Block‐matching sparsity regularization‐based image reconstruction for low‐dose computed tomography

Cai, Ailong; Li, Lei; Zheng, Zhizhong; Wang, Linyuan; Yan, Bin

doi:10.1002/mp.12911

Cited by 8 publications

(3 citation statements)

References 39 publications

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“…This finds widespread application [39]. Cai et al [14] used sparsity regularization to reconstruct images from tomography. Xie et al [42] propose a tensorbased denoising approach to improve image quality in multispectrum.…”

Section: Related Workmentioning

confidence: 99%

Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging

Ahmadi¹,

Kästner²,

Hauffen³

et al. 2021

Preprint

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This paper presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super resolution (SR) imaging. Photothermal imaging is a well-known technique in active thermography for nondestructive inspection of defects in materials such as metals or composites. A grand challenge of active thermography is to overcome the spatial resolution limitation imposed by heat diffusion in order to accurately resolve each defect. The photothermal SR approach enables to extract high-frequency spatial components based on the deconvolution with the thermal point spread function. However, stable deconvolution can only be achieved by using the sparse structure of defect patterns, which often requires tedious, hand-crafted tuning of hyperparameters and results in computationally intensive algorithms. On this account, Photothermal-SR-Net is proposed in this paper, which performs deconvolution by deep unfolding considering the underlying physics. This enables to super resolve 2D thermal images for nondestructive testing with a substantially improved convergence rate. Since defects appear sparsely in materials, Photothermal-SR-Net applies trained blocksparsity thresholding to the acquired thermal images in each convolutional layer. The performance of the proposed approach is evaluated and discussed using various deep unfolding and thresholding approaches applied to 2D thermal images. Subsequently, studies are conducted on how to increase the reconstruction quality and the computational performance of Photothermal-SR-Net is evaluated. Thereby, it was found that the computing time for creating high-resolution images could be significantly reduced without decreasing the reconstruction quality by using pixel binning as a preprocessing step.

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Section: Related Workmentioning

confidence: 99%

Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging

Ahmadi¹,

Kästner²,

Hauffen³

et al. 2021

Preprint

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show abstract

“…At the same time, K. Kim et al [19] proposed low dose CT reconstruction using spatially encoded nonlocal penalty, in which an ordered subset SQS method for log-likelihood is developed and the patch-based similarity constraint with a spatially variant factor is developed to reduce the noise effectively and preserve features simultaneously. Very recently, Cai et al [20] investigated block-marching sparsity regularization and developed a practical reconstruction algorithm using hard thresholding and projection onto convex set methods for low dose CT reconstruction.…”

Section: Introductionmentioning

confidence: 99%

Low Dose CT Image Reconstruction Based on Structure Tensor Total Variation Using Accelerated Fast Iterative Shrinkage Thresholding Algorithm

Mou

Chen

2020

Sensors

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Low dose computed tomography (CT) has drawn much attention in the medical imaging field because of its ability to reduce the radiation dose. Recently, statistical iterative reconstruction (SIR) with total variation (TV) penalty has been developed to low dose CT image reconstruction. Nevertheless, the TV penalty has the drawback of creating blocky effects in the reconstructed images. To overcome the limitations of TV, in this paper we firstly introduce the structure tensor total variation (STV1) penalty into SIR framework for low dose CT image reconstruction. Then, an accelerated fast iterative shrinkage thresholding algorithm (AFISTA) is developed to minimize the objective function. The proposed AFISTA reconstruction algorithm was evaluated using numerical simulated low dose projection based on two CT images and realistic low dose projection data of a sheep lung CT perfusion. The experimental results demonstrated that our proposed STV1-based algorithm outperform FBP and TV-based algorithm in terms of removing noise and restraining blocky effects.

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“…Zhang et al [43] introduced the curvature-driven Euler's elastica regularization to rectify large curvatures and kept the isophotes smooth without erratic distortions. Cai et al [8] proposed the block matching sparsity regularization for CT image reconstruction for an incomplete projection set. Wang et al [37] presented the guided image filtering-based limited-angle CT reconstruction algorithm using a wavelet 2469-7311 c 2022 IEEE.…”

mentioning

confidence: 99%

LRIP-Net: Low-Resolution Image Prior-Based Network for Limited-Angle CT Reconstruction

Gao

Ding

Wang

et al. 2023

IEEE Trans. Radiat. Plasma Med. Sci.

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In the practical applications of computed tomography (CT) imaging, the projection data may be acquired within a limited-angle range and corrupted by noises due to the limitation of scanning conditions. The noisy incomplete projection data results in the ill-posedness of the inverse problems. Based on the observation that the low-resolution reconstruction problem has better numerical stability, we propose a novel low-resolution image prior-based CT reconstruction model for limited-angle reconstruction. More specifically, we build up a low-resolution reconstruction problem on the down-sampled projection data, and use the reconstructed low-resolution image as prior knowledge for the high-resolution limited-angle CT problem. The constrained minimization problem is then solved by the alternating direction method with all subminimization problems approximated by the convolutional neural networks. Numerical experiments demonstrate that our double-resolution network outperforms both the variational method and popular learning-based reconstruction methods on noisy limited-angle reconstruction problems.

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Block‐matching sparsity regularization‐based image reconstruction for low‐dose computed tomography

Abstract: A block-matching-based reconstruction method for low-dose CT is proposed. Improvements in image quality are verified by quantitative metrics and visual comparisons, thereby indicating the potential of the proposed method for real-life applications.

Cited by 8 publications

References 39 publications

Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging

Photothermal-SR-Net: A Customized Deep Unfolding Neural Network for Photothermal Super Resolution Imaging

Low Dose CT Image Reconstruction Based on Structure Tensor Total Variation Using Accelerated Fast Iterative Shrinkage Thresholding Algorithm

LRIP-Net: Low-Resolution Image Prior-Based Network for Limited-Angle CT Reconstruction

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