A fast image reconstruction method for planar objects CT inspired by differentiation property of Fourier transform (DPFT)

Zhao, Shusen; Xia, Dimeng; Zhao, Xing

doi:10.1088/1361-6420/abff79

Cited by 7 publications

(3 citation statements)

References 45 publications

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“…After the introduction of Target.net, the residual unit remains unchanged for a period of time, which reduces the correlation between the unit mapping and the identity mapping to a certain extent, and improves the stability of the algorithm. After introducing the deep residual network mechanism, the parameters in the residual network are defined as θ Q , Q μ ½s, μðsÞ which represents the expected return value obtained by using the μ strategy to select an action in the s state, and because it is in a continuous space [19], it is expected that it can be calculated by integral; then, formula (1) can be used to express the quality of strategy μ.…”

Section: Introduce the Deep Residual Network Mechanismmentioning

confidence: 99%

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

Sun

2022

Journal of Sensors

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In order to solve the problems of image perception and quality decision-making of wood defects with typical bionic intelligent algorithms, the existence of multidimensional degradation factors causes serious real problems of image distortion; the author proposes a wood defect image reconstruction and quality evaluation model based on deep reinforcement learning. The model introduced the deep learning mechanism and realized real-time and efficient reconstruction of multidimensional defect images of different wood by using the deep residual network for iterative training. In this model, a panoramic autonomous perception model was constructed for the fine segmentation and feature extraction of multidimensional defects of different wood and a shared resource pool of wood defect features of the magnitude of big data was constructed. Introduce the reinforcement learning mechanism, use the deep deterministic policy gradient algorithm, and establish a high-dimensional decision mapping between the iterative update of defect features, autonomous decision-making, panoramic visualization, depth prediction, and wood quality evaluation; it realizes the horizontal sharing integration of multidimensional difference wood defect image reconstruction and quality evaluation. The results obtained are as follows: in a typical environment, systematic wood quality evaluation, and autonomous intelligent decision-making performance, the coincidence rate with artificial defect recognition and evaluation efficiency can reach 90% and the loss of the training set can be controlled below 0.2%. Compared with the traditional wood quality grading system, the wood defect image reconstruction, and quality evaluation model system designed by the author, the quality evaluation decision-making efficiency rate was 90.19%, an increase of about 20%, and the system decision-making operation and maintenance loss was 2.23%, a decrease of about 10%. It is proved that the system designed by the author can realize the timely detection of wood quality defects very effectively and save a lot of manpower and material resources.

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Section: Introduce the Deep Residual Network Mechanismmentioning

confidence: 99%

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

Sun

2022

Journal of Sensors

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“…In general, when the number of pixels in a reconstructed image exceeds the number of projection samples in CT imaging, the inverse problem of (1) becomes ill-posed. Due to the radiation, various approaches have been explored, which can be primarily categorized into two main categories, either to modify the scanning protocol by reducing the tube voltage and/or tube current [44] or downsample the measured data for CT reconstruction, such as interior CT [36,51,60,70], and sparse-view CT [34,63,65,66,68,72]. These approaches aim to achieve dose reduction while maintaining satisfactory image quality.…”

Section: Introductionmentioning

confidence: 99%

Low-resolution prior equilibrium network for CT reconstruction

Yang,

Gao,

Duan

2024

Inverse Problems

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The unrolling method has been investigated for learning variational models in X-ray computed tomography. However, for incomplete data reconstruction, such as sparse-view and limited-angle problems, the unrolling method of gradient descent of the energy minimization problem cannot yield satisfactory results. In this paper, we present an effective CT reconstruction model, where the low-resolution image is introduced as a regularization for incomplete data problems. In what follows, we utilize the deep equilibrium approach to unfolding of the gradient descent algorithm, thereby constructing the backbone network architecture for solving the minimization model. We theoretically discuss the convergence of the proposed low-resolution prior equilibrium model and provide the necessary conditions to guarantee its convergence. Experimental results on both sparse-view and limited-angle reconstruction problems are provided, demonstrating that our end-to-end low-resolution prior equilibrium model outperforms other state-of-the-art methods in terms of noise reduction, contrast-to-noise ratio, and preservation of edge details.

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“…So in this case, reconstructing images by traditional algorithms may cause beam-hardening and scatter artifacts [16,17]. How to reconstruct ideal images without artifacts from noisy data is a typical inverse problem in CT imaging [18].…”

Section: Introductionmentioning

confidence: 99%

An iterative method for simultaneous reduction on beam-hardening and scatter artifacts in x-ray CT

Xia¹,

Zhao²,

Zhang³

et al. 2023

Phys. Scr.

Self Cite

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Beam-hardening and scatter are two significant factors leading to contrast reduction and gray value inaccuracy in CT images. The cupping artifacts and obscure boundaries in reconstructed images are also caused mainly by both beam-hardening and scattering. It is difficult to get high-quality CT images with only one of them to make correction. In this paper, we proposed an x-ray CT polychromatic attenuation model with scatter effect, and an iterative method for simultaneous reduction on beam-hardening and scatter artifacts. In this model, the measured data of the detector comprise two parts: an attenuation term and a scatter term. The former is defined by an exponential rational fraction to fit the traditional attenuation process, and the latter is defined by a convolutional scatter intensity. The coefficients of the rational fraction in the attenuation term and the scatter term kernel are all calculated from a calibration phantom which is scanned to get corresponding equations. Based on the polychromatic attenuation model, we proposed an iterative artifacts reduction method combining deconvolution technique with linearized back-projection (iDLB method). This method makes the nonlinear polychromatic attenuation model become easily solvable by linearizing transformation, which simplifies the residuals allocation process. Experiments of both numerical simulation and practical data show the iDLB method has the ability to reduce beam-hardening and scatter artifacts simultaneously, improve the contrast of CT images, and it is highly parallelized for lower computational cost.

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A fast image reconstruction method for planar objects CT inspired by differentiation property of Fourier transform (DPFT)

Cited by 7 publications

References 45 publications

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

Low-resolution prior equilibrium network for CT reconstruction

An iterative method for simultaneous reduction on beam-hardening and scatter artifacts in x-ray CT

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