Quantitative Susceptibility Mapping through Model-based Deep Image Prior
  (MoDIP)
Zhuang Xiong Yang Gao Yin Liu Amir Fazlollahi Peter Nestor Feng Liu Hongfu Sun
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.