FrequencyCT is a frequency-domain zero-shot self-supervised method that creates pseudo-samples via low-frequency anchoring, phase-preserving high-frequency perturbations, and signal-correlated truncation for low-dose CT denoising.
Kevin Zhou
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CDPA scales diffusion-based reconstruction to large 3D volumes by conditioning 2D models on initial 3D reconstructions plus data-consistency alignment, delivering state-of-the-art results on synthetic and real CBCT data.
MozzaVID supplies X-ray CT volumes of mozzarella microstructure for classifying 25 cheese types from 149 samples in three resolution variants.
PSCT-Net introduces a geometry-aware neural framework that uses differentiable back-projection and attention-guided 3D refinement to reconstruct pediatric skull CT from bi-planar X-rays.
citing papers explorer
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FrequencyCT: Frequency Domain Self-supervised Low-dose CT Denoising
FrequencyCT is a frequency-domain zero-shot self-supervised method that creates pseudo-samples via low-frequency anchoring, phase-preserving high-frequency perturbations, and signal-correlated truncation for low-dose CT denoising.
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Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction
CDPA scales diffusion-based reconstruction to large 3D volumes by conditioning 2D models on initial 3D reconstructions plus data-consistency alignment, delivering state-of-the-art results on synthetic and real CBCT data.
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MozzaVID: Mozzarella Volumetric Image Dataset
MozzaVID supplies X-ray CT volumes of mozzarella microstructure for classifying 25 cheese types from 149 samples in three resolution variants.
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PSCT-Net: Geometry-Aware Pediatric Skull CT Reconstruction via Differentiable Back-Projection and Attention-Guided Refinement
PSCT-Net introduces a geometry-aware neural framework that uses differentiable back-projection and attention-guided 3D refinement to reconstruct pediatric skull CT from bi-planar X-rays.