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Conditional Denoising Diffusion Probabilistic Models for Metal Artifact Reduction in Computed Tomography

Quattrocchi, Jorn L. (2025) Conditional Denoising Diffusion Probabilistic Models for Metal Artifact Reduction in Computed Tomography.

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Abstract:Metal artifacts in computed tomography (CT) significantly degrade image quality, potentially introducing challenges in diagnostic evaluations. This study investigates the application of advanced diffusion-based deep learning models for metal artifact reduction (MAR) in CT images. Conditional Denoising Diffusion Probabilistic Models (CDDPM), Brownian Bridge Diffusion Models (BBDM), and their superconditional variant (SBBDM) were implemented in this research, trained on a large simulated dataset of CT slices with synthetic metal artifacts.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:31 mathematics, 33 physics, 44 medicine, 54 computer science
Programme:Applied Mathematics MSc (60348)
Link to this item:https://purl.utwente.nl/essays/107022
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