Acceleration of dynamic fluorescence molecular tomography with principal component analysis

Abstract

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.

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

“…All XCT slices of the investigated region were manually segmented to separate the liver, lungs and other tissues. A heterogeneous forward model was constructed by assigning the corresponding optical properties to the segmented organs [35]. …”

“…As shown in Fig. 4(a), a Digimouse atlas [35] containing the liver and lungs was employed to construct a 3-D simulation model. Two hypothetical cylindrical tumors of 3 mm in diameter and 4 mm in height were placed in the liver.…”

“…The scattering of X-rays was neglected. A heterogeneous forward model was constructed by assigning adequate optical properties to the segmented organs [35]. The simulation model was rotated for 360°, with 24 projections ( S = 24) acquired from the surface with an angular increment of 15°.…”

Reconstruction method for fluorescence molecular tomography based on L1-norm primal accelerated proximal gradient

“…Fluorescence molecular tomography (FMT) is an emerging imaging modality that enables three-dimensionally (3-D) quantitative observation of imaging targets and pathways at the molecular and cellular level. [1][2][3][4][5][6] FMT has been widely used in the preclinical research of oncology, which can noninvasively show the dynamic interactions of fluorescent targets. Because of its quantification characteristics, FMT has been considered as an important tool for tumor diagnostic imaging and basic researches.…”

Facilitating in vivo tumor localization by principal component analysis based on dynamic fluorescence molecular imaging

“…Functioning as a feature extraction algorithm, principal component analysis (PCA) assesses the original data and detects new variables called principal components (PCs), which are linear combinations of the original data. 50 Our group has used PCA to facilitate dynamic fluorescence diffuse optical tomography, 51,52 accelerate fluorescence molecular imaging, [53][54][55] and separate fluorescent targets with different fluorophore concentrations. 56 PCA has also been applied on dynamic fluorescence image sequences to extract in vivo anatomical maps, locate internal organs, 57 distinguish arteries and veins, 58 pinpoint tumor with the help of carbon nanotubes, 59 and extract physiological features related to both vasculopathy 60,61 and rheumatoid arthritis.…”