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Found 8 papers, 1 papers with code
CT Material Decomposition using Spectral Diffusion Posterior Sampling
In this work, we introduce a new deep learning approach based on diffusion posterior sampling (DPS) to perform material decomposition from spectral CT measurements.
Diffusion Posterior Sampling for Nonlinear CT Reconstruction
This technique is attractive since it permits a one-time, unsupervised training of a CT prior; which can then be incorporated with an arbitrary data model.
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
Score-based generative modeling, informally referred to as diffusion models, continue to grow in popularity across several important domains and tasks.
High-Fidelity Modeling of Detector Lag and Gantry Motion in CT Reconstruction
We show that modeling detector lag can reduce/remove the characteristic lag artifacts in head imaging in both a simulation study and physical experiments.
Medical Physics
Penalized-Likelihood Reconstruction with High-Fidelity Measurement Models for High-Resolution Cone-Beam Imaging
In a simulation study, GPL-BC was able to achieve lower bias as compared to deblurring followed by FDK as well as a model-based reconstruction method without integration of measurement blur.
Medical Physics
High-Fidelity Modeling of Shift-Variant Focal-Spot Blur for High-Resolution CT
While the high-fidelity models used here are applied using the specifications of a dedicated extremities imaging system, the methods are general and may be applied to optimize imaging performance in any CT system.
Medical Physics
Model-based Iterative Reconstruction for Flat-Panel Cone-Beam CT with Focal Spot Blur, Detector Blur, and Correlated Noise
In this work, we develop a forward model for flat-panel-based systems that includes blur and noise correlation associated with finite focal spot size and an indirect detector (e. g., scintillator).
Medical Physics
Modeling shift-variant X-ray focal spot blur for high-resolution flat-panel cone-beam CT
In both experiments image quality using the shift-variant model was significantly improved over approaches that modeled no blur or only a shift-invariant blur, suggesting a potential means to overcome traditional CBCT spatial resolution and system design limitations.
Medical Physics Optimization and Control
