LAUSR

Most MRI sequences used clinically are qualitative or weighted. While such images provide useful information for clinicians to diagnose and monitor disease progression, they lack the ability to quantify tissue damage for more objective assessment. In this study, an algorithm referred to as the T1‐REQUIRE is presented as a proof‐of‐concept which uses nonlinear transformations to retrospectively estimate T1 relaxation times in the brain using T1‐weighted MRIs, the appropriate signal equation, and internal, healthy tissues as references. T1‐REQUIRE was applied to two T1‐weighted MR sequences, a spin‐echo and a MPRAGE, and validated with a reference standard T1 mapping algorithm in vivo. In addition, a multiscanner study was run using MPRAGE images to determine the effectiveness of T1‐REQUIRE in conforming the data from different scanners into a more uniform way of analyzing T1‐relaxation maps. The T1‐REQUIRE algorithm shows good agreement with the reference standard (Lin's concordance correlation coefficients of 0.884 for the spin‐echo and 0.838 for the MPRAGE) and with each other (Lin's concordance correlation coefficient of 0.887). The interscanner studies showed improved alignment of cumulative distribution functions after T1‐REQUIRE was performed. T1‐REQUIRE was validated with a reference standard and shown to be an effective estimate of T1 over a clinically relevant range of T1 values. In addition, T1‐REQUIRE showed excellent data conformity across different scanners, providing evidence that T1‐REQUIRE could be a useful addition to big data pipelines.