Keywords: Susceptibility, Quantitative Susceptibility mapping, implicit neural representationThis study introduced an unsupervised deep learning-based method for QSM reconstruction using implicit neural representation (INR-QSM), a training databases-free method for high-quality QSM reconstruction. In INR-QSM, the susceptibility map was represented as a continuous function of the spatial coordinates. A coordinate-based multilayer perceptron (MLP) parameterized this function, took the coordinate as input and predicted the susceptibility value at the corresponding spatial location. The parameters of MLP were updated by minimizing a custom cost function. Preliminary results on two different datasets demonstrated the potential of INR for unsupervised QSM reconstruction.
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