Image reconstruction in sparse-view CT using improved nonlocal total variation regularization

Kim, Yongchae; Kudo, Hiroyuki; Chigita, Kazuki; Lian, Songzhe

doi:10.1117/12.2529164

Cited by 5 publications

(5 citation statements)

References 2 publications

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“…Kim et al proposed a non-local TV regularisation method for SVCT reconstruction method, which can preserve the smooth intensity changes of CT images. The method can achieve reconstruction of 50-100 views by 20 iterations [15]. Selim used the prior information extracted in the reconstruction process instead of the prior information prepared in advance to extract the prior information through the reconstruction process [16].…”

Section: Jinst 19 P03009mentioning

confidence: 99%

CT image reconstruction via industrial CT fast scanning

Bai,

Du,

Long

2024

J. Inst.

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In automated manufacturing and safety inspection, there is a high demand for fast computed tomography (CT) scanning and image reconstruction. Currently, faster scanning can be achieved by reducing the X-ray exposure time within sparse view CT. The faster scanning strategy introduces significant streak artefacts and noise during the sampling process. Consequently, streak artefacts and noise need to be simultaneously suppressed, which is poses a challenge for existing reconstruction methods. This paper presents a fast iterative reconstruction algorithm that can simultaneously suppress both streak artefacts and noise. This method can not only reconstruct high-fidelity images from rapidly acquired projection data, but also has a faster reconstruction speed than the existing iterative reconstruction algorithms. First, we present a high-order multi-directional total variation (HOM-TV) method that specifically focuses on preserving edge details of the image. Then, we present a fast iterative reconstruction model by incorporating HOM-TV and non-local means into the objective function. Finally, the effectiveness of the presented reconstruction model is validated by simulation and real experiments. The faster scanning method can complete the scan in only 5 seconds, and the structural similarity index (SSIM) of the CT image reconstructed by our method is 0.9755, which is higher than 0.0175 of the Fast Null Space Reconstruction (FNSR) algorithm. The peak signal-to-noise ratio (PSNR) index is 1.656, which is higher than that of the contrast algorithm. In terms of reconstruction time, our algorithm can achieve reconstruction in as little as 36 seconds, outperforming the baseline algorithms.

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Section: Jinst 19 P03009mentioning

confidence: 99%

CT image reconstruction via industrial CT fast scanning

Bai,

Du,

Long

2024

J. Inst.

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“…In particular, penalized weighted least squares (PWLS) approaches have been popular for CT image reconstruction that optimize a combination of a statistically weighted quadratic data-fidelity term (capturing the forward and noise model) and a regularizer penalty that captures prior information of the object [11]. MBIR methods have often used simple regularizers [12] such as edge-preserving regularization involving nonquadratic functions of differences between neighboring pixels [13] (implying image gradients may be sparse) or other improved regularizers [14], [15], [16], [17].…”

Section: A Backgroundmentioning

confidence: 99%

Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data

Lahiri¹,

Klasky²,

Fessler³

et al. 2022

Preprint

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Reconstruction of CT images from a limited set of projections through an object is important in several applications ranging from medical imaging to industrial settings. As the number of available projections decreases, traditional reconstruction techniques such as the FDK algorithm and modelbased iterative reconstruction methods perform poorly. Recently, data-driven methods such as deep learning-based reconstruction have garnered a lot of attention in applications because they yield better performance when enough training data is available. However, even these methods have their limitations when there is a scarcity of available training data. This work focuses on image reconstruction in such settings, i.e., when both the number of available CT projections and the training data is extremely limited. We adopt a sequential reconstruction approach over several stages using an adversarially trained shallow network for 'destreaking' followed by a data-consistency update in each stage. To deal with the challenge of limited data, we use image subvolumes to train our method, and patch aggregation during testing. To deal with the computational challenge of learning on 3D datasets for 3D reconstruction, we use a hybrid 3D-to-2D mapping network for the 'destreaking' part. Comparisons to other methods over several test examples indicate that the proposed method has much potential, when both the number of projections and available training data are highly limited.

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“…In the sparse-view CT [23,24], by using the projection data corresponding to less than 100 directions (the conventional CT uses 1000-2000 projection data), the equation…”

Section: Problem Definitionmentioning

confidence: 99%

“…Nonlocal Total Variation (TV) [ 1 , 2 , 3 , 4 , 5 , 6 ] was proposed as an improved version of ordinary TV. Nonlocal TV can use a weighting function (e.g., the weight of nonlocal means filter) by taking the intensity difference between the pixel pair into account, and can obtain higher image quality than the ordinary TV that uses only pairs of adjacent pixels.…”

Section: Introductionmentioning

confidence: 99%

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Nonlocal Total Variation Using the First and Second Order Derivatives and Its Application to CT image Reconstruction

Kim¹,

Kudo²

2020

Sensors

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We propose a new class of nonlocal Total Variation (TV), in which the first derivative and the second derivative are mixed. Since most existing TV considers only the first-order derivative, it suffers from problems such as staircase artifacts and loss in smooth intensity changes for textures and low-contrast objects, which is a major limitation in improving image quality. The proposed nonlocal TV combines the first and second order derivatives to preserve smooth intensity changes well. Furthermore, to accelerate the iterative algorithm to minimize the cost function using the proposed nonlocal TV, we propose a proximal splitting based on Passty’s framework. We demonstrate that the proposed nonlocal TV method achieves adequate image quality both in sparse-view CT and low-dose CT, through simulation studies using a brain CT image with a very narrow contrast range for which it is rather difficult to preserve smooth intensity changes.

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Image reconstruction in sparse-view CT using improved nonlocal total variation regularization

Cited by 5 publications

References 2 publications

CT image reconstruction via industrial CT fast scanning

CT image reconstruction via industrial CT fast scanning

Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data

Nonlocal Total Variation Using the First and Second Order Derivatives and Its Application to CT image Reconstruction

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