machine-learning Results | Available Intellectual Property | Rensselaer Polytechnic Institute

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Dual Network Architecture for Few-view CT – Trained on ImageNet Data and Transferred for Medical Imaging
RPI ID: 2020-001-401Innovation Summary:This invention introduces a deep learning-based system for reconstructing CT images from a limited number of views. The model is trained to infer missing projection data and generate high-quality images from sparse inputs. It significantly reduces radiation exposure while maintaining diagnostic accuracy. The system...
Published: 7/21/2025   |   Updated: 7/3/2025   |   Inventor(s): Ge Wang, Hongming Shan, Wenxiang Cong, Huidong Xie
Keywords(s): deep learning, Dual network architecture (DNA), few-view CT, generative adversarial network (GAN), machine-learning, sparse-view CT
Category(s): Biotechnology and the Life Sciences
Modularized Adaptive Processing Neural Network (MAP-NN) for Low-dose CT with Radiologists-in-the-loop
RPI ID: 2019-023-201Innovation Summary:This invention introduces a modularized adaptive processing neural network architecture designed for efficient and scalable AI applications. The system dynamically adjusts its structure and processing pathways based on input complexity and task requirements. It supports real-time learning and inference, making...
Published: 7/21/2025   |   Updated: 7/3/2025   |   Inventor(s): Ge Wang, Hongming Shan
Keywords(s): deep learning, denoising, machine-learning, radiologists-in-the-loop, tomography reconstruction
Category(s): Biotechnology and the Life Sciences
A Synergized Pulsing-Imaging Network (SPIN)
RPI ID: 2018-029-401Innovation Summary:This invention introduces a synergized pulsing-imaging network (SPIN) that jointly optimizes MRI pulse sequences and image reconstruction. The system uses a deep learning framework to co-train the pulse generation and image reconstruction modules, improving image quality and acquisition efficiency. It adapts to...
Published: 7/21/2025   |   Updated: 7/3/2025   |   Inventor(s): Ge Wang, Qing Lyu, Tao Xu
Keywords(s): deep learning, machine-learning, medical imaging
Category(s): Biotechnology and the Life Sciences
Training a CNN with pseudo ground truth for CT metal artifact reduction
RPI ID: 2018-021-201 / 2018-021-601Innovation Summary:This invention introduces a method for training convolutional neural networks (CNNs) to reduce artifacts in CT images using pseudo ground truth data. The system includes an estimated ground truth generator that produces reference images from artifact-laden scans, enabling the CNN to learn artifact...
Published: 7/21/2025   |   Updated: 7/3/2025   |   Inventor(s): Ge Wang, Lars Gjesteby, Hongming Shan
Keywords(s): Biomedical Engineering, machine-learning, medical imaging
Category(s): Computational Science and Engineering
A Deep Neural Network based Corrector for Pulse Pileup Effect of Photon Counting Detectors
RPI ID: 2018-020-401 / 2018-020-601Innovation Summary:This invention introduces a neural network-based corrector for photon-counting computed tomography (PCCT) systems. Designed to address spectral distortions and detector non-linearities, the system processes raw photon data to generate corrected images with enhanced spectral fidelity and quantitative...
Published: 7/21/2025   |   Updated: 7/3/2025   |   Inventor(s): Ge Wang, Ruibin Feng, David Rundle
Keywords(s): deep learning, machine-learning, photon-counting Data
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