NeXtQSM -- 一种基于混合数据训练的用于数据一致性定量磁化率成像的完整深度学习流程
NeXtQSM -- A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with hybrid data
AI总结：
NeXtQSM提出一种基于混合数据训练的端到端深度学习流程，联合求解QSM的背景场校正与偶极子反演，实现数据一致性，克服误差传播，提供稳健快速的结果。
摘要：
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，获得了与成熟的非学习方法相似的结果。然而，许多当前的深度学习方法不具备数据一致性，需要体内训练数据，或通过连续步骤求解QSM问题，从而导致误差传播。在此，我们旨在克服这些局限性，并开发了一个框架来联合求解QSM处理步骤。我们提出了一种新的混合训练数据生成方法，该方法能够使用结合了QSM模型项和学习正则化器的变分网络，以数据一致的方式实现背景场校正和偶极子反演的端到端训练。我们证明NeXtQSM克服了以往深度学习方法的局限性。NeXtQSM提供了一种新的基于深度学习的定量磁化率图计算流程，将每个处理步骤整合到训练中，并提供稳健且快速的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
