QSMDiff: Unsupervised 3D Diffusion Models for Quantitative
  Susceptibility Mapping
Zhuang Xiong Wei Jiang Yang Gao Feng Liu Hongfu Sun
Quantitative Susceptibility Mapping (QSM) dipole inversion is an ill-posed
inverse problem for quantifying magnetic susceptibility distributions from MRI
tissue phases. While supervised deep learning methods have shown success in
specific QSM tasks, their generalizability across different acquisition
scenarios remains constrained. Recent developments in diffusion models have
demonstrated potential for solving 2D medical imaging inverse problems.
However, their application to 3D modalities, such as QSM, remains challenging
due to high computational demands. In this work, we developed a 3D image
patch-based diffusion model, namely QSMDiff, for robust QSM reconstruction
across different scan parameters, alongside simultaneous super-resolution and
image-denoising tasks. QSMDiff adopts unsupervised 3D image patch training and
full-size measurement guidance during inference for controlled image
generation. Evaluation on simulated and in-vivo human brains, using
gradient-echo and echo-planar imaging sequences across different acquisition
parameters, demonstrates superior performance. The method proposed in QSMDiff
also holds promise for impacting other 3D medical imaging applications beyond
QSM.