Adaptive convolution for including a-priori information in deep learning models for quantitative susceptibility mapping

Deep learning models used for solving dipole inversion of quantitative susceptibility mapping typically lack an integration of a-priori information. We show for the first time that information of voxel-size and field-of-view orientation with respect to B0 can be incorporated into network models by deploying adaptive convolution. Various network models were trained for 170 epochs to solve dipole inversion on synthetic data with arbitrary orientation and voxel-sizes. Adaptive convolution models outperform conventional models in computing susceptibility maps from arbitrarily oriented field distributions with anisotropic voxel sizes and allows a reduction of training time.