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Visual Attention Network for Low Dose CT
Wenchao Du, Hu Chen, Peixi Liao +3
Noise and artifacts are intrinsic to low dose CT (LDCT) data acquisition, and will significantly affect the imaging performance. Perfect noise removal and image restoration is intr…
LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT
Hu Chen, Yi Zhang, Yunjin Chen +7
Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-vie…
Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)
Hu Chen, Yi Zhang, Mannudeep K. Kalra +5
Given the potential X-ray radiation risk to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. The current main stream low-dose CT methods…
Low-dose CT denoising with convolutional neural network
Hu Chen, Yi Zhang, Weihua Zhang +4
To reduce the potential radiation risk, low-dose CT has attracted much attention. However, simply lowering the radiation dose will lead to significant deterioration of the image qu…
Low-Dose CT via Deep Neural Network
Hu Chen, Yi Zhang, Weihua Zhang +4
In order to reduce the potential radiation risk, low-dose CT has attracted more and more attention. However, simply lowering the radiation dose will significantly degrade the imagi…