metal+artifact+reduction Results | Available Intellectual Property | Rensselaer Polytechnic Institute

Search Results - metal+artifact+reduction

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Low-dimensional manifold constrained disentanglement network for metal artifact reduction in CT images
RPI ID: 2020-111-201 Innovation Summary: A deep learning network is designed to reduce metal artifacts in CT imaging by constraining feature disentanglement within a low-dimensional manifold. The model separates anatomical structures from artifact-induced distortions, improving image clarity. It is trained on synthetic and real-world datasets and supports...
Published: 3/10/2026   |   Updated: 3/10/2026   |   Inventor(s): Chuang Niu, Wenxiang Cong, Ge Wang
Keywords(s): disentanglement network, low-dimensional manifold model (LDMM), metal artifact reduction, unpaired learning
Category(s): Computational Science and Engineering
Deep learning for metal artifact reduction in computed tomography images
RPI ID: 2018-008-401, 2018-008-601Innovation Summary:This invention from Rensselaer Polytechnic Institute introduces a deep learning-based framework for reducing metal artifacts in computed tomography (CT) images. The system leverages a neural network trained on paired CT datasets—with and without metal artifacts—to reconstruct high-fidelity images...
Published: 7/21/2025   |   Updated: 7/3/2025   |   Inventor(s): Ge Wang, Lars Gjesteby, Qingsong Yang, Hongming Shan
Keywords(s): computed tomography (CT), deep learning, machine-learning, metal artifact reduction
Category(s): Biotechnology and the Life Sciences