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Anisotropic diffusion phantom technology Neuroimaging reference standards for MRI, data harmonisation solutions for CNS trials, imaging biomarker studies 🧠 ✉️ contact@preoperativeperformance.com
We're delighted to share news of a new publication, featuring PreOperative Performance's anisotropic diffusion phantom technology, in the Magnetic Resonance in Medicine journal.
onlinelibrary.wiley.com/doi/10.1002/...
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… standardized physical phantoms will be essential for quality assurance, protocol optimization, and cross-site harmonization."
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onlinelibrary.wiley.com/doi/10.1002/...
… the phantom supported consistent measurements across a range of fiber geometries, particularly under high angular resolution acquisitions.
As advanced diffusion imaging continues to expand in clinical and research settings…
From the paper:
"This study demonstrated that the PreOperative Performance anisotropic phantom is a reliable and effective tool for evaluating the repeatability of advanced dMRI metrics. Despite differences in model assumptions and sensitivities…
In this paper, the phantom hardware, and white matter mimicking modules, are used to assess the repeatability of high-order diffusion tensor models, addressing the challenges presented by volumes with complex geometry such as crossing white matter fibers.
We're delighted to share news of a new publication, featuring PreOperative Performance's anisotropic diffusion phantom technology, in the Magnetic Resonance in Medicine journal.
onlinelibrary.wiley.com/doi/10.1002/...
More news coming shortly!
A significant milestone for our tech, but a bigger win for multi-site imaging studies!
If you’re running multi-site MRI studies, how much of your budget is consumed by travel logistics?
📬 DM us to collaborate!
(6/6)
By addressing systemic biases in MR systems with anisotropic diffusion phantom measurements, perceived gain from adding more human volunteers drops significantly. More actionable and quantitative data is acquired faster, for a fraction of the budget.
(5/6)
The Modeling Results:
▶️ Phantom only: 1.6x to 5.7x ROI on a single project,
▶️ Hybrid scenario: 35%-60% savings, combining phantom measurements and limited human subjects,
▶️ Speed: A study that once took years can now be completed in a day between participating sites, and repeated at will.
(4/6)
Our white matter modules are fully parameterized, temporally stable, and emulate physiological responses, without the biological variance of a human volunteer.
(3/6)
At PreOperative Performance, we’ve engineered a way out.
We’ve demonstrated unit-to-unit equivalency with our anisotropic diffusion phantom technology.
(2/6)
The Human Gold Standard in neuroimaging has a scaling problem.
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Traditional multi-site MRI harmonization relies on traveling subjects. It’s a $300k investment that takes 24+ months from first planning steps to publication.
A short thread…
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These results support the use of anisotropic diffusion phantoms for identification of site-specific variability, and quality control solutions for multicenter diffusion MRI studies.
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The study presents an imaging analysis pipeline that yields equivalency tolerances across replica anisotropic diffusion phantoms, demonstrating consistent structural, and physiological 'white matter like' behavior, across individual phantom builds.
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We're pleased to announce that our abstract, “Characterizing Fiber Bundles in an Anisotropic Diffusion Phantom: Comparison of Phantom Replicas” (Abstract # 01873) has been accepted to ISMRM 2026 this May. 🧠
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PreOperative Performance HQ the morning after the biggest winter storm of the season so far.
Neurological reference standards can solve non-obvious and costly problems in neuroimaging data quality.
A short thread…
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#MRI #dMRI #diffusionMRI #neurosky #neuro #neurorad #neuroradiology #neuroscience #imagingstudies #radiologyAI
This is how anisotropic diffusion phantom reference standards add real value - turning subtle, hard-to-spot inconsistencies into clear, actionable information, ensuring that downstream analysis and clinical decisions are based on trustworthy data.
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And relying on anatomy alone, such as checking the corpus callosum on an axial slice, is often not enough to confirm that the b-vectors and the image are aligned.
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These issues are more common than most people expect, especially when software packages use different orientation conventions (e.g., LAS vs. RAS).
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In our white-matter modules, any L-R orientation mismatch between the image and the b-vectors becomes immediately visible, the diffusion signal falls perpendicular to the fiber bundle axes on both diagonal bundles.
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PreOperative Performance’s ground-truth phantoms with white matter simulating modules enable a capability that can be missed in routine practice - clear, objective detection of orientation mismatches and diffusion encoding errors.
(2/6)
Neurological reference standards can solve non-obvious and costly problems in neuroimaging data quality.
A short thread…
(1/6)
#MRI #dMRI #diffusionMRI #neurosky #neuro #neurorad #neuroradiology #neuroscience #imagingstudies #radiologyAI
It’s the last day of #RSNA today and #AI is a central topic. PreOperative Performance has validation solutions for AI projects and multicenter studies.
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✉️ contact@preoperativeperformance.com
Diffusion MRI is sensitive to scanner and protocol differences which is why multi-site trials, studies and AI tools struggle with trust in the numbers.
This thread introduces a phantom MRI data analysis tool, in use with our phantom technology, measuring the reproducibility of phantom data.
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DM us or email for more information. Details in bio!
#dMRI #neurosky #neurorad #MRI #neuroscience
This demonstrates that the modules behave as a stable reference, useful for multi-site MRI data comparisons, longitudinal QA & validating AI tools that depend on diffusion metrics.
This is one of the building blocks for quality controlled standards in diffusion MRI across sites and vendors.
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Using this analysis, we are able to see that our bundle has some very high FA values. Using the profiles, we can use statistical methods (TOST) to compare the bundles and assess equivalence of the two profiles to a tolerance level of 0.03, a negligible FA difference.
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