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.

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.

An effective sinogram inpainting for complementary limited-angle dual-energy computed tomography imaging using generative adversarial networks

2020 ◽  
pp. 1-25
Author(s):  
Yizhong Wang ◽  
Wenkun Zhang ◽  
Ailong Cai ◽  
Linyuan Wang ◽  
Chao Tang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Imaging System ◽  
High Energy ◽  
Dual Energy ◽  
Generative Adversarial Networks ◽  
Reconstruction Technique ◽  
Reconstruction Algorithms ◽  
Dual Energy Computed Tomography ◽  
Adversarial Networks ◽  
Limited Angle

Dual-energy computed tomography (DECT) provides more anatomical and functional information for image diagnosis. Presently, the popular DECT imaging systems need to scan at least full angle (i.e., 360°). In this study, we propose a DECT using complementary limited-angle scan (DECT-CL) technology to reduce the radiation dose and compress the spatial distribution of the imaging system. The dual-energy total scan is 180°, where the low- and high-energy scan range is the first 90° and last 90°, respectively. We describe this dual limited-angle problem as a complementary limited-angle problem, which is challenging to obtain high-quality images using traditional reconstruction algorithms. Furthermore, a complementary-sinogram-inpainting generative adversarial networks (CSI-GAN) with a sinogram loss is proposed to inpainting sinogram to suppress the singularity of truncated sinogram. The sinogram loss focuses on the data distribution of the generated sinogram while approaching the target sinogram. We use the simultaneous algebraic reconstruction technique namely, a total variable (SART-TV) algorithms for image reconstruction. Then, taking reconstructed CT images of pleural and cranial cavity slices as examples, we evaluate the performance of our method and numerically compare different methods based on root mean square error (RMSE), peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Compared with traditional algorithms, the proposed network shows advantages in numerical terms. Compared with Patch-GAN, the proposed network can also reduce the RMSE of the reconstruction results by an average of 40% and increase the PSNR by an average of 26%. In conclusion, both qualitative and quantitative comparison and analysis demonstrate that our proposed method achieves a good artifact suppression effect and can suitably solve the complementary limited-angle problem.

scholarly journals Classification of Space Objects by Using Deep Learning with Micro-Doppler Signature Images

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4365
Author(s):  
Kwangyong Jung ◽  
Jae-In Lee ◽  
Nammoon Kim ◽  
Sunjin Oh ◽  
Dong-Wook Seo
Keyword(s):  
Transfer Learning ◽  
Data Augmentation ◽  
Doppler Frequency ◽  
Extraction Methods ◽  
Classification Performance ◽  
Reconstruction Method ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Space Objects ◽  
Target Characteristics

Radar target classification is an important task in the missile defense system. State-of-the-art studies using micro-doppler frequency have been conducted to classify the space object targets. However, existing studies rely highly on feature extraction methods. Therefore, the generalization performance of the classifier is limited and there is room for improvement. Recently, to improve the classification performance, the popular approaches are to build a convolutional neural network (CNN) architecture with the help of transfer learning and use the generative adversarial network (GAN) to increase the training datasets. However, these methods still have drawbacks. First, they use only one feature to train the network. Therefore, the existing methods cannot guarantee that the classifier learns more robust target characteristics. Second, it is difficult to obtain large amounts of data that accurately mimic real-world target features by performing data augmentation via GAN instead of simulation. To mitigate the above problem, we propose a transfer learning-based parallel network with the spectrogram and the cadence velocity diagram (CVD) as the inputs. In addition, we obtain an EM simulation-based dataset. The radar-received signal is simulated according to a variety of dynamics using the concept of shooting and bouncing rays with relative aspect angles rather than the scattering center reconstruction method. Our proposed model is evaluated on our generated dataset. The proposed method achieved about 0.01 to 0.39% higher accuracy than the pre-trained networks with a single input feature.

A content-adaptive unstructured grid based regularized CT reconstruction method with a SART-type preconditioned fixed-point proximity algorithm

2022 ◽  
Author(s):  
Yun Chen ◽  
Yao Lu ◽  
Xiangyuan Ma ◽  
Yuesheng Xu
Keyword(s):  
Integral Equation ◽  
Fixed Point ◽  
Optimization Problem ◽  
Unstructured Grid ◽  
Reconstruction Technique ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Image Domain ◽  
Computational Costs ◽  
Content Adaptive

Abstract The goal of this study is to develop a new computed tomography (CT) image reconstruction method, aiming at improving the quality of the reconstructed images of existing methods while reducing computational costs. Existing CT reconstruction is modeled by pixel-based piecewise constant approximations of the integral equation that describes the CT projection data acquisition process. Using these approximations imposes a bottleneck model error and results in a discrete system of a large size. We propose to develop a content-adaptive unstructured grid (CAUG) based regularized CT reconstruction method to address these issues. Specifically, we design a CAUG of the image domain to sparsely represent the underlying image, and introduce a CAUG-based piecewise linear approximation of the integral equation by employing a collocation method. We further apply a regularization defined on the CAUG for the resulting illposed linear system, which may lead to a sparse linear representation for the underlying solution. The regularized CT reconstruction is formulated as a convex optimization problem, whose objective function consists of a weighted least square norm based fidelity term, a regularization term and a constraint term. Here, the corresponding weighted matrix is derived from the simultaneous algebraic reconstruction technique (SART). We then develop a SART-type preconditioned fixed-point proximity algorithm to solve the optimization problem. Convergence analysis is provided for the resulting iterative algorithm. Numerical experiments demonstrate the outperformance of the proposed method over several existing methods in terms of both suppressing noise and reducing computational costs. These methods include the SART without regularization and with quadratic regularization on the CAUG, the traditional total variation (TV) regularized reconstruction method and the TV superiorized conjugate gradient method on the pixel grid.

Deep Learning Computed Tomography: Learning Projection-Domain Weights From Image Domain in Limited Angle Problems

2018 ◽  
Vol 37 (6) ◽  
pp. 1454-1463 ◽  
Author(s):  
Tobias Wurfl ◽  
Mathis Hoffmann ◽  
Vincent Christlein ◽  
Katharina Breininger ◽  
Yixin Huang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Image Domain ◽  
Projection Domain ◽  
Limited Angle

Computed tomography reconstruction on distributed storage using hybrid regularization approach

2019 ◽  
Vol 33 (06) ◽  
pp. 1950063 ◽  
Author(s):  
Shailendra Tiwari ◽  
Kavkirat Kaur ◽  
Yadunath Pathak ◽  
Shivendraa Shivani ◽  
Kuldeep Kaur
Keyword(s):  
Computed Tomography ◽  
Gaussian Noise ◽  
Distributed Storage ◽  
Hybrid Approach ◽  
Computational Cost ◽  
Reconstruction Algorithm ◽  
Noise Robustness ◽  
Projection Data ◽  
Reconstruction Method ◽  
Ct Reconstruction

Computed Tomography (CT) is considered as a significant imaging tool for clinical diagnoses. Due to low-dose radiation in CT, the projection data is highly affected by Gaussian noise which may lead to blurred images, staircase effect, loss of basic fine structure and detailed information. Therefore, there is a demand for an approach that can eliminate noise and can provide high-quality images. To achieve this objective, this paper presents a new statistical image reconstruction method by proposing a suitable regularization approach. The proposed regularization is a hybrid approach of Complex Diffusion and Shock filter as a prior term. To handle the problem of prominent Gaussian noise as well as ill-posedness, the proposed hybrid regularization is further combined with the standard Maximum Likelihood Expectation Maximization (MLEM) reconstruction algorithm in an iterative manner and has been referred to as the proposed CT-Reconstruction (CT-R) algorithm here after. Besides, considering the large sizes of image data sets for medical imaging, distributed storage for images have been employed on Hadoop Distributed File System (HDFS) and the proposed MLEM algorithms have been deployed for improved performance.The proposed method has been evaluated on both the simulated and real test phantoms. The final results are compared with the other standard methods and it is observed that the proposed method has many desirable properties such as better noise robustness, less computational cost and enhanced denoising effect.

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