Approximating R1 and R2: a quantitative approach to clinical weighted MRI

Abstract

AbstractWeighted MRI images are widely used in clinical as well as open-source neuroimaging databases. Weighted images such as T1-weighted, T2-weighted, and proton density-weighted (T1w, T2w, and PDw, respectively) are used for evaluating the brain’s macrostructure; however, their values cannot be used for microstructural analysis, since they lack physical meaning. Quantitative MRI (qMRI) relaxation rate parameters (e.g., R1 and R2), and related relaxivity coefficients, do contain microstructural physical meaning.Nevertheless, qMRI is rarely done in large-scale clinical databases.Currently, the weighted images ratio T1w/T2w is used as a quantifier to approximate the brain’s microstructure. In this paper, we propose three additional quantifiers that approximate quantitative maps, which can help bring quantitative MRI to the clinic for easy use.Following the signal equations and using simple mathematical operations, we combine the T1w, T2w, and PDw images to estimate the R1 and R2.We find that two of these quantifiers (T1w/PDw and T1w/ln(T2w)) can serve as a semi-quantitative proxy for R1, and that (ln(T2w/PDw)) can approximate R2.We find that this approach also can be applied to T2w scans taken from widely available DTI datasets. We tested these quantifiers on bothin vitrophantom andin vivohuman datasets. We found that the quantifiers accurately represent the quantitative parameters across datasets. Finally, we tested the quantifiers within a clinical context, and found that they retain tissue information across datasets. Our work provides a simple pipeline to enhance the usability and quantitative accuracy of MRI weighted images.