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

Mahmoudi, Golshan; Fouladi, Mohammad Reza; Ay, Mohammad Reza; Rahmim, Arman; Ghadiri, Hossein

doi:10.1088/1748-0221/14/08/p08023

Cited by 14 publications

(6 citation statements)

References 57 publications

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“…Several image reconstruction methods were applicable in UCT systems [28,29]; the FBP technique used the Ram-Lack filter for image reconstruction [24]. The TOF images would be calculated by reconstructing the TOF sinogram.…”

Section: Image Reconstructionmentioning

confidence: 99%

Effects of Beamforming Techniques on Quality of Ultrasound Computed Tomography Images

Solgi

2022

J Biomed Phys Eng

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Background: In body tissues, tumors generally have different speeds of sound (SOS) than normal tissues. In this respect, ultrasound computed tomography (UCT) can generate a cross-sectional SOS map as an innovative ultrasound imaging method. This technique can produce images with a resolution of millimeters and a high signal-to-noise ratio.Objective: This study aimed to improve UCT image quality without increasing breast cancer screening and diagnosis time. Material and Methods:In this analytical study, a ring-shaped UCT breast imaging system was simulated using the K-wave toolbox of MATLAB. The system has a 20 cm diameter and 256 ultrasonic piezoelectrics placed in the ring's circumference. Different beamforming techniques imaged two designed phantoms (i.e., resolution and contrast), and the resolution and contrast to noise ratio (CNR) were calculated. Results:The results of resolution phantom imaging without any beamforming showed that only bars with the value of 0.125 and 0.167 lp/mm were distinguishable, and the 0.1 bars were not recognizable in the imaging. In addition, increasing the number of transmitters led to no noticeable change in resolution for 0.125 and 0.167 lp/mm bars. In all beamforming techniques for imaging the contrast phantom, the CNR parameter up to an object with a diameter of 8 mm increases with increasing diameter without any change. Conclusion:The beamforming technique using three simultaneous transmitters improved the resolution by about 1 mm compared to the normal strategy. In addition to highcontrast images, beamforming with 9 simultaneous transmitters led to a preferable technique.

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Section: Image Reconstructionmentioning

confidence: 99%

Effects of Beamforming Techniques on Quality of Ultrasound Computed Tomography Images

Solgi

2022

J Biomed Phys Eng

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“…Compared to the traditional ART [9] and EM [10], the reconstruction performance of TV method is significantly improved as a result of its edge preserving ability [11,12]. Although the TV regularization method has achieved good reconstruction performance, certain limitations still exist [13,14]. In the past decade, various variants of TV regularization terms have been proposed, such as multi-directional TV [15], gradient directional TV [16], weighted TV [17,18], relative TV [19,20], and others [21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

A non-local total generalized variation regularization reconstruction method for sparse-view x-ray CT

Min,

Tao,

Liu

et al. 2024

Meas. Sci. Technol.

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Sparse-view X-ray computed tomography (CT) reconstruction, employing total generalised variation (TGV), effectively mitigates the stepwise artefacts associated with total variation (TV) regularisation while preserving structural features within transitional regions of the reconstructed image. Despite TGV surpassing TV in reconstruction quality, it neglects the non-local self-similarity prior, recognised for its efficacy in restoring details during CT reconstruction. This study introduces a non-local total generalised variation (NLTGV) to address the limitation of TGV regularisation method. Specifically, we propose an NLTGV-regularised method for sparse-view CT reconstruction, utilising non-local high-order derivative information to maintain image features and non-local self-similarity for detail recovery. Owing to the non-differentiability of the NLTGV regulariser, we employ an alternating direction method of multipliers (ADMM) optimisation method, facilitating an efficient solution by decomposing the reconstruction model into sub-problems. The proposed method undergoes evaluation using both simulated and real-world projection data. Simulation and experimental results demonstrate the efficacy of the proposed approach in enhancing the quality of reconstructed images compared to other competitive variational reconstruction methods. In conclusion, the simultaneous incorporation of sparsity priors of high-order TV and non-local similarity proves advantageous for structural detail recovery in sparse-view CT reconstruction.

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“…Mahmoudi et al [26] combined adaptive-weighted total variation with adaptiveweighted diagonal total variation (AwTV + AwDTV) in 2019. In this method, AwTV considered vertical and horizontal gradients, while AwDTV considered diagonal gradients.…”

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

Cited by 14 publications

References 57 publications

Effects of Beamforming Techniques on Quality of Ultrasound Computed Tomography Images

Effects of Beamforming Techniques on Quality of Ultrasound Computed Tomography Images

A non-local total generalized variation regularization reconstruction method for sparse-view x-ray CT

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

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