Agah Karakuzu, Mathieu Boudreau, Tanguy Duval, Tommy Boshkovski, Ilana Leppert, Jean-François Cabana, Ian Gagnon, Pascale Béliveau, G. Bruce Pike, Julien Cohen-Adad, Nikola Stikov
Article de revue (2020)
Résumé
Magnetic resonance imaging (MRI) has revolutionized the way we look at the human body. However, conventional MR scanners are not measurement devices. They produce digital images represented by “shades of grey”, and the intensity of the shades depends on the way the images are acquired. This is why it is difficult to compare images acquired at different clinical sites, limiting the diagnostic, prognostic, and scientific potential of the technology. Quantitative MRI (qMRI) aims to overcome this problem by assigning units to MR images, ensuring that the values represent a measurable quantity that can be reproduced within and across sites. While the vision for quantitative MRI is to overcome site-dependent variations, this is still a challenge due to variability in the hardware and software used by MR vendors to produce quantitative MRI maps. Although qMRI has yet to enter mainstream clinical use, imaging scientists see great promise in the technique’s potential to characterize tissue microstructure. However, most qMRI tools for fundamental research are developed in-house and are difficult to port across sites, which in turn hampers their standardization, reproducibility, and widespread adoption. To tackle this problem, we developed qMRLab, an open-source software package that provides a wide selection of qMRI methods for data fitting, simulation and protocol optimization Figure 1. It not only brings qMRI under one umbrella, but also facilitates its use through documentation that features online executable notebooks, a user friendly graphical user interface (GUI), interactive tutorials and blog posts.
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