Bayesian Framework Based Direct Reconstruction of Fluorescence Parametric Images

et al. 2015

Biomed. Opt. Express

Self Cite

Dynamic fluorescence molecular tomography (FMT) is an attractive imaging technique for three-dimensionally resolving the metabolic process of fluorescent biomarkers in small animal. When combined with compartmental modeling, dynamic FMT can be used to obtain parametric images which can provide quantitative pharmacokinetic information for drug development and metabolic research. However, the computational burden of dynamic FMT is extremely huge due to its large data sets arising from the long measurement process and the densely sampling device. In this work, we propose to accelerate the reconstruction process of dynamic FMT based on principal component analysis (PCA). Taking advantage of the compression property of PCA, the dimension of the sub weight matrix used for solving the inverse problem is reduced by retaining only a few principal components which can retain most of the effective information of the sub weight matrix. Therefore, the reconstruction process of dynamic FMT can be accelerated by solving the smaller scale inverse problem. Numerical simulation and mouse experiment are performed to validate the performance of the proposed method. Results show that the proposed method can greatly accelerate the reconstruction of parametric images in dynamic FMT almost without degradation in image quality. Szabo, and Y. Wang, "CAM-CM: A signal deconvolution tool for in vivo dynamic contrast-enhanced imaging of complex tissues," Bioinformatics 27(18), 2607-2609 (2011). 34. X. Guo, X. Liu, X. Wang, F. Tian, F. Liu, B. Zhang, G. Hu, and J. Bai, "A combined fluorescence and microcomputed tomography system for small animal imaging," IEEE Trans.

Section: Discussionmentioning

confidence: 99%

Acceleration of dynamic fluorescence molecular tomography with principal component analysis

et al. 2015

Biomed. Opt. Express

Self Cite

“…Since biological tissues have highly scattering and weakly absorbing properties in the visible or NIR spectral window, the transport of the emitted light can be modeled by the diffusion equation (DE) [28]

- \nabla \cdot false[D false(boldr false) \nabla normalΦ false(boldr false) false] + μ_{a} false(boldr false) normalΦ false(boldr false) = S false(boldr false) false(boldr \in normalΩ false)

where Ω is the image domain, Φ( r ) is the photon fluence generated by the light source S ( r ), μ a ( r ) is the absorption coefficient, D ( r ) is the diffusion coefficient given by

D false(boldr false) = 1 / true[3 true(μ_{s}^{'} false(boldr false) + μ_{a} false(boldr false) true) true]

, with

μ_{s}^{'} false(boldr false)

representing the reduced scattering coefficient. The DE is constrained by the Robin boundary condition [27].…”

Section: Theorymentioning

confidence: 99%

“…An alternating optimization scheme is employed to solve the above joint estimation problem [28]. In this scheme, the unknown hyperparameters are alternately estimated in each iteration, and the image is updated each time after the estimated hyperparameters are obtained.…”

Section: Theorymentioning

confidence: 99%

See 1 more Smart Citation

Cone Beam X-ray Luminescence Computed Tomography Based on Bayesian Method

IEEE Trans. Med. Imaging

Liu

et al. 2017

Self Cite

X-ray luminescence computed tomography (XLCT), which aims to achieve molecular and functional imaging by X-rays, has recently been proposed as a new imaging modality. Combining the principles of X-ray excitation of luminescence-based probes and optical signal detection, XLCT naturally fuses functional and anatomical images and provides complementary information for a wide range of applications in biomedical research. In order to improve the data acquisition efficiency of previously developed narrow-beam XLCT, a cone beam XLCT (CB-XLCT) mode is adopted here to take advantage of the useful geometric features of cone beam excitation. Practically, a major hurdle in using cone beam X-ray for XLCT is that the inverse problem here is seriously ill-conditioned, hindering us to achieve good image quality. In this paper, we propose a novel Bayesian method to tackle the bottleneck in CB-XLCT reconstruction. The method utilizes a local regularization strategy based on Gaussian Markov random field to mitigate the ill-conditioness of CB-XLCT. An alternating optimization scheme is then used to automatically calculate all the unknown hyperparameters while an iterative coordinate descent algorithm is adopted to reconstruct the image with a voxel-based closed-form solution. Results of numerical simulations and mouse experiments show that the self-adaptive Bayesian method significantly improves the CB-XLCT image quality as compared with conventional methods.

“…These methods are of great significance to avoid the ambiguity of the solution and obtain the high-precision reconstruction image. The Bayesian inference method based on statistical iteration can effectively utilize the physical effects of the system, the statistical properties of the projection data, and the noise [20][21][22][23][24]. The statistical iterative reconstruction method considers the statistical distribution of signal and noise, but it has the problem of high computational complexity and the slow convergence speed.…”

Section: Introductionmentioning

confidence: 99%

Variational Bayesian blind restoration reconstruction based on shear wave transform for low-dose medical CT image

Sun

Teng

et al. 2017

J Image Video Proc.

A variational Bayesian blind restoration reconstruction based on shear wave transform for low-dose medical computed tomography (CT) image is proposed. The proposed algorithm eliminates the effects of the point spread function in the process of low-dose medical CT image reconstruction and improves the reconstructed image quality. The shear wave transform is used to sparsely represent the CT image, which can speed up the efficiency of image processing. In the Bayesian framework, a posteriori probability objective function with unknown parameters is constructed. These unknown parameters include the shear wave coefficients, the point spread function, and the hyper parameters. It specified a Laplacian distribution model for the prior probability distribution of the shear wave coefficients. The autoregressive model is used as the prior model of the point spread function. All of hyper parameters follow the gamma distribution. The variational Bayesian method is used to estimate all of unknown parameters and solve the above posteriori probability objective function. These generalized parameter estimators are used to realize the low-dose CT image blind restoration reconstruction. Computer simulation results indicate that a good performance-reconstructed image can be obtained and some metrics such as peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), structural similarity index metric (SSIM), and sum of square differences error (SSDE) are improved.