Approximating <scp>R1</scp> and <scp>R2</scp> : A Quantitative Approach to Clinical Weighted <scp>MRI</scp>

Weighted 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, as they lack physical meaning. Quantitative MRI (qMRI) relaxation rate parameters (e.g., R1 and R2) 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 test 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 maps. We find that two of these quantifiers (T1w/PDw and T1w/ln(T2w)) can approximate R1, and that (ln(T2w/PDw)) can approximate R2, in 3 datasets that were tested. We find that this approach also can be applied to T2w scans taken from widely available DTI (Diffusion Tensor Imaging) datasets. We tested these quantifiers on both in vitro phantom and in vivo human 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 are robust across datasets. Our work provides a simple pipeline to enhance the usability and quantitative accuracy of MRI weighted images.