Physics-Informed DeepMRI: Bridging the Gap from Heat Diffusion to k-Space Interpolation
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In the field of parallel imaging (PI), alongside image-domain regularization methods, substantial research has been dedicated to exploring k-space interpolation. However, the interpretability of these methods remains an unresolved issue. Furthermore, these approaches currently face acceleration limitations that are comparable to those experienced by image-domain methods. In order to enhance interpretability and overcome the acceleration limitations, this paper introduces an interpretable framework that unifies both k-space interpolation techniques and image-domain methods, grounded in the physical principles of heat diffusion equations. Building upon this foundational framework, a novel k-space interpolation method is proposed. Specifically, we model the process of high-frequency information attenuation in k-space as a heat diffusion equation, while the effort to reconstruct high-frequency information from low-frequency regions can be conceptualized as a reverse heat equation. However, solving the reverse heat equation poses a challenging inverse problem. To tackle this challenge, we modify the heat equation to align with the principles of magnetic resonance PI physics and employ the score-based generative method to precisely execute the modified reverse heat diffusion. Finally, experimental validation conducted on publicly available datasets demonstrates the superiority of the proposed approach over traditional k-space interpolation methods, deep learning-based k-space interpolation methods, and conventional diffusion models in terms of reconstruction accuracy, particularly in high-frequency regions.
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