Bioluminescence Tomography Based on Gaussian Weighted Laplace Prior Regularization for <italic>In Vivo</italic> Morphological Imaging of Glioma

Abstract

Bioluminescence tomography (BLT) is a powerful non-invasive molecular imaging tool for in vivo studies of glioma in mice. However, because of the light scattering and resulted ill-posed problems, it is challenging to develop a sufficient reconstruction method, which can accurately locate the tumor and define the tumor morphology in three-dimension. In this paper, we proposed a novel Gaussian weighted Laplace prior (GWLP) regularization method. It considered the variance of the bioluminescence energy between any two voxels inside an organ had a non-linear inverse relationship with their Gaussian distance to solve the over-smoothed tumor morphology in BLT reconstruction. We compared the GWLP with conventional Tikhonov and Laplace regularization methods through various numerical simulations and in vivo orthotopic glioma mouse model experiments. The in vivo magnetic resonance imaging and ex vivo green fluorescent protein images and hematoxylin-eosin stained images of whole head cryoslicing specimens were utilized as gold standards. The results demonstrated that GWLP achieved the highest accuracy in tumor localization and tumor morphology preservation. To the best of our knowledge, this is the first study that achieved such accurate BLT morphological reconstruction of orthotopic glioma without using any segmented tumor structure from any other structural imaging modalities as the prior for reconstruction guidance. This enabled BLT more suitable and practical for in vivo imaging of orthotopic glioma mouse models.

Sparse‐graph manifold learning method for bioluminescence tomography

“…It should be noted that only the single‐spectrum and single‐view surface measurement was used to BLT reconstruction in three in vivo experiments. With the help of some effective reconstruction strategy, such as the permissible source region based on the region of interest (ROI) and multispectral resolved measurements , the performance of the SGML method will be further improved.…”

“…To verify the performance of our proposed algorithm, three comparative algorithms, including the sparse reconstruction algorithm based on ℓ 1/2 ‐norm regularization , the morphology recovery algorithm based on Gaussian weighted Laplace prior (GWLP) regularization , and the manifold regularization method based on gradient projection‐resolved Laplacian manifold (GPRLM) based on a joint ℓ 1 and Laplacian regularization were adopted for comparison.…”

“…In the past decade, BLT has widely been used in preclinical researches. In tumor imaging and monitoring, BLT has been applied in glioma imaging , liver cancer detection , gastric cancer imaging , monitoring breast tumor growth and metastatic spreading , and in vivo stem cell tracking . In radiation oncology, BLT has been utilized to guide irradiation delivery and quantitatively assess irradiation‐induced tumor response .…”

“…By assuming that the source distribution is piecewise‐constant, the level set algorithm and total variation regularization method have been proposed for solving the inverse problem of bioluminescence tomography . By considering the variance between any two voxels decreases with the increasing of their spatial distance, the Gaussian weighted Laplace prior regularization method and block sparse bayesian learning method were proposed for in vivo morphological imaging of glioma in BLT reconstruction . In this research, the shape‐constraint or energy‐constraint was introduced in the optimization function, which results in higher accuracy in tumor localization and morphology preservation.…”

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

“…In this study, based on the proposed dose-based forward model, the adaptive Tikhonov regularization method was adopted for adaptive reconstruction of CB-XLCT imaging. To further improve the image quality, statistical reconstruction methods using regularizations, such as the Bayesian method based on Gaussian Markov random field proposed by Zhang et al, 22 total variance (TV) regularization, 38 and Laplace regularization, 39 can be used or extended for CB-XLCT imaging. The combination of the proposed model with regularized reconstruction is under investigation.…”