A ray-driven neural base-material field model parameterizes attenuation coefficients as continuous implicit functions and uses auto-differentiation to solve spectral CT reconstruction.
Uncertainr: Uncertainty quantification of end-to-end implicit neural representations for computed tomography
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ShuffleFlow is a variational inference framework that partitions images with pixel-unshuffling and models the joint posterior over sub-images using a shared conditional normalizing flow conditioned on neural field features for scalable Bayesian inverse imaging.
Deep signed distance functions combined with MCMC sampling enable uncertainty-aware reconstruction of left and right ventricles from limited data.
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Ray-driven Spectral CT Reconstruction Based on Neural Base-Material Fields
A ray-driven neural base-material field model parameterizes attenuation coefficients as continuous implicit functions and uses auto-differentiation to solve spectral CT reconstruction.
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ShuffleFlow: Scalable Posterior Inference for Bayesian Inverse Imaging
ShuffleFlow is a variational inference framework that partitions images with pixel-unshuffling and models the joint posterior over sub-images using a shared conditional normalizing flow conditioned on neural field features for scalable Bayesian inverse imaging.
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Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methods
Deep signed distance functions combined with MCMC sampling enable uncertainty-aware reconstruction of left and right ventricles from limited data.