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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
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Updated: 7/3/2025
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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
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