scholarly journals Development of Limited-Angle Iterative Reconstruction Algorithms with Context Encoder-Based Sinogram Completion for Micro-CT Applications

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4458 ◽  
Author(s):  
Shih-Chun Jin ◽  
Chia-Jui Hsieh ◽  
Jyh-Cheng Chen ◽  
Shih-Huan Tu ◽  
Ya-Chen Chen ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Iterative Reconstruction ◽  
Micro Ct ◽  
Reconstruction Algorithms ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Novel Approach ◽  
Ct System ◽  
Limited Angle

Limited-angle iterative reconstruction (LAIR) reduces the radiation dose required for computed tomography (CT) imaging by decreasing the range of the projection angle. We developed an image-quality-based stopping-criteria method with a flexible and innovative instrument design that, when combined with LAIR, provides the image quality of a conventional CT system. This study describes the construction of different scan acquisition protocols for micro-CT system applications. Fully-sampled Feldkamp (FDK)-reconstructed images were used as references for comparison to assess the image quality produced by these tested protocols. The insufficient portions of a sinogram were inpainted by applying a context encoder (CE), a type of generative adversarial network, to the LAIR process. The context image was passed through an encoder to identify features that were connected to the decoder using a channel-wise fully-connected layer. Our results evidence the excellent performance of this novel approach. Even when we reduce the radiation dose by 1/4, the iterative-based LAIR improved the full-width half-maximum, contrast-to-noise and signal-to-noise ratios by 20% to 40% compared to a fully-sampled FDK-based reconstruction. Our data support that this CE-based sinogram completion method enhances the efficacy and efficiency of LAIR and that would allow feasibility of limited angle reconstruction.

Reducing CT radiation dose with iterative reconstruction algorithms: The influence of scan and reconstruction parameters on image quality and CTDIvol

2014 ◽  
Vol 83 (9) ◽  
pp. 1645-1654 ◽  
Author(s):  
Thorsten Klink ◽  
Verena Obmann ◽  
Johannes Heverhagen ◽  
Alexander Stork ◽  
Gerhard Adam ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Iterative Reconstruction ◽  
Reconstruction Algorithms ◽  
Ct Radiation

scholarly journals Promising Generative Adversarial Network Based Sinogram Inpainting Method for Ultra-Limited-Angle Computed Tomography Imaging

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3941 ◽  
Author(s):  
Li ◽  
Cai ◽  
Wang ◽  
Zhang ◽  
Tang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Imaging System ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Reconstruction Algorithms ◽  
Generative Adversarial Network ◽  
Image Domain ◽  
Adversarial Network ◽  
Domain Loss ◽  
Limited Angle

Limited-angle computed tomography (CT) image reconstruction is a challenging problem in the field of CT imaging. In some special applications, limited by the geometric space and mechanical structure of the imaging system, projections can only be collected with a scanning range of less than 90°. We call this kind of serious limited-angle problem the ultra-limited-angle problem, which is difficult to effectively alleviate by traditional iterative reconstruction algorithms. With the development of deep learning, the generative adversarial network (GAN) performs well in image inpainting tasks and can add effective image information to restore missing parts of an image. In this study, given the characteristic of GAN to generate missing information, the sinogram-inpainting-GAN (SI-GAN) is proposed to restore missing sinogram data to suppress the singularity of the truncated sinogram for ultra-limited-angle reconstruction. We propose the U-Net generator and patch-design discriminator in SI-GAN to make the network suitable for standard medical CT images. Furthermore, we propose a joint projection domain and image domain loss function, in which the weighted image domain loss can be added by the back-projection operation. Then, by inputting a paired limited-angle/180° sinogram into the network for training, we can obtain the trained model, which has extracted the continuity feature of sinogram data. Finally, the classic CT reconstruction method is used to reconstruct the images after obtaining the estimated sinograms. The simulation studies and actual data experiments indicate that the proposed method performed well to reduce the serious artifacts caused by ultra-limited-angle scanning.

Image quality in coronary computed tomography angiography: influence of adaptive statistical iterative reconstruction at various radiation dose levels

2018 ◽  
Vol 59 (10) ◽  
pp. 1194-1202 ◽  
Author(s):  
Helle Precht ◽  
Oke Gerke ◽  
Jesper Thygesen ◽  
Kenneth Egstrup ◽  
Søren Auscher ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Radiation Dose ◽  
Iterative Reconstruction ◽  
Quality Criteria ◽  
Left Ventricular ◽  
Reconstruction Algorithms ◽  
Adaptive Statistical Iterative Reconstruction ◽  
Low Contrast ◽  
Statistical Iterative Reconstruction

Background Computed tomography (CT) technology is rapidly evolving and software solution developed to optimize image quality and/or lower radiation dose. Purpose To investigate the influence of adaptive statistical iterative reconstruction (ASIR) at different radiation doses in coronary CT angiography (CCTA) in detailed image quality. Material and Methods A total of 160 CCTA were reconstructed as follows: 55 scans with filtered back projection (FBP) (650 mA), 51 scans (455 mA) with 30% ASIR (ASIR30), and 54 scans (295 mA) with 60% ASIR (ASIR60). For each reconstruction, subjective image quality was assessed by five independent certified cardiologists using a visual grading analysis (VGA) with five predefined image quality criteria consisting of a 5-point scale. Objective measures were contrast, noise, and contrast-to-noise ratio (CNR). Results The CTDIvol resulted in 10.3 mGy, 7.4 mGy, and 4.6 mGy for FBP, ASIR30, and ASIR60, respectively. Homogeneity of the left ventricular lumen was the sole aspect in which reconstruction algorithms differed with a decreasing effect for ASIR60 compared to FBP (estimated odds ratio [OR] = 0.49 [95% confidence interval (CI) = 0.32–0.76; P = 0.001]). Decreased sharpness and spatial- and low-contrast resolutions were observed when using ASIR instead of FBP, but differences were not statistically significant. Concerning objective measurements, noise increased significantly for ASIR30 (OR = 1.08; 95% CI = 1.02–1.14; P = 0.006) and ASIR60 (OR = 1.06; 95% CI = 1.01–1.12; P = 0.034) compared to FBP. Conclusion ASIR significantly decreased the subjectively assessed homogeneity of the left ventricular lumen and increased the objectively measured noise compared to FBP. Considering these results, ASIR at a reduced radiation dose should be implemented with caution.

scholarly journals Six iterative reconstruction algorithms in brain CT: a phantom study on image quality at different radiation dose levels

2013 ◽  
Vol 86 (1031) ◽  
pp. 20130388 ◽  
Author(s):  
A Löve ◽  
M-L Olsson ◽  
R Siemund ◽  
F Stålhammar ◽  
I M Björkman-Burtscher ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Iterative Reconstruction ◽  
Phantom Study ◽  
Reconstruction Algorithms ◽  
Brain Ct ◽  
Dose Levels

scholarly journals An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index

2021 ◽  
Vol 9 (7) ◽  
pp. 691
Author(s):  
Kai Hu ◽  
Yanwen Zhang ◽  
Chenghang Weng ◽  
Pengsheng Wang ◽  
Zhiliang Deng ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Quality Evaluation ◽  
High Efficiency ◽  
Natural Image ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Image Quality Evaluation ◽  
Adversarial Network ◽  
Underwater Image

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.

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

scholarly journals Contrast agent dose reduction in computed tomography with deep learning using a conditional generative adversarial network

2021 ◽  
Author(s):  
Johannes Haubold ◽  
René Hosch ◽  
Lale Umutlu ◽  
Axel Wetter ◽  
Patrizia Haubold ◽  
...  
Keyword(s):  
Image Quality ◽  
Contrast Media ◽  
Animal Experiments ◽  
Turing Test ◽  
Dual Energy ◽  
Generative Adversarial Networks ◽  
Arterial Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

Abstract Objectives To reduce the dose of intravenous iodine-based contrast media (ICM) in CT through virtual contrast-enhanced images using generative adversarial networks. Methods Dual-energy CTs in the arterial phase of 85 patients were randomly split into an 80/20 train/test collective. Four different generative adversarial networks (GANs) based on image pairs, which comprised one image with virtually reduced ICM and the original full ICM CT slice, were trained, testing two input formats (2D and 2.5D) and two reduced ICM dose levels (−50% and −80%). The amount of intravenous ICM was reduced by creating virtual non-contrast series using dual-energy and adding the corresponding percentage of the iodine map. The evaluation was based on different scores (L1 loss, SSIM, PSNR, FID), which evaluate the image quality and similarity. Additionally, a visual Turing test (VTT) with three radiologists was used to assess the similarity and pathological consistency. Results The −80% models reach an SSIM of > 98%, PSNR of > 48, L1 of between 7.5 and 8, and an FID of between 1.6 and 1.7. In comparison, the −50% models reach a SSIM of > 99%, PSNR of > 51, L1 of between 6.0 and 6.1, and an FID between 0.8 and 0.95. For the crucial question of pathological consistency, only the 50% ICM reduction networks achieved 100% consistency, which is required for clinical use. Conclusions The required amount of ICM for CT can be reduced by 50% while maintaining image quality and diagnostic accuracy using GANs. Further phantom studies and animal experiments are required to confirm these initial results. Key Points • The amount of contrast media required for CT can be reduced by 50% using generative adversarial networks. • Not only the image quality but especially the pathological consistency must be evaluated to assess safety. • A too pronounced contrast media reduction could influence the pathological consistency in our collective at 80%.

Sign in / Sign up

Export Citation Format

Share Document