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AI-Enabled Ultra-Low-Dose CT Reconstruction
Weiwen Wu, Chuang Niu, Shadi Ebrahimian +3
By the ALARA (As Low As Reasonably Achievable) principle, ultra-low-dose CT reconstruction is a holy grail to minimize cancer risks and genetic damages, especially for children. Wi…
Task-Oriented Low-Dose CT Image Denoising
Jiajin Zhang, Hanqing Chao, Xuanang Xu +3
The extensive use of medical CT has raised a public concern over the radiation dose to the patient. Reducing the radiation dose leads to increased CT image noise and artifacts, whi…
Noise Entangled GAN For Low-Dose CT Simulation
Chuang Niu, Ge Wang, Pingkun Yan +8
We propose a Noise Entangled GAN (NE-GAN) for simulating low-dose computed tomography (CT) images from a higher dose CT image. First, we present two schemes to generate a clean CT…