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.

中文翻译：

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使用隐式神经表示进行定量磁化率绘图的特定于主题的无监督深度学习方法


定量磁化率测绘 (QSM) 是一种基于 MRI 的技术，可根据相位信号估计底层组织的磁化率。基于深度学习 (DL) 的方法在处理具有挑战性的 QSM 重建不适定反问题方面显示出了前景。然而，它们需要大量的配对训练数据，而这些数据通常是不可用的并且存在泛化问题。由于基于补丁的训练约束，最近的模型结合深度学习方法还忽略了应用源场前向模型时组织相的非局部效应，导致预测和测量之间的差异以及随后的次优 QSM 重建。本研究提出了一种基于隐式神经表示（INR）的无监督且针对特定主题的 DL 方法进行 QSM 重建，简称 INR-QSM。 INR 已成为一个强大的框架，可通过利用信号（图像）的内部信息而无需训练标签来学习信号（图像）的高质量连续表示。在 INR-QSM 中，所需的磁敏度图表示为空间坐标的连续函数，由全连接神经网络参数化。通过最小化损失函数来学习权重，该损失函数包括由物理模型合并的数据保真度项和正则化项。此外，首次提出了一种新颖的相位补偿策略，在数据一致性计算中考虑组织相位的非局部影响，使物理模型更加准确。 我们的实验表明，INR-QSM 在定性和定量上都优于传统的已建立的 QSM 重建方法和比较的无监督 DL 方法，并且在数据扰动下与有监督 DL 方法具有竞争力。