a standard for MR imaging is a key quality measure with the power to help overburdened departments run more effectively while supporting value-based care for patients. It could have the power to help overburdened departments run more effectively, while supporting value-based care for patients.
Clinicians each use MR images to examine, diagnose and make surgical plans for their patients. In particular, diffusion MRI (dMRI) can be used to visualise the 3D arrangement of the nerves in the brain. It’s also often used to make surgical plans that avoid high-functioning areas of the brain.
But, there is currently no accepted standard to assess the quality of this data. Building this quality standard would ensure that only high quality, validated MRI data is used to plan patient interventions.
Validated data has the potential to expand diagnostic capability from medical imaging, helping to expand and accelerate diagnosis, improve surgical decision making, reduce error rates, and potentially give patients a higher quality of life.
For clinicians, better access to data-driven tools may lead to better clinical outcomes and lowered costs to healthcare systems and institutions.