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Abstract #2169

A self-supervised deep learning approach to synthesize weighted images and T1, T2, and PD parametric maps based on MR physics priors

Elisa Moya-Sáez1,2, Rodrigo de Luis-García1, and Carlos Alberola-López1
1University of Valladolid, Valladolid, Spain, 2Fundación Científica AECC, Valladolid, Spain

Synthetic MRI is gaining popularity due to its ability to generate realistic MR images. However, T1, T2, and PD maps are rarely used despite they provide the information needed to synthesize any image modality by applying the appropriate theoretical equations derived from MR physics or through involved simulation procedures. In this work we propose an extension of an state-of-the-art standard CNN to a self-supervised CNN by including MR physics priors to tackle confounding factors not considered in the equations, while bypassing the need of costly simulations. Our approach yields both realistic maps and weighted images from real data.

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