Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage

Liu, Tianshuai; Rong, Junyan; Gao, Peng; Zhang, Wenli; Liu, Wenlei

doi:10.1117/1.jbo.23.2.026006

Cited by 20 publications

(19 citation statements)

References 36 publications

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“…The quality of reconstructed CB-XLCT images was evaluated quantitatively by several indexes including the location error (LE), dice similarity coefficient (DICE) and contrast-tonoise ratio (CNR) [30].…”

Section: Quantitative Evaluationmentioning

confidence: 99%

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Regularized reconstruction based on joint L₁ and total variation for sparse-view cone-beam X-ray luminescence computed tomography

Liu

Rong

Gao

et al. 2018

Biomed. Opt. Express

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As an emerging hybrid imaging modality, cone-beam X-ray luminescence computed tomography (CB-XLCT) has been proposed based on the development of X-ray excitable nanoparticles. Owing to the high degree of absorption and scattering of light through tissues, the CB-XLCT inverse problem is inherently ill-conditioned. Appropriate priors or regularizations are needed to facilitate reconstruction and to restrict the search space to a specific solution set. Typically, the goal of CB-XLCT reconstruction is to get the distributions of nanophosphors in the imaging object. Considering that the distributions of nanophosphors inside bodies preferentially accumulate in specific areas of interest, the reconstruction of XLCT images is usually sparse with some locally smoothed high-intensity regions. Therefore, a combination of the L 1 and total variation regularization is designed to improve the imaging quality of CB-XLCT in this study. The L 1 regularization is used for enforcing the sparsity of the reconstructed images and the total variation regularization is used for maintaining the local smoothness of the reconstructed image. The implementation of this method can be divided into two parts. First, the reconstruction image was reconstructed based on the fast iterative shrinkage-thresholding (FISTA) algorithm, then the reconstruction image was minimized by the gradient descent method. Numerical simulations and phantom experiments indicate that compared with the traditional ART, ADAPTIK and FISTA methods, the proposed method demonstrates its advantage in improving spatial resolution and reducing imaging time.

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Section: Quantitative Evaluationmentioning

confidence: 99%

“…30]. To evaluate the reconstruction results of two targets with different distances, numerical simulations were performed with the targets positioned at distances of 3, 2 and 1mm (edge-to-edge distance between the two targets), respectively.…”

mentioning

confidence: 99%

Regularized reconstruction based on joint L₁ and total variation for sparse-view cone-beam X-ray luminescence computed tomography

Liu

Rong

Gao

et al. 2018

Biomed. Opt. Express

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“…Based on this, the study of the light transport model should focus on the luminescence characteristics of Eu 3+ . In XLCT, diffusion equation (DE) is most commonly used to model the photon migration in biological tissues in the studies reported so far, and in all these studies, Eu 3+ is chosen as the luminescent particle (16,(22)(23)(24). In addition, in the research conclusions of Zhang et al, SP 3 simulation is more suitable for Eu 3+ luminescence (25).…”

Section: Introductionmentioning

confidence: 99%

A Finite Element Mesh Regrouping Strategy-Based Hybrid Light Transport Model for Enhancing the Efficiency and Accuracy of XLCT

Chu

et al. 2022

Front. Oncol.

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X-ray luminescence computed tomography (XLCT) is an emerging hybrid imaging modality in optical molecular imaging, which has attracted more attention and has been widely studied. In XLCT, the accuracy and operational efficiency of an optical transmission model play a decisive role in the rapid and accurate reconstruction of light sources. For simulation of optical transmission characteristics in XLCT, considering the limitations of the diffusion equation (DE) and the time and memory costs of simplified spherical harmonic approximation equation (SPN), a hybrid light transport model needs to be built. DE and SPN models are first-order and higher-order approximations of RTE, respectively. Due to the discontinuity of the regions using the DE and SPN models and the inconsistencies of the system matrix dimensions constructed by the two models in the solving process, the system matrix construction of a hybrid light transmission model is a problem to be solved. We provided a new finite element mesh regrouping strategy-based hybrid light transport model for XLCT. Firstly, based on the finite element mesh regrouping strategy, two separate meshes can be obtained. Thus, for DE and SPN models, the system matrixes and source weight matrixes can be calculated separately in two respective mesh systems. Meanwhile, some parallel computation strategy can be combined with finite element mesh regrouping strategy to further save the system matrix calculation time. Then, the two system matrixes with different dimensions were coupled though repeated nodes were processed according to the hybrid boundary conditions, the two meshes were combined into a regrouping mesh, and the hybrid optical transmission model was established. In addition, the proposed method can reduce the computational memory consumption than the previously proposed hybrid light transport model achieving good balance between computational accuracy and efficiency. The forward numerical simulation results showed that the proposed method had better transmission accuracy and achieved a balance between efficiency and accuracy. The reverse simulation results showed that the proposed method had superior location accuracy, morphological recovery capability, and image contrast capability in source reconstruction. In-vivo experiments verified the practicability and effectiveness of the proposed method.

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“…Nevertheless, scanning around the sample is still necessary, which limits further improvement of XLCT imaging speed for real time applications such as detecting dynamic physiological responses. For example, in cone-beam XLCT [23,[26][27][28][29][30], either sample or optical detector (camera) needs to be rotated in order to obtain multi-view projections for three-dimensional (3D) image reconstruction. Theoretically, the more views of data are acquired, making the inverse problem in XLCT image reconstruction less ill-conditioned, the higher spatial resolution can be achieved.…”

Section: Introductionmentioning

confidence: 99%

High‐speed X‐ray‐induced luminescence computed tomography

Dai

Cheng

Zhao

et al. 2020

Journal of Biophotonics

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X‐ray‐induced luminescence computed tomography (XLCT) is an emerging molecular imaging. Challenges in improving spatial resolution and reducing the scan time in a whole‐body field of view (FOV) still remain for practical in vivo applications. In this study, we present a novel XLCT technique capable of obtaining three‐dimensional (3D) images from a single snapshot. Specifically, a customed two‐planar‐mirror component is integrated into a cone beam XLCT imaging system to obtain multiple optical views of an object simultaneously. Furthermore, a compressive sensing based algorithm is adopted to improve the efficiency of 3D XLCT image reconstruction. Numerical simulations and experiments were conducted to validate the single snapshot X‐ray‐induced luminescence computed tomography (SS‐XLCT). The results show that the 3D distribution of the nanophosphor targets can be visualized much faster than conventional cone beam XLCT imaging method that was used in our comparisons while maintaining comparable spatial resolution as in conventional XLCT imaging. SS‐XLCT has the potential to harness the power of XLCT for rapid whole‐body in vivo molecular imaging of small animals.

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Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage

Cited by 20 publications

References 36 publications

Regularized reconstruction based on joint L₁ and total variation for sparse-view cone-beam X-ray luminescence computed tomography

Regularized reconstruction based on joint L₁ and total variation for sparse-view cone-beam X-ray luminescence computed tomography

A Finite Element Mesh Regrouping Strategy-Based Hybrid Light Transport Model for Enhancing the Efficiency and Accuracy of XLCT

High‐speed X‐ray‐induced luminescence computed tomography

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Cone-beam x-ray luminescence computed tomography based on x-ray absorption dosage

Cited by 20 publications

References 36 publications

Regularized reconstruction based on joint L1 and total variation for sparse-view cone-beam X-ray luminescence computed tomography

Regularized reconstruction based on joint L1 and total variation for sparse-view cone-beam X-ray luminescence computed tomography

A Finite Element Mesh Regrouping Strategy-Based Hybrid Light Transport Model for Enhancing the Efficiency and Accuracy of XLCT

High‐speed X‐ray‐induced luminescence computed tomography

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Regularized reconstruction based on joint L₁ and total variation for sparse-view cone-beam X-ray luminescence computed tomography

Regularized reconstruction based on joint L₁ and total variation for sparse-view cone-beam X-ray luminescence computed tomography