Abstract：
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 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 min.
摘要：
监督学习方法的数据驱动方法在解决定量敏感性映射（QSM）中的偶极子反演时具有有限的适用性，该方法在不同对象上具有变化的扫描参数。为了解决监督QSM方法中的这个泛化问题，我们提出了一种新颖的基于无训练模型的无监督方法，称为MoDIP（基于模型的深度图像先验）。MoDIP包括一个小的，未训练的网络和数据保真度优化(DFO)模块。网络收敛到一个临时状态，作为图像正则化的隐式先验，而优化过程强制QSM偶极子反演的物理模型。实验结果表明，MoDIP在解决跨不同扫描参数的QSM偶极子反演中具有出色的泛化性。它对病理性大脑QSM表现出鲁棒性，与监督式深度学习方法相比，准确率提高了32%以上。它的计算效率也提高了33%，运行速度比传统的基于DIP的方法快4倍。在4.5分钟内实现3D高分辨率图像重建。
