Keywords: Machine Learning/Artificial Intelligence, RelaxometryRecovery of T2 distribution of tissue from MRI data acquired at multiple echo times has the potential to be used as a biomarker for the assessment of various pathologies, including stroke and epilepsy, investigation of neurodegenerative diseases, and tumor characterization. Current deep neural networks (DNN) for T2 distribution recovery are highly sensitive to variations in the acquisition parameters such as different echo times. We present a new physically-driven DNN model that encodes the TE acquisition parameters as part of its architecture. Our model accurately recovers the T2 distribution, regardless of variations in SNR and in the acquisition parameters.
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