Deep learning based Quantitative Susceptibility Mapping (QSM) has shown great potential in recent years, obtaining similar results to established non-learning approaches. Many current deep learning approaches are not data consistent, require in vivo training data or solve the QSM problem in consecutive steps resulting in the propagation of errors. Here we aim to overcome these limitations and developed a framework to solve the QSM processing steps jointly. We developed a new hybrid training data generation method that enables the end-to-end training for solving background field correction and dipole inversion in a data-consistent fashion using a variational network that combines the QSM model term and a learned regularizer. We demonstrate that NeXtQSM overcomes the limitations of previous deep learning methods. NeXtQSM offers a new deep learning based pipeline for computing quantitative susceptibility maps that integrates each processing step into the training and provides results that are robust and fast.

Quantitative susceptibility mapping (QSM) is a useful magnetic resonance imaging (MRI) technique which provides spatial distribution of magnetic susceptibility values of tissues. QSMs can be obtained by deconvolving the dipole kernel from phase images, but the spectral nulls in the dipole kernel make the inversion ill-posed. In recent times, deep learning approaches have shown a comparable QSM reconstruction performance as the classic approaches, despite the fast reconstruction time. Most of the existing deep learning methods are, however, based on supervised learning, so matched pairs of input phase images and the ground-truth maps are needed. Moreover, it was reported that the supervised learning often leads to underestimated QSM values. To address this, here we propose a novel unsupervised QSM deep learning method using physics-informed cycleGAN, which is derived from optimal transport perspective. In contrast to the conventional cycleGAN, our novel cycleGAN has only one generator and one discriminator thanks to the known dipole kernel. Experimental results confirm that the proposed method provides more accurate QSM maps compared to the existing deep learning approaches, and provide competitive performance to the best classical approaches despite the ultra-fast reconstruction.

We propose Nonlinear Dipole Inversion (NDI) for high - quality Quantitative Susceptibility Mapping (QSM) without regularization tuning, while matching the image quality of state - of - the - art reconstruction techniques. In addition to avoiding over - smoothing that these techniques often suffer from, we also ob viate the need for parameter selection. NDI is flexible enough to allow for reconstruction from an arbitrary number of head orientations, and outperforms COSMOS even when using as few as 1 - direction data . This is made possible by a nonlinear forward - model that uses the magnitude as an effective prior, for which we derived a simple gradient descent update rule . We synergistically combine this physics - model with a Variational Network (VN) to leverage the power of d eep l earning in the VaNDI algorithm. This technique adopts the simple gradient descent rule from NDI and learns the network parameters during training, hence requires no additional parameter tuning. Further, we evaluate NDI at 7T using highly accelerated Wave - CAIPI acquisition s at 0.5 mm isotropic resolutio n and demonstrate high - quality QSM from as f e w as 2 - direction data .

Quantitative susceptibility mapping (QSM) is a powerful MRI technique that has shown great potential in quantifying tissue susceptibility in numerous neurological disorders. However, the intrinsic ill-posed dipole inversion problem greatly affects the accuracy of the susceptibility map. We proposed QSMGAN: a 3D deep convolutional neural network approach based on improved U-Net with increased phase receptive field and further refined the network using the WGAN-GP training strategy. Our method could generate accurate and realistic QSM from single orientation phase maps efficiently and performed significantly better than traditional non-learning-based dipole inversion algorithms.