Search Results - liam+broughton-neiswanger

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Nanoparticle-enabled X-ray Magnetic Resonance Imaging (NXMR)
RPI ID: 2014-052-401 Innovation Summary: NXMRI combines X‑ray excitation of nanoparticles with MRI readout to localize contrast agents by monitoring changes in resonance parameters (T1/T2/T2*) induced by excitation. Nanoparticles (e.g., nanophosphors) embedded in tissue are energized by X‑rays or UV, altering their local magnetic environment. An MRI...
Published: 3/10/2026   |   Inventor(s): Wenxiang Cong, Matthew Getzin, Lars Gjesteby, Ge Wang
Keywords(s): Automated Shading, Blinds, Coatings\Adhesive Technology, Daylight Filter, Daylight Harvesting, Daylight Management, Electrical Engineering, Energy Management, Lighting & Illumination Technology, Louvers, Material Sciences\Engineering, Mechanical Shading, Optoelectronics Systems, Passive Filter, Photocromic, Shades
Category(s): Biotechnology and the Life Sciences
Method and Apparatus for enhancing sensitivity and resolution in a grating interferometer by machine learning
RPI ID: 2019-001-401 / 2019-001-601 Innovation Summary: A machine‑learning‑enhanced reconstruction pipeline improves both contrast sensitivity and spatial resolution in grating‑based X‑ray and neutron interferometry by fusing complementary image channels through a trained convolutional neural network (CNN). Paired interferometer images—capturing...
Published: 3/10/2026   |   Inventor(s): Ge Wang, Lee Seung Wook, Seho Lee
Keywords(s): convolutional neural network, Grating interferometer, High-sensitive phase contrast image, Image processing, machine-learning
Category(s): Biotechnology and the Life Sciences
HOME: High-Order Mixed-Moment-based Embedding for Representation Learning
RPI ID: 2023-002-301, 2023-002-401 Innovation Summary: This technology introduces a novel embedding framework for representation learning that leverages high-order mixed statistical moments to capture complex data relationships. The system enhances feature extraction by encoding richer structural and distributional information from input data. It...
Published: 3/10/2026   |   Inventor(s): Chuang Niu, Ge Wang
Keywords(s): Artificial Intelligence, deep learning, self-supervised reapresentation learning
Category(s): Computational Science and Engineering
Task-Oriented Deep Learning Image Denoising
RPI ID: 2022-015-201 Innovation Summary: A deep learning-based image denoising method tailored to specific tasks such as segmentation or classification. Unlike generic denoising, it preserves features critical to downstream analysis. The model adapts its strategy based on the intended application, improving performance in real-world scenarios. It is...
Published: 3/10/2026   |   Inventor(s): Jiajin Zhang, Hanqing Chao, Ge Wang, Pingkun Yan
Keywords(s): Artificial Intelligence, deep learning, Image Denoising, image quality, medical imaging
Category(s): Computational Science and Engineering
Decorrelation Mechanism and Dual Neck Autoencoders for Deep Learning
RPI ID: 2022-018-201 Innovation Summary: A dual-neck autoencoder architecture is introduced to improve feature separation and reduce redundancy in deep learning models. The decorrelation mechanism embedded in the encoder layers enhances generalization by minimizing overlap in learned representations. This design allows for more effective training in...
Published: 3/10/2026   |   Inventor(s): Christopher Wiedeman, Ge Wang
Keywords(s): Adversarial Attacks, Attack Transferability, computer vision, Decorrelation, deep learning
Category(s): Computational Science and Engineering
X-Ray Dissectography
RPI ID: 2022-016-401 / 2022-016-301 Innovation Summary: X-ray dissectography enhances radiographic imaging by dissecting overlapping anatomical layers using contrast modulation and spatial filtering. The technique improves depth resolution and diagnostic clarity in complex regions. It is particularly effective for identifying subtle pathologies that...
Published: 3/10/2026   |   Inventor(s): Chuang Niu, Ge Wang
Keywords(s): Atificial Intellegnece, Desectography, X-ray Imaging
Category(s): Biotechnology and the Life Sciences
Stationary Multi-source AI-powered Real-time Tomography (SMART) for Dynamic Cardiac Imaging
RPI ID: 2022-011-201 Innovation Summary: The SMART system uses 29 fixed source-detector pairs arranged on a circular track to perform real-time tomography without mechanical rotation. AI algorithms reconstruct high-resolution 3D images from simultaneous multi-angle data. This architecture improves temporal resolution and reduces wear compared to rotating...
Published: 3/10/2026   |   Inventor(s): Weiwen Wu, Yan Xi, Ge Wang
Keywords(s): cardiac Imaging, computed tomography (CT), deep learning, image reconstruction, Multi-Source, preclinical imaging, real-time
Category(s): Computational Science and Engineering
Self-Supervised Representation Learning Through Multi-Segmental Discriminative Coding
RPI ID: 2022-058-301, 2022-058-401 Innovation Summary: This invention introduces a self-supervised representation learning (SSRL) system that utilizes multi-segmental informational coding to enhance data representation. The SSRL circuitry includes transformer-based components designed to process input data without requiring labeled examples. By leveraging...
Published: 3/10/2026   |   Inventor(s): Ge Wang, Chuang Niu
Keywords(s): Artificial Intelligence, deep learning, self-supervised reapresentation learning
Category(s): Computational Science and Engineering
AI Enables Ultra-Low-Dose CT Reconstruction
RPI ID: 2021-072-401 Innovation Summary: AI-enabled ultra-low-dose CT reconstruction systems use deep learning to restore image quality from low-radiation scans. The models are trained on paired datasets to enhance resolution and contrast while minimizing noise. This approach allows for safer imaging without compromising diagnostic accuracy. It is...
Published: 3/10/2026   |   Inventor(s): Mannudeep Kalra, Chuang Niu, Hengyong Yu, Shadi Ebrahimian, Weiwen Wu, Ge Wang
Keywords(s): deep learning, Deep Tomographic Reconstruction, Few-view, Ultra-low-dose
Category(s): Computational Science and Engineering
Noise2Sim – Similarity-based Self-Learning for Image Denoising
RPI ID: 2021-034-401 Innovation Summary: Noise2Sim is a self-supervised image denoising method that leverages similarity-based learning without requiring clean reference images. The algorithm identifies consistent patterns across noisy inputs and uses them to reconstruct cleaner outputs. It is particularly effective in medical and scientific imaging...
Published: 3/10/2026   |   Inventor(s): Chuang Niu, Ge Wang
Keywords(s): deep learning, Image Denoising, self-learning, self-similarity
Category(s): Computational Science and Engineering
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