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

TL;DR

This paper reviews how deep learning techniques are transforming quantitative susceptibility mapping (QSM) in MRI by improving processing efficiency and accuracy, discussing current methods, challenges, and future opportunities in the field.

Contribution

It provides a comprehensive overview of deep learning applications in QSM, highlighting recent advancements, limitations, and future research directions.

Findings

Deep learning accelerates QSM processing by replacing iterative methods.

CNN-based approaches reduce computational costs and parameter tuning.

Current methods show promise but face challenges like generalization and robustness.

Abstract

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.