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Parallel Diffusion Model-based Sparse-view Cone-beam Breast CT

Autor: Xia, Wenjun, Tseng, Hsin Wu, Niu, Chuang, Cong, Wenxiang, Zhang, Xiaohua, Liu, Shaohua, Ning, Ruola, Vedantham, Srinivasan, Wang, Ge

Breast cancer is the most prevalent cancer among women worldwide, and early detection is crucial for reducing its mortality rate and improving quality of life. Dedicated breast computed tomography (CT) scanners offer better image quality than mammogr

Externí odkaz: http://arxiv.org/abs/2303.12861

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Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction

Autor: Xie, Huidong, Shan, Hongming, Cong, Wenxiang, Liu, Chi, Zhang, Xiaohua, Liu, Shaohua, Ning, Ruola, Wang, Ge

Breast CT provides image volumes with isotropic resolution in high contrast, enabling detection of small calcification (down to a few hundred microns in size) and subtle density differences. Since breast is sensitive to x-ray radiation, dose reductio

Externí odkaz: http://arxiv.org/abs/1912.04278

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Deep-learning-based Breast CT for Radiation Dose Reduction

Autor: Cong, Wenxiang, Shan, Hongming, Zhang, Xiaohua, Liu, Shaohua, Ning, Ruola, Wang, Ge

Cone-beam breast computed tomography (CT) provides true 3D breast images with isotropic resolution and high-contrast information, detecting calcifications as small as a few hundred microns and revealing subtle tissue differences. However, breast is h

Externí odkaz: http://arxiv.org/abs/1909.11721

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Dual Network Architecture for Few-view CT -- Trained on ImageNet Data and Transferred for Medical Imaging

Autor: Xie, Huidong, Shan, Hongming, Cong, Wenxiang, Zhang, Xiaohua, Liu, Shaohua, Ning, Ruola, Wang, Ge

X-ray computed tomography (CT) reconstructs cross-sectional images from projection data. However, ionizing X-ray radiation associated with CT scanning might induce cancer and genetic damage. Therefore, the reduction of radiation dose has attracted ma

Externí odkaz: http://arxiv.org/abs/1907.01262

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