Researchers at St. Joseph’s Hospital and McMaster University have published a peer-reviewed paper in Magnetic Resonance Materials in Physics, Biology and Medicine featuring PreOperative Performance’s anisotropic diffusion phantom technology.
The article demonstrates the potential for the diffusion phantom to help researchers establish a quality threshold harmonizing and standardizing DTI metrics when collected from different MRI systems, physical locations and times.
Diffusion tensor imaging (DTI) is an MRI technology that enables 3D visualization of the brain’s myelin coating—the fatty substance that protects neurons. Since myelin can be affected by various diseases, diffusion-based brain scans have enormous potential for diagnostics and treatment.
“Assessing measurement consistency of a diffusion tensor imaging (DTI) quality control (QC) anisotropy phantom,” documents how the team used a phantom to evaluate 32-direction DTI sequences on three different 3 Tesla MRI systems (produced by GE Healthcare, Siemens, and Philips respectively).
From the paper’s abstract:
DTI is a valuable non-invasive imaging modality with applications that include pre-operative surgical planning and TBI assessment. There are a wealth of imaging biomarkers that can be derived from this method, but until now the instrumentation to standardise quantitative biomarkers has been unavailable.
In this study, a DTI QC phantom was employed to demonstrate that repeatability and reproducibility of MRI data quality was possible to achieve, controlling for repeated measurements and vendor variables. DTI scalar metrics were shown to be consistent through the use of the DTI QC phantom.
This can enable large scale imaging studies, the identification of imaging biomarkers for early disease detection, injury characterisation and pathology assessment, and ultimately improved patient outcomes from neurological interventions.
Typically, MRI studies involve multiple machines in multiple locations, which presents a variety of challenges for researchers. In the past, travelling human phantoms, vendor-provided standardization phantoms, and data harmonization have all been used to help researchers synthesize study all have significant limitations that don’t account for vendor-vendor differences in the context of multi-site and ‘Big Data’ DTI application settings.
“The PreOperative Performance Phantom presented provides a potential solution for comparing temporal and multi-site/multivendor DTI data,” said Dr. Michael D. Noseworthy, professor of Electrical and Computer Engineering at McMaster University and co-director of the McMaster School of Biomedical Engineering, who co-authored the paper. “It demonstrates that it’s possible to reliably compare data obtained across imaging platforms, potentially providing vendors the ability to standardize their protocols.”
“The results suggest several exciting directors,” says PreOperative Performance CEO Fergal Kerins, who contributed data curation, methodology, resources and visualization support to the study. “With the efficacy of this technology as a harmonization instrument demonstrated, we have the first piece of validation to work with data taken across different systems and field strengths. We can support research findings in various fields, such neurodegenerative diseases, TBI, neuro-oncology, while developing training tools for AI and machine learning.”
“In the long term, our goal is to deliver a quality control solution for neuroimaging that gives neurosurgeons greater confidence in the quality of the data used to create their surgical plans,” says Kerins. “That would contribute to elevated quality of life for patients, higher performance for clinical teams and lower costs for healthcare providers.”
PreOperative Performance plans to help expand DTI’s diagnostic capability for characterizing traumatic brain injuries (TBIs) and creating personalized treatment plans. Phantoms may also contribute to efforts to characterize and quantify imaging biomarkers, such as Fractional Anisotropy or Mean Diffusivity, that may help to better detect neurodegenerative diseases earlier. Earlier diagnosis would also mean more opportunities to halt progression. The company also plans to support other clinical and research efforts to run retrospectives designed to improve the standard of care in neuro-oncology.
The Synapse Life Sciences Consortium’s SOPHIE program, which facilitates pharmaceutical and medtech projects in Ontario in collaboration with FedDev Ontario, supported this work.
About PreOperative Performance
Based in Toronto, Canada, PreOperative Performance is a seed-stage company with unique, proprietary technology that supports MR scan accuracy. Its vision is for every patient and clinician to enter treatment with the confidence that they’re working from the best-possible MR data. PreOperative Performance’s products and programs help to accelerate the rate of successful outcomes from neurological procedures.
For further information:
Fergal Kerins, CEO and founder, PreOperative Performance
contact@preoperativeperformance.com
