MotionDPS is a unified Bayesian method that alternates diffusion posterior sampling with proximal optimization to reconstruct motion-compensated 3D brain MRI images, estimate motion, and coil maps in a fully unsupervised manner.
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 method that alternates diffusion posterior sampling with proximal optimization to reconstruct motion-compensated 3D brain MRI images, estimate motion, and coil maps in a fully unsupervised manner.