Clinical evaluation of a phantom-based deep convolutional neural network for whole-body-low-dose and ultra-low-dose CT skeletal surveys

2021 ◽  
Author(s):  
Nathan Huber ◽  
Tara Anderson ◽  
Andrew Missert ◽  
Mark Adkins ◽  
Shuai Leng ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Clinical Evaluation ◽  
Low Dose ◽  
Deep Convolutional Neural Network ◽  
Whole Body ◽  
Low Dose Ct

SACNN: Self-Attention Convolutional Neural Network for Low-Dose CT Denoising With Self-Supervised Perceptual Loss Network

2020 ◽  
Vol 39 (7) ◽  
pp. 2289-2301 ◽  
Author(s):  
Meng Li ◽  
William Hsu ◽  
Xiaodong Xie ◽  
Jason Cong ◽  
Wen Gao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Dose ◽  
Low Dose Ct

scholarly journals Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network

2017 ◽  
Vol 36 (12) ◽  
pp. 2524-2535 ◽  
Author(s):  
Hu Chen ◽  
Yi Zhang ◽  
Mannudeep K. Kalra ◽  
Feng Lin ◽  
Yang Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Low Dose ◽  
Low Dose Ct

scholarly journals Design and implementation of convolutional neural network architectures for low-dose CT image noise reduction

2021 ◽  
Author(s):  
Seyyedomid Badretale
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Low Dose ◽  
Ct Images ◽  
Learning Approaches ◽  
Low Dose Ct ◽  
Image Noise Reduction ◽  
Low Dose Computed Tomography

An essential objective in low-dose Computed Tomography (CT) imaging is how best to preserve the image quality. While the image quality lowers with reducing the X-ray dosage, improving the quality is crucial. Therefore, a novel method to denoise low-dose CT images has been presented in this thesis. Different from the traditional algorithms which utilize similar shared features of CT images in the spatial domain, the deep learning approaches are suggested for low-dose CT denoising. The proposed algorithm learns an end-to-end mapping from the low-dose CT images for denoising the low-dose CT images. The first method is based on a fully convolutional neural network. The second approach is a deep convolutional neural network architecture consisting of five major sections. The results of two frameworks are compared with the state-of-the-art methods. Several metrics for assessing image quality are applied in this thesis in order to highlight the supremacy of the performed method.

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