  Quantitative Susceptibility Mapping (QSM) can estimate the underlying tissue
magnetic susceptibility and reveal pathology. Current deep-learning-based
approaches to solve the QSM inverse problem are restricted on fixed image
resolution. They trained a specific model for each image resolution which is
inefficient in computing. In this work, we proposed a novel method called
Meta-QSM to firstly solve QSM reconstruction of arbitrary image resolution with
a single model. In Meta-QSM, weight prediction was used to predict the weights
of kernels by taking the image resolution as input. The proposed method was
evaluated on synthetic data and clinical data with comparison to existing QSM
reconstruction methods. The experimental results showed the Meta-QSM can
effectively reconstruct susceptibility maps with different image resolution
using one neural network training.
