Flow-QSM integrates learned generative priors and physics-based constraints to improve imaging outcomes, suggesting a novel framework for complex imaging challenges.
Flow-QSM provides a unified probabilistic framework that integrates learned generative priors with physics-based constraints for susceptibility mapping. The proposed approach offers a flexible and principled strategy for addressing ill-posed inverse problems in computational imaging.
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Qin et al. (2026) studied this question.
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