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.

翻译：基于深度学习的定量磁化率成像（QSM）近年来展现出巨大潜力，其成像结果可与传统非学习方法相媲美。然而，当前多数深度学习方法存在数据不一致、依赖活体训练数据或通过分步求解QSM问题导致误差累积等局限。本研究旨在突破上述瓶颈，构建了联合求解QSM处理流程的统一框架。我们提出了一种新型混合训练数据生成方法，通过结合QSM模型项与学习型正则化器的变分网络，实现了以数据一致性方式端到端求解背景场校正与偶极子逆问题。实验证明，NeXtQSM克服了以往深度学习方法的局限性。该技术作为全新的深度学习流程，将各处理步骤整合至训练过程中，能够鲁棒且快速地生成定量磁化率图。