MotionDPS is a unified Bayesian framework for motion-compensated 3D MRI reconstruction that alternates diffusion posterior updates with proximal optimization for rigid motion and coil sensitivity estimation using pretrained 3D complex-valued score-based diffusion models as anatomical priors.
Diffusion posterior sampling for general noisy inverse problems
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Noise injection into plug-and-play algorithms using pretrained score-based diffusion denoisers optimizes a Gaussian-smoothed objective and yields better reconstructions for severely ill-posed imaging tasks.
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MotionDPS: Motion-Compensated 3D Brain MRI Reconstruction
MotionDPS is a unified Bayesian framework for motion-compensated 3D MRI reconstruction that alternates diffusion posterior updates with proximal optimization for rigid motion and coil sensitivity estimation using pretrained 3D complex-valued score-based diffusion models as anatomical priors.
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Stochastic Generative Plug-and-Play Priors
Noise injection into plug-and-play algorithms using pretrained score-based diffusion denoisers optimizes a Gaussian-smoothed objective and yields better reconstructions for severely ill-posed imaging tasks.