A CT reconstruction approach from sparse projection with adaptive-weighted diagonal total-variation in biomedical application

Deng, Luzhen; Mi, Deling; He, Peng; Feng, Peng; Yu, Pengfei; Chen, Mianyi; Li, Zhichao; Wang, Jian; Wei, Biao

doi:10.3233/bme-151468

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…Instead of relying only on MSE-based loss, ISRGAN is trained by the combination of of VGG based perceptual loss and adversarial loss. To further improve the quality of super-resolution images, inspired by adaptive total variation [24], [25] and diagonal total variation [26], [27] model to take full advantage of directional information of edges and textures, a novel strategy is introduced to improve the performance of super-resolution based on GAN coupled with the total-variation based model. Furthermore, a new adaptive model based on diagonal total variation is proposed to keep texture details.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Diagonal Total-Variation Generative Adversarial Network for Super-Resolution Imaging

et al. 2020

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To address problems that the loss function does not correlate well with perceptual vision in super-resolution methods based on the convolutional neural network(CNN), a novel model called the ADTV-SRGAN is designed based on the adaptive diagonal total-variation generative adversarial network. Combined with global perception and the local structure adaptive method, spatial loss based on the diagonal variation model is proposed to make the loss function can be adjusted according to the spatial features. Pixel loss and characteristic loss are in combination with the spatial loss for the fusing optimization of the total loss function such that high-frequency details of the images are maintained to improve their quality. The results of experiment show that the proposed method can obtain competitive results in objective evaluations. In subjective assessment, images reconstructed by it are clear, delicate, and natural, and it preserved edgeand texture-related details.

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Section: Related Workmentioning

confidence: 99%

Adaptive Diagonal Total-Variation Generative Adversarial Network for Super-Resolution Imaging

et al. 2020

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“…TV considers only the vertical and horizontal gradient operators, and the diagonal gradients are missed and some directional information of edges and image texture are lost as a consequence. To overcome this limitation, a diagonal total variation (DTV) model was proposed, which considers only the diagonal gradients instead of the vertical and horizontal gradients [43].…”

Section: Jinst 14 P08023mentioning

confidence: 99%

“…Some directional information of edges and image texture are likely to be lost in the conventional TV model due to considering only vertical and horizontal gradient operators. To overcome this problem, a DTV model was introduced [43] for sparse-views CT image reconstruction which includes only the diagonal gradient operators. The DTV model has been defined as follows:…”

Section: Adaptive-weighted Diagonal Tv Modelmentioning

confidence: 99%

Sparse-view statistical image reconstruction with improved total variation regularization for X-ray micro-CT imaging

Mahmoudi

Fouladi

et al. 2019

J. Inst.

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A: Sparse-view x-ray micro computed tomography (micro-CT) reconstruction algorithms via total variation (TV) optimize the data without introducing notable noise and artifacts, resulting in significant scanning time reduction while maintaining image quality. However, due to the piecewise constant assumption for the image, a conventional TV minimization often suffers from patchy artifacts in reconstructed images. Moreover, for lack of directional gradient in TV some directional information are lost. To obviate these drawbacks, in this study we develop a penalized weighted least-square (PWLS) strategy for micro-CT sparse-view image reconstruction by incorporating an adaptive weighted total variation in combination with an adaptive weighted diagonal total variation (AwTV+AwDTV) penalty term. The AwTV considers the vertical and horizontal gradients while the AwDTV uses the diagonal gradients. The associated weights which are defined based on the anisotropic edge properties of an image, are expressed as an exponential function and can be adaptively adjusted by the amount of the difference between voxel intensities to preserve the edge details. To evaluate the presented (AwTV+AwDTV)-PWLS algorithm, both qualitative and quantitative studies were performed by computer simulations and micro-CT data experiments. The Shepp-Logan phantom for computer simulation and the micro-CT water phantom and a rat skull for micro-CT experiments are employed to perform image reconstruction. To evaluate the performance of AwTV+AwDTV algorithm, we compared it with TV and AwTV reconstruction algorithms. The simulation results show that the presented (AwTV+AwDTV)-PWLS algorithm can achieve the lowest RMSE and highest PSNR, SSIM and MTF for different number of projections as compared to the AwTV and conventional TV algorithms. The micro-CT data results confirmed the superiority of the proposed (AwTV+AwDTV) method to the AwTV and TV methods for different number of projections. K: Computerized Tomography (CT) and Computed Radiography (CR); Data reduction methods; Image reconstruction in medical imaging

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“…However, these algorithms solely take into account the vertical and horizontal directions for the gradient operator of the TV term and ignore the diagonal direction. To address this limitation, Deng et al [23] proposed a diagonal TV computation method in 2015 that only considers the diagonal gradient in the model, claiming to reconstruct a better image than the traditional TV method that only considers the vertical and horizontal directions. Additionally, researchers have proposed TV models based on different directions and multi-directions to better recover edges and details lost in minimized TV, including TV models based on 8-and 26-directional gradient operators [24], multi-direction anisotropic total variation (MDATV) [25], and a BDTV model that adaptively computes the orientation information of gradient operators [19].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of Sparse-View X-ray Computed Tomography Based on Adaptive Total Variation Minimization

Yu,

Wen,

Yang

2023

Micromachines

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Sparse-view reconstruction has garnered significant interest in X-ray computed tomography (CT) imaging owing to its ability to lower radiation doses and enhance detection efficiency. Among current methods for sparse-view CT reconstruction, an algorithm utilizing iterative reconstruction based on full variational regularization demonstrates good performance. The optimized direction and number of computations for the gradient operator of the regularization term play a crucial role in determining not only the reconstructed image quality but also the convergence speed of the iteration process. The conventional TV approach solely accounts for the vertical and horizontal directions of the two-dimensional plane in the gradient direction. When projection data decrease, the edges of the reconstructed image become blurred. Exploring too many gradient directions for TV terms often comes at the expense of more computational costs. To enhance the balance of computational cost and reconstruction quality, this study suggests a novel TV computation model that is founded on a four-direction gradient operator. In addition, selecting appropriate iteration parameters significantly impacts the quality of the reconstructed image. We propose a nonparametric control method utilizing the improved TV approach as a solution to the tedious manual parameter optimization issue. The relaxation parameters of projection onto convex sets (POCS) are determined according to the scanning number and numerical proportion of the projection data; according to the image error before and after iterations, the gradient descent step of the TV item is adaptively adjusted. Compared with several representative iterative reconstruction algorithms, the experimental results show that the algorithm can effectively preserve edges and suppress noise in sparse-view CT reconstruction.

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A CT reconstruction approach from sparse projection with adaptive-weighted diagonal total-variation in biomedical application

Cited by 8 publications

References 17 publications

Adaptive Diagonal Total-Variation Generative Adversarial Network for Super-Resolution Imaging

Adaptive Diagonal Total-Variation Generative Adversarial Network for Super-Resolution Imaging

Sparse-view statistical image reconstruction with improved total variation regularization for X-ray micro-CT imaging

Reconstruction of Sparse-View X-ray Computed Tomography Based on Adaptive Total Variation Minimization

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