A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation

• Published in: Medical image analysis Vol. 95; p. 103173
• Main Authors: Zhang, Ming, Feng, Ruimin, Li, Zhenghao, Feng, Jie, Wu, Qing, Zhang, Zhiyong, Ma, Chengxin, Wu, Jinsong, Yan, Fuhua, Liu, Chunlei, Zhang, Yuyao, Wei, Hongjiang
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.07.2024
• Subjects: Implicit neural representation; Phase compensation; Quantitative susceptibility mapping; Unsupervised learning; Quantitative susceptibility mapping; Implicit neural representation; Unsupervised learning; Phase compensation
• ISSN: 1361-8415; 1361-8423; 1361-8423
• DOI: 10.1016/j.media.2024.103173

•Subject-specific and unsupervised deep learning for QSM reconstruction.•Integration of implicit continuous signal representation and explicit regularizations.•Phase compensation strategy for an accurate physical model.•Improved accuracy and quality compared with established methods. Quantitative susceptibility mapping (QSM) is an MRI-based technique that estimates the underlying tissue magnetic susceptibility based on phase signal. Deep learning (DL)-based methods have shown promise in handling the challenging ill-posed inverse problem for QSM reconstruction. However, they require extensive paired training data that are typically unavailable and suffer from generalization problems. Recent model-incorporated DL approaches also overlook the non-local effect of the tissue phase in applying the source-to-field forward model due to patch-based training constraint, resulting in a discrepancy between the prediction and measurement and subsequently suboptimal QSM reconstruction. This study proposes an unsupervised and subject-specific DL method for QSM reconstruction based on implicit neural representation (INR), referred to as INR-QSM. INR has emerged as a powerful framework for learning a high-quality continuous representation of the signal (image) by exploiting its internal information without training labels. In INR-QSM, the desired susceptibility map is represented as a continuous function of the spatial coordinates, parameterized by a fully-connected neural network. The weights are learned by minimizing a loss function that includes a data fidelity term incorporated by the physical model and regularization terms. Additionally, a novel phase compensation strategy is proposed for the first time to account for the non-local effect of tissue phase in data consistency calculation to make the physical model more accurate. Our experiments show that INR-QSM outperforms traditional established QSM reconstruction methods and the compared unsupervised DL method both qualitatively and quantitatively, and is competitive against supervised DL methods under data perturbations.