A novel reconstruction approach combining OSEM and split Bregman method for low dose CT

Sheng, Jinhua; Chen, Bin; Ma, Yangjie; Shi, Yuchen

doi:10.1016/j.bspc.2020.102095

Cited by 7 publications

(6 citation statements)

References 10 publications

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“…As mentioned earlier, the DCT signal is collected from a total of 48 S‐D measurements, which is far less than the tissue elements (i.e., 1536 unknown αD B ), and results in a severely ill‐posed problem of Equation (). According to our previous efforts, the split‐Bregman algorithm combined with total variation (TV) regularization, namely, Bregman‐TV algorithm, would greatly improve the quality of image reconstruction 44,45 . Specifically, let v = αD B , b = Sl , the target function of the Bregman‐TV proposed for NL‐DCT is as follow.

\begin{array}{c}{v}^{\ast }=\mathrm{argmin}{\left\Vert v\right\Vert}_{TV}\kern0.3em +\kern1em \frac{\mu }{2}{\left\Vert Bv-b\kern0.1em \right\Vert}_2^2\kern0.8000001em \\ {}\mathrm{s}.\mathrm{t}.\kern1.75em {v}_i\ge 0\left(i=1,\kern0.4em 2,\kern0.4em \dots \kern0.4em ,n\right)\end{array}

here, non‐negativity of solutions is enforced.…”

Section: Methodsmentioning

confidence: 99%

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An extensive upgrading of contact diffuse CorrelationTomography system

Zhang,

Ding,

Shang

et al. 2024

Int J Imaging Syst Tech

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Diffuse correlation tomography (DCT) is an emerging tissue blood flow index (BFI) imaging technique that typically requires a large number of source‐detector pairs, resulting in high instrumentation costs. We developed a low‐cost paradigm for upgrading the DCT system with time‐sharing hardware sensors via optical switches, wherein the S‐D configuration was spatially optimized and combined with a novelty Nth‐order linear algorithm for image reconstruction. We verified this system through the phantom experiment and the lower limb skeletal muscle cuff occlusion test. The reconstructed BFI images exhibit the excellent performance of the upgraded DCT system in retrieval of the target location, outline, and anomaly contrast. For the deepest quasi‐solid anomaly, location accuracy was 90% with the αDB‐contrast of 0.75. While, for the deepest liquid tube anomaly, a good linear relationship was achieved between contrast and pump speed, with the αDB‐contrast of 3.29 at 600 mL/h. For human experiments, it was found that the BFI in the relaxed state was ten times higher than in the cuff occlusion state. This study demonstrates the feasibility of the proposed upgraded low‐cost DCT system for diagnosing various diseases associated with local perfusion abnormalities.

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\begin{array}{c}{v}^{\ast }=\mathrm{argmin}{\left\Vert v\right\Vert}_{TV}\kern0.3em +\kern1em \frac{\mu }{2}{\left\Vert Bv-b\kern0.1em \right\Vert}_2^2\kern0.8000001em \\ {}\mathrm{s}.\mathrm{t}.\kern1.75em {v}_i\ge 0\left(i=1,\kern0.4em 2,\kern0.4em \dots \kern0.4em ,n\right)\end{array}

here, non‐negativity of solutions is enforced.…”

Section: Methodsmentioning

confidence: 99%

“…According to our previous efforts, the split-Bregman algorithm combined with total variation (TV) regularization, namely, Bregman-TV algorithm, would greatly improve the quality of image reconstruction. 44,45 Specifically, let v = αD B , b = Sl, the target function of the Bregman-TV proposed for NL-DCT is as follow.…”

Section: Image Reconstruction Approachmentioning

confidence: 99%

An extensive upgrading of contact diffuse CorrelationTomography system

Zhang,

Ding,

Shang

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…( 6) and applying the optimality conditions, the deformation leads to the basic Bregman iterative model as shown in eq. ( 8) [27],…”

Section: Rof-tvmentioning

confidence: 99%

Split Bregman quantum noise removal algorithm for 3D reconstruction of neutron computed tomography image

Zhu,

Liu,

Luo

et al. 2024

EPL

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The low intensity of the neutron source for neutron computed tomography (CT) results in a long acquisition time for a single projection, which causes the neutron projection data to contain a large amount of quantum noise. Quantum noise will degrade the quality of neutron CT reconstruction images. Therefore, an efficient quantum noise removal algorithm must be used in CT reconstruction. In this paper, an efficient quantum noise removal algorithm for neutron CT 3D image reconstruction is proposed by analysing classical image processing algorithms and quantum image processing algorithms, which employs the maximum likelihood expectation-maximization to reconstruct the image and split bregman to solve for the total variation (MLEM-SBTV). Experimental results show that MLEM-SBTV performs well in removing quantum noise and reconstructing the detailed structure of images.

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Section: Introductionmentioning

confidence: 99%

“…[11,12] In contrast, the iterative algorithm uses a system model and regularization, which makes the quality of reconstructed images better than the FBP algorithm, but the computational complexity of each iteration process in the iterative algorithm is equivalent to one FBP algorithm, so the reconstruction speed of the iterative algorithm is usually much slower than that of the FBP algorithm. [13,14] In recent years, deep learning has been applied and made significant progress in various fields such as image restoration, image segmentation, and object detection. Many researchers have also applied deep learning techniques to PET reconstruction, including deep learning as a pre-processing tool in the reconstruction process for processing projection domain data.…”

Section: Introductionmentioning

confidence: 99%

FBP‐CNN: A Direct PET Image Reconstruction Network for Flow Visualization

Fang

Guo

Zhao

et al. 2023

Advcd Theory and Sims

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Dynamic positron emission tomography (PET) imaging has the potential to address technical challenges that persist in the visualization of optically inaccessible flow fields in integrated systems. However, traditional reconstruction algorithms are unable to reconstruct high-quality images from dynamic scan data. In this paper, a neural network structure that can reconstruct high-quality images directly using sinograms as input by combining the filtered back-projection (FBP) algorithm and denoising convolutional neural network (CNN) is proposed, which is named FBP-CNN. Computational fluid dynamics (CFD) software and the Monte Carlo simulation platform are used jointly to generate a dataset for the flow around the bluff body problem. The dataset is then used to train, validate, and test the FBP-CNN network, and the network after completing training is used to reconstruct the real projection data. The results show that FBP-CNN can reconstruct high-quality images from both simulated datasets and real projection data.

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A novel reconstruction approach combining OSEM and split Bregman method for low dose CT

Cited by 7 publications

References 10 publications

An extensive upgrading of contact diffuse CorrelationTomography system

An extensive upgrading of contact diffuse CorrelationTomography system

Split Bregman quantum noise removal algorithm for 3D reconstruction of neutron computed tomography image

FBP‐CNN: A Direct PET Image Reconstruction Network for Flow Visualization

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