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 images from under-sampled k-space is a commonly used strategy to reduce scan time. The posterior sampling of diffusion models based on the real measurement data holds significant promise of improved reconstruction accuracy. However%2C traditional posterior sampling methods often lack effective data consistency guidance%2C leading to inaccurate and unstable reconstructions. Implicit neural representation (INR) has emerged as a powerful paradigm for solving inverse problems by modeling a signal's attributes as a continuous function of spatial coordinates. In this study%2C we present a novel posterior sampler for diffusion models using INR%2C named DiffINR. The INR-based component incorporates both the diffusion prior distribution and the MRI physical model to ensure high data fidelity. DiffINR demonstrates superior performance on experimental datasets with remarkable accuracy%2C even under high acceleration factors (up to R%3D12 in single-channel reconstruction). Notably%2C our proposed framework can be a generalizable framework to solve inverse problems in other medical imaging tasks.?quality=80&w=800)
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