bibtext: @InProceedings{ Ada_Physics_MICCAI2024,
                   author = { Adams-Tew, Samuel I. and Odéen, Henrik and Parker, Dennis L. and Cheng, Cheng-Chieh and Madore, Bruno and Payne, Allison and Joshi, Sarang },
                   title = { { Physics informed neural networks for estimation of tissue properties from multi-echo configuration state MRI } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15011 },
                   month = {October},
                   pages = { pending },
                }
abstract: This work investigates the use of configuration state imaging together with deep neural networks to develop quantitative MRI techniques for deployment in an interventional setting. A physics modeling technique for inhomogeneous fields and heterogeneous tissues is presented and used to evaluate the theoretical capability of neural networks to estimate parameter maps from configuration state signal data. All tested normalization strategies achieved similar performance in estimating T2 and T2*. Varying network architecture and data normalization had substantial impacts on estimated flip angle and T1, highlighting their importance in developing neural networks to solve these inverse problems. The developed signal modeling technique provides an environment that will enable the development and evaluation of physics-informed machine learning techniques for MR parameter mapping and facilitate the development of quantitative MRI techniques to inform clinical decisions during MR-guided treatments.
title: Physics informed neural networks for estimation of tissue properties from multi-echo configuration state MRI
authors: Adams-Tew, Samuel I. Odéen, Henrik Parker, Dennis L. Cheng, Cheng-Chieh Madore, Bruno Payne, Allison Joshi, Sarang