The data-driven approach of supervised learning methods has limited applicability in solving dipole inversion in Quantitative Susceptibility Mapping (QSM) with varying scan parameters across different objects. To address this generalization issue in supervised QSM methods, we propose a novel training-free model-based unsupervised method called MoDIP (Model-based Deep Image Prior). MoDIP comprises a small, untrained network and a Data Fidelity Optimization (DFO) module. The network converges to an interim state, acting as an implicit prior for image regularization, while the optimization process enforces the physical model of QSM dipole inversion. Experimental results demonstrate MoDIP's excellent generalizability in solving QSM dipole inversion across different scan parameters. It exhibits robustness against pathological brain QSM, achieving over 32% accuracy improvement than supervised deep learning and traditional iterative methods. It is also 33% more computationally efficient and runs 4 times faster than conventional DIP-based approaches, enabling 3D high-resolution image reconstruction in under 4.5 minutes.

翻译：监督学习的数据驱动方法在解决不同扫描参数下定量磁化率成像（QSM）中的偶极子反演问题时，其适用性受限。为解决监督QSM方法中的泛化问题，我们提出一种新颖的免训练无监督方法MoDIP（基于模型的深度图像先验）。MoDIP包含一个小型未训练网络和一个数据保真优化（DFO）模块。该网络收敛至中间状态，作为图像正则化的隐式先验，而优化过程则强制执行QSM偶极子反演的物理模型。实验结果表明，MoDIP在不同扫描参数下解决QSM偶极子反演时具有出色的泛化能力。它对病理性脑部QSM表现出鲁棒性，相比监督深度学习和传统迭代方法，精度提升超过32%。同时，其计算效率比传统基于DIP的方法提高33%，运行速度快4倍，可在4.5分钟内完成三维高分辨率图像重建。