Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities. (23rd March 2020)

• Record Type: Journal Article
• Authors: Jung, Woojin; Bollmann, Steffen; Lee, Jongho
• Other Names: Zhang Hui guestEditor.; Alexander Daniel C. guestEditor.; Shen Dinggang guestEditor.; Yap Pew‐Thian guestEditor.
• Is Part Of: NMR in biomedicine. Volume 35:Number 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-23
• Subjects: background field correction -- deep learning -- dipole inversion -- quantitative susceptibility mapping -- unwrapping; Nuclear magnetic resonance -- Periodicals; Magnetic Resonance Spectroscopy -- Periodicals; 574
• DOI: 10.1002/nbm.4292
• Languages: English
• ISSNs: 0952-3480

Abstract

Abstract : 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. Abstract : 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.