# Overview of quantitative susceptibility mapping using deep learning -- Current status, challenges and opportunities

arXiv: https://arxiv.org/abs/1912.05410 · HTML: https://mlsanity.com/paper/1912.05410/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [DIFFnet: Diffusion parameter mapping network generalized for input diffusion gradient schemes and bvalues](https://mlsanity.com/paper/2102.02463/) (2021)
- [CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping](https://mlsanity.com/paper/2210.06330/) (2022)
- [DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging](https://mlsanity.com/paper/2209.04504/) (2022)
- [NeXtQSM -- A complete deep learning pipeline for data-consistent quantitative susceptibility mapping trained with hybrid data](https://mlsanity.com/paper/2107.07752/) (2021)
- [TransBoost: A Boosting-Tree Kernel Transfer Learning Algorithm for Improving Financial Inclusion](https://mlsanity.com/paper/2112.02365/) (2021)
- [Subject-specific quantitative susceptibility mapping using patch based deep image priors](https://mlsanity.com/paper/2210.06471/) (2022)
- [IR2QSM: Quantitative Susceptibility Mapping via Deep Neural Networks with Iterative Reverse Concatenations and Recurrent Modules](https://mlsanity.com/paper/2406.12300/) (2024)

## Abstract (arXiv)

Quantitative susceptibility mapping (QSM) has gained broad interests in the field by extracting biological tissue properties, predominantly 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 iterative optimization nor 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.