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Deep Efficient End-to-end Reconstruction (DEER) Network for Few-view Breast CT Image Reconstruction
Huidong Xie, Hongming Shan, Wenxiang Cong +5
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 di…
Deep Encoder-decoder Adversarial Reconstruction (DEAR) Network for 3D CT from Few-view Data
Huidong Xie, Hongming Shan, Ge Wang
X-ray computed tomography (CT) is widely used in clinical practice. The involved ionizing X-ray radiation, however, could increase cancer risk. Hence, the reduction of the radiatio…
Deep learning for accelerating Monte Carlo radiation transport simulation in intensity-modulated radiation therapy
Zhao Peng, Hongming Shan, Tianyu Liu +4
Cancer is a primary cause of morbidity and mortality worldwide. The radiotherapy plays a more and more important role in cancer treatment. In the radiotherapy, the dose distributio…
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising
Yu Gong, Hongming Shan, Yueyang Teng +5
Due to the widespread use of positron emission tomography (PET) in clinical practice, the potential risk of PET-associated radiation dose to patients needs to be minimized. However…
A Method of Rapid Quantification of Patient-Specific Organ Dose for CT Using Coupled Deep-Learning based Multi-Organ Segmentation and GPU-accelerated Monte Carlo Dose Computing
Zhao Peng, Xi Fang, Pingkun Yan +7
Purpose: This paper describes a new method to apply deep-learning algorithms for automatic segmentation of radiosensitive organs from 3D tomographic CT images before computing orga…
Deep-learning-based Breast CT for Radiation Dose Reduction
Wenxiang Cong, Hongming Shan, Xiaohua Zhang +3
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…