Cone Beam Ct
PulseAugur coverage of Cone Beam Ct — every cluster mentioning Cone Beam Ct across labs, papers, and developer communities, ranked by signal.
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New splat-based method reduces metal artifacts in CT scans
Researchers have developed a new method for reducing artifacts in cone-beam computed tomography (CBCT) scans, particularly those caused by metal implants. This novel approach utilizes a physics-inspired, self-calibratin…
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New splat-based method reduces metal artifacts in CT scans
Researchers have developed a novel splat-based framework for reducing metal artifacts in cone-beam CT scans. This method incorporates a physically grounded polychromatic forward model within a continuous Gaussian repres…
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New CBCT-IQ dataset released for medical image quality assessment
Researchers have introduced CBCT-IQ, a new, publicly available dataset designed to advance image quality assessment in cone-beam computed tomography (CBCT). This dataset contains 1,764 annotated image slices, graded by …
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$K$-NeAS advances multi-material CT reconstruction with neural SDFs
Researchers have developed $K$-NeAS, a novel architecture for scalable multi-material CT reconstruction. This system utilizes neural signed distance functions (SDFs) and a Gaussian Mixture Model (GMM) to automate attenu…
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Differentiable SV-FBP for Cone-Beam CT Shows Robustness to Trajectory Irregularities
Researchers have analyzed the Differentiable Shift-Variant FBP (SV-FBP) framework for cone-beam CT reconstruction, finding it robust to irregular and discontinuous source trajectories. The framework's performance is mor…
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Self-supervised vision transformers show promise for TMJ OA detection
Researchers have explored the effectiveness of self-supervised vision transformers, specifically the DINO family, for detecting temporomandibular joint osteoarthritis (TMJ OA) from cone-beam CT (CBCT) scans. Their study…
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New neural network method reconstructs CT scans with fewer artifacts
Researchers have developed a new self-supervised 3D reconstruction framework for Cone Beam CT (CBCT) that addresses data truncation artifacts. The method utilizes neural scene representations to map spatial coordinates …
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SynthRAD2025 challenge shows AI improves synthetic CT for radiotherapy
The SynthRAD2025 challenge report details advancements in generating synthetic computed tomography (sCT) images for radiotherapy planning. This year's challenge focused on converting MRI or cone-beam CT (CBCT) into CT-e…
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Diffusion models enhance image reconstruction for inverse problems and sparse-view CT
Researchers are developing new methods to improve image reconstruction from limited data using diffusion models. One approach optimizes diffusion priors from a single observation by combining existing models, showing pr…