Abstract #2284

Optimal Transport driven Cycle-consistent Generative Adversarial Network (OT-CycleGAN) for an accurate MR water-fat separation

Juan Pablo Meneses^1,2,3, Cristobal Arrieta^1,2, Gabriel della Maggiora^1,2, Pablo Irarrazaval^1,2,3,4, Cristian Tejos^1,2,3, Marcelo Andia^1,2,5, Sergio Uribe^1,2,5, and Carlos Sing Long^1,2,4,6,7

¹Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile, ²ANID – Millennium Science Initiative Program – Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile, ³Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, ⁴Institute for Biological and Medical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile, ⁵Radiology Department, School of Medicine, Pontificia Universidad Catolica de Chile, Santiago, Chile, ⁶Institute for Mathematical & Computational Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile, ⁷ANID – Millennium Science Initiative Program – Millennium Nucleus for Discovery of Structure in Complex Data, Santiago, Chile

Synopsis

MRI water-fat separation is an ill-posed inverse problem usually addressed using complex numerical methods. This problem is related to an MR signal physical model that includes the effects of R₂^* signal decay ratio and main magnetic field inhomogeneities (Δf), which induce non-linearities that are related to the ill-posedness of the inverse problem. In this work, we propose an Optimal Transport driven Cycle-consistent Generative Adversarial Networks (OT-CycleGAN) framework, which is physics-based, and could use partially labeled training data to estimate the non-linear components (R₂^* and Δf), and posteriorly compute the water-fat images through a conventional least-squares approach.

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