Toward a comparison and an optimization of CT protocols using new metrics of dose and image quality part I: prediction of human observers using a model observer for detection and discrimination tasks in low-dose CT images in various scanning conditions

2021 ◽  
Author(s):  
Nadia Othman ◽  
Anne Catherine Simon ◽  
Thierry Montagu ◽  
Laureline Berteloot ◽  
David Grévent ◽  
...  
Keyword(s):  
Image Quality ◽  
Low Dose ◽  
Ct Images ◽  
Low Dose Ct ◽  
Model Observer

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.

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.

scholarly journals Disentangled generative adversarial network for low-dose CT

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wenchao Du ◽  
Hu Chen ◽  
Hongyu Yang ◽  
Yi Zhang
Keyword(s):  
Network Architecture ◽  
Low Dose ◽  
Noise Suppression ◽  
State Of The Art ◽  
Visual Quality ◽  
Ct Images ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network ◽  
Suppression Method

AbstractGenerative adversarial network (GAN) has been applied for low-dose CT images to predict normal-dose CT images. However, the undesired artifacts and details bring uncertainty to the clinical diagnosis. In order to improve the visual quality while suppressing the noise, in this paper, we mainly studied the two key components of deep learning based low-dose CT (LDCT) restoration models—network architecture and adversarial loss, and proposed a disentangled noise suppression method based on GAN (DNSGAN) for LDCT. Specifically, a generator network, which contains the noise suppression and structure recovery modules, is proposed. Furthermore, a multi-scaled relativistic adversarial loss is introduced to preserve the finer structures of generated images. Experiments on simulated and real LDCT datasets show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.

Ultra-Low-Dose CT of the Thorax Using Iterative Reconstruction: Evaluation of Image Quality and Radiation Dose Reduction

2015 ◽  
Vol 204 (6) ◽  
pp. 1197-1202 ◽  
Author(s):  
Yookyung Kim ◽  
Yoon Kyung Kim ◽  
Bo Eun Lee ◽  
Seok Jeong Lee ◽  
Yon Ju Ryu ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Dose Reduction ◽  
Iterative Reconstruction ◽  
Low Dose ◽  
Radiation Dose Reduction ◽  
Low Dose Ct

The Gated Recurrent Conditional Generative Adversarial Network (GRC-GAN): application to denoising of low-dose CT images

2021 ◽  
Author(s):  
Mateus Baltazar de Almeida ◽  
Luis F. Alves Pereira ◽  
Tsang Ing Ren ◽  
George D. C. Cavalcanti ◽  
Jan Sijbers
Keyword(s):  
Low Dose ◽  
Ct Images ◽  
Generative Adversarial Network ◽  
Low Dose Ct ◽  
Adversarial Network

Combination of Deep Learning–Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation

2020 ◽  
Vol 215 (6) ◽  
pp. 1321-1328
Author(s):  
Akinori Hata ◽  
Masahiro Yanagawa ◽  
Yuriko Yoshida ◽  
Tomo Miyata ◽  
Mitsuko Tsubamoto ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Iterative Reconstruction ◽  
Low Dose ◽  
Low Dose Ct

scholarly journals Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography

2021 ◽  
Vol 94 (1117) ◽  
pp. 20200677
Author(s):  
Andrea Steuwe ◽  
Marie Weber ◽  
Oliver Thomas Bethge ◽  
Christin Rademacher ◽  
Matthias Boschheidgen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Dose Reduction ◽  
Low Dose ◽  
Ct Images ◽  
Image Noise ◽  
The Novel ◽  
Subjective Image Quality ◽  
Image Information ◽  
Noise Ratio

Objectives: Modern reconstruction and post-processing software aims at reducing image noise in CT images, potentially allowing for a reduction of the employed radiation exposure. This study aimed at assessing the influence of a novel deep-learning based software on the subjective and objective image quality compared to two traditional methods [filtered back-projection (FBP), iterative reconstruction (IR)]. Methods: In this institutional review board-approved retrospective study, abdominal low-dose CT images of 27 patients (mean age 38 ± 12 years, volumetric CT dose index 2.9 ± 1.8 mGy) were reconstructed with IR, FBP and, furthermore, post-processed using a novel software. For the three reconstructions, qualitative and quantitative image quality was evaluated by means of CT numbers, noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) in six different ROIs. Additionally, the reconstructions were compared using SNR, peak SNR, root mean square error and mean absolute error to assess structural differences. Results: On average, CT numbers varied within 1 Hounsfield unit (HU) for the three assessed methods in the assessed ROIs. In soft tissue, image noise was up to 42% lower compared to FBP and up to 27% lower to IR when applying the novel software. Consequently, SNR and CNR were highest with the novel software. For both IR and the novel software, subjective image quality was equal but higher than the image quality of FBP-images. Conclusion: The assessed software reduces image noise while maintaining image information, even in comparison to IR, allowing for a potential dose reduction of approximately 20% in abdominal CT imaging. Advances in knowledge: The assessed software reduces image noise by up to 27% compared to IR and 48% compared to FBP while maintaining the image information. The reduced image noise allows for a potential dose reduction of approximately 20% in abdominal imaging.

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