Quantitative Susceptibility Mapping by Controllable Diffusion Models

Motivation: Current deep learning Quantitative Susceptibility Mapping (QSM) methods often rely on rigorous supervised training with paired data of the input and susceptibility maps and are only capable of specific one-to-one reconstructions. Goal(s): In this study, we introduce the Diffusion Model QSM (DM-QSM), a controllable generative model capable of synthesizing high-quality susceptibility maps without the need for supervised training. Approach: The DM-QSM method can produce controllable susceptibility maps with different measurements as the guidance. Results: DM-QSM is versatile and suitable for many-to-one task including QSM super resolution and dipole inversion for both simulated and in-vivo tests. Impact: This manuscript investigates the application of 3D generative models on QSM. It demonstrates robustness against acqusition artifacts for in-vivo test, and shows the potential beyond current tasks and is able to solve inverse problems like single-step QSM reconstruction.