Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities,NMR in Biomedicine

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Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities
NMR in Biomedicine ( IF 2.7 ) Pub Date : 2020-03-23 , DOI: 10.1002/nbm.4292
Woojin Jung ₁ , Steffen Bollmann _{2,

3} , Jongho Lee ₁

Affiliation

Quantitative susceptibility mapping (QSM) has gained broad interest in the field by extracting bulk tissue magnetic susceptibility, predominantly determined by myelin, iron and calcium from magnetic resonance imaging (MRI) phase measurements in vivo. Thereby, QSM can reveal pathological changes of these key components in a variety of diseases. QSM requires multiple processing steps such as phase unwrapping, background field removal and field-to-source inversion. Current state-of-the-art techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and require a careful choice of regularization parameters. With the recent success of deep learning using convolutional neural networks for solving ill-posed reconstruction problems, the QSM community also adapted these techniques and demonstrated that the QSM processing steps can be solved by efficient feed forward multiplications not requiring either iterative optimization or the choice of regularization parameters. Here, we review the current status of deep learning-based approaches for processing QSM, highlighting limitations and potential pitfalls, and discuss the future directions the field may take to exploit the latest advances in deep learning for QSM.

中文翻译：

使用深度学习的定量敏感性映射概述：现状、挑战和机遇

定量磁化率映射 (QSM) 通过提取大块组织磁化率在该领域获得了广泛的兴趣，该磁化率主要由体内磁共振成像 (MRI) 相位测量中的髓鞘、铁和钙确定。因此，QSM可以揭示这些关键成分在多种疾病中的病理变化。QSM 需要多个处理步骤，例如相位展开、背景场去除和场到源反转。当前最先进的技术利用迭代优化程序来解决反演和背景场校正，这在计算上是昂贵的并且需要仔细选择正则化参数。随着最近使用卷积神经网络解决不适定重建问题的深度学习取得成功，QSM 社区也采用了这些技术，并证明 QSM 处理步骤可以通过有效的前馈乘法来解决，而不需要迭代优化或正则化参数的选择。在这里，我们回顾了基于深度学习的 QSM 处理方法的现状，强调了局限性和潜在的缺陷，并讨论了该领域在利用 QSM 深度学习的最新进展方面可能采取的未来方向。

更新日期：2020-03-23

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