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.

基于模型的深度图像先验 (MoDIP) 是一种新颖的训练无关的无监督方法，利用小型未经训练的网络和数据保真度优化模块来解决变化扫描参数下定量磁化率成像 (QSM) 中的偶极反转问题。实验证明，MoDIP 在解决不同扫描参数下的 QSM 偶极反转中具有很好的泛化能力，对于病理性脑 QSM 的鲁棒性提升了超过32% 的准确度，比受监督深度学习和传统迭代方法提高了33% 的计算效率并且运行速度比常规的基于 DIP 的方法快4倍，使得 3D 高分辨率图像重建在4.5分钟内完成。

基于模型的深层图像先验的定量易感性映射