Improving reproducibility in MRI with vendor-neutral acquisitions and transparent workflows

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Myelin is a key component of the central nervous system. The myelin sheaths insulate axons with a triple effect: allowing fast electrical conduction, protecting the axon, and providing structural support. Myelin is also relevant from a clinical perspective, given that demyelination is often observed in several neurological diseases such as multiple sclerosis Quantitative magnetic resonance imaging (qMRI) assigns units to MR images, making it possible to quantify myelin content across MRI scanners. The one thing that makes a broad clinical adoption of quantitative MRI difficult is the lack of transparency and standardization in qMRI protocols. Most qMR techniques are developed in-house and lack the proper framework to deploy on multiple scanners manufactured by different MRI vendors. To overcome this problem, we propose to develop a vendor-neutral platform that uses open-source pulse sequence development ("Spinbench"), real-time interaction with the scanner, and an open-source image analysis toolbox (qMRLab.org) that aims to bring quantitative MRI under one umbrella. Our long-term goal is to improve the accuracy and reliability of qMRI maps across vendors, in phantoms and in vivo. We will start by focusing on fundamental qMRI parameters (T1), and slowly expand to incorporate other qMRI modalities sensitive to myelin. Using this framework, we will (i) Develop a standardized pipeline for processing qMRI maps; (ii) Validate our pipeline in objects designed to mimic human tissue (phantoms); (iii) Conduct a multi-vendor MRI study of the myelin content in healthy subjects and multiple sclerosis (MS) patients, showing that the protocols are reproducible and clinically feasible in healthy and pathological nervous tissue. MS will be used as a proof of concept because its characteristics enable the comparison of different types of myelin degeneration, from focal inflammation to global neurodegeneration. By reproducing myelin measurements across vendors, we will make it possible for clinicians to use standardized tools for diagnosis, follow-up, and prognosis. Additionally, this will make it possible to remove vendor-dependent bias from artificial intelligence (AI) algorithms. Finally, we plan to generalize this platform to other pathologies and body parts, eventually building an `App store' for quantitative MRI that will be vendor-agnostic, open-source and accessible to clinicians worldwide.