Keywords: Quantitative Imaging, Machine Learning/Artificial IntelligenceClinical translation of quantitative MRI (qMRI) is challenged by the multiple dependencies MR signal has on different tissue compartments. We previously introduced a neural network designed for single subject analysis (BIOPHYSICSS-DL) that overcomes the multiple tissue dependencies of qMRI and produces maps of source content. BIOPHYSICSS-DL has previously focused to myelin and iron quantification in the brain but in this work we expand that model to include a free water compartment. The network also used exclusively quantitative MRI metrics but the expansion of this model was accomplished with weighted MRI data which is another critical step to eventual clinical adoption.
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