scholarly journals Normalized Born Approximation-Based Two-Stage Reconstruction Algorithm for Quantitative Fluorescence Molecular Tomography

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Huangjian Yi ◽  
Duofang Chen ◽  
Wei Li ◽  
Shuang Zhou ◽  
Miao Ning ◽  
...  
Keyword(s):  
Born Approximation ◽  
Small Animal Imaging ◽  
Small Animal ◽  
Reconstruction Algorithm ◽  
Reconstruction Technique ◽  
Reconstruction Method ◽  
Fluorescence Molecular Tomography ◽  
Two Stage ◽  
Quantitative Reconstruction

Fluorescence molecular tomography (FMT) is a promising technique forin vivosmall animal imaging. In this paper, a two-stage reconstruction method based on normalized Born approximation is developed for FMT, which includes two steps for quantitative reconstruction. First, the localization of fluorescent fluorophore is determined byl1-norm regularization method. Then, in the location region of fluorophore, which is provided by the first stage, algebraic reconstruction technique (ART) is utilized for the fluorophore concentration reconstruction. The validity of the two-stage quantitative reconstruction algorithm is testified by simulation experiments on a 3D digital mouse atlas and physical experiments on a phantom. The results suggest that we are able to recover the fluorophore location and concentration.

scholarly journals Effective and robust approach for fluorescence molecular tomography based on CoSaMP and SP3 model

2016 ◽  
Vol 09 (06) ◽  
pp. 1650024 ◽  
Author(s):  
Xiaowei He ◽  
Hongbo Guo ◽  
Jingjing Yu ◽  
Xu Zhang ◽  
Yuqing Hou
Keyword(s):  
3D Reconstruction ◽  
Light Propagation ◽  
Matching Pursuit ◽  
Reconstruction Algorithm ◽  
Biological Tissues ◽  
Signal Recovery ◽  
Reconstruction Method ◽  
Fluorescence Molecular Tomography ◽  
Reconstruction Accuracy

Fluorescence molecular tomography (FMT) allows the detection and quantification of various biological processes in small animals in vivo, which expands the horizons of pre-clinical research and drug development. Efficient three-dimensional (3D) reconstruction algorithm is the key to accurate localization and quantification of fluorescent target in FMT. In this paper, 3D reconstruction of FMT is regarded as a sparse signal recovery problem and the compressive sampling matching pursuit (CoSaMP) algorithm is adopted to obtain greedy recovery of fluorescent signals. Moreover, to reduce the modeling error, the simplified spherical harmonics approximation to the radiative transfer equation (RTE), more specifically [Formula: see text], is utilized to describe light propagation in biological tissues. The performance of the proposed reconstruction method is thoroughly evaluated by simulations on a 3D digital mouse model by comparing it with three representative greedy methods including orthogonal matching pursuit (OMP), stagewise OMP(StOMP), and regularized OMP (ROMP). The CoSaMP combined with [Formula: see text] shows an improvement in reconstruction accuracy and exhibits distinct advantages over the comparative algorithms in multiple targets resolving. Stability analysis suggests that CoSaMP is robust to noise and performs stably with reduction of measurements. The feasibility and reconstruction accuracy of the proposed method are further validated by phantom experimental data.

In-vivo X-ray Microtomography System for Small Animal Imaging

2007 ◽  
pp. 1649-1652
Author(s):  
Ki-Yong Nam ◽  
J. H. Lim ◽  
J. Park ◽  
H. H. Sohn ◽  
H. K. Kim ◽  
...  
Keyword(s):  
Small Animal Imaging ◽  
Small Animal ◽  
X Ray ◽  
Animal Imaging

MEMS-based side-view endomicroscope for in vivo small animal imaging (Conference Presentation)

2017 ◽  
Author(s):  
Xiyu Duan ◽  
Haijun Li ◽  
Gaoming Li ◽  
Xue Li ◽  
Kenn R. Oldham ◽  
...  
Keyword(s):  
Small Animal Imaging ◽  
Small Animal ◽  
Side View ◽  
Animal Imaging

Sci-Fri AM: YIS-02: Evaluation of the LabPET4 imaging capabilities for in vivo small animal imaging

2008 ◽  
Vol 35 (7Part3) ◽  
pp. 3410-3410
Author(s):  
M Bergeron ◽  
J Cadorette ◽  
M-A Tétrault ◽  
N Viscogliosi ◽  
J-F Beaudoin ◽  
...  
Keyword(s):  
Small Animal Imaging ◽  
Small Animal ◽  
Animal Imaging

NEMA NU 4-2008 and in vivo imaging performance of RAYCAN trans-PET/CT X5 small animal imaging system

2019 ◽  
Vol 64 (11) ◽  
pp. 115014 ◽  
Author(s):  
J Teuho ◽  
C Han ◽  
L Riehakainen ◽  
A Honkaniemi ◽  
M Tirri ◽  
...  
Keyword(s):  
In Vivo Imaging ◽  
Imaging System ◽  
Small Animal Imaging ◽  
Small Animal ◽  
Animal Imaging ◽  
Pet Ct ◽  
Imaging Performance

scholarly journals The Combination of In vivo 124I-PET and CT Small Animal Imaging for Evaluation of Thyroid Physiology and Dosimetry

Diagnostics ◽  
2012 ◽  
Vol 2 (2) ◽  
pp. 10-22 ◽  
Author(s):  
Henrik H. El-Ali ◽  
Martin Eckerwall ◽  
Dorthe Skovgaard ◽  
Erik Larsson ◽  
Sven-Erik Strand ◽  
...  
Keyword(s):  
Small Animal Imaging ◽  
Small Animal ◽  
Animal Imaging

scholarly journals Application of Blind Deblurring Reconstruction Technique to SPECT Imaging

2007 ◽  
Vol 2007 ◽  
pp. 1-9 ◽  
Author(s):  
Heng Li ◽  
Yibin Zheng
Keyword(s):  
Experimental Studies ◽  
Small Animal ◽  
Ground Truth ◽  
Reconstruction Algorithm ◽  
Reconstruction Technique ◽  
Spect Image ◽  
Blind Deblurring ◽  
Image Blurring ◽  
Reconstruction Efficiency ◽  
Spect System

An SPECT image can be approximated as the convolution of the ground truth spatial radioactivity with the system point spread function (PSF). The PSF of an SPECT system is determined by the combined effect of several factors, including the gamma camera PSF, scattering, attenuation, and collimator response. It is hard to determine the SPECT system PSF analytically, although it may be measured experimentally. We formulated a blind deblurring reconstruction algorithm to estimate both the spatial radioactivity distribution and the system PSF from the set of blurred projection images. The algorithm imposes certain spatial-frequency domain constraints on the reconstruction volume and the PSF and does not otherwise assume knowledge of the PSF. The algorithm alternates between two iterative update sequences that correspond to the PSF and radioactivity estimations, respectively. In simulations and a small-animal study, the algorithm reduced image blurring and preserved the edges without introducing extra artifacts. The localized measurement shows that the reconstruction efficiency of SPECT images improved more than 50% compared to conventional expectation maximization (EM) reconstruction. In experimental studies, the contrast and quality of reconstruction was substantially improved with the blind deblurring reconstruction algorithm.

scholarly journals A CT Reconstruction Algorithm Based on Non-Aliasing Contourlet Transform and Compressive Sensing

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Lu-zhen Deng ◽  
Peng Feng ◽  
Mian-yi Chen ◽  
Peng He ◽  
Quang-sang Vo ◽  
...  
Keyword(s):  
Compressive Sensing ◽  
Iteration Process ◽  
Reconstruction Algorithm ◽  
Ct Images ◽  
Contourlet Transform ◽  
Projection Data ◽  
Reconstruction Technique ◽  
Reconstruction Method ◽  
Ct Reconstruction ◽  
Split Bregman

Compressive sensing (CS) theory has great potential for reconstructing CT images from sparse-views projection data. Currently, total variation (TV-) based CT reconstruction method is a hot research point in medical CT field, which uses the gradient operator as the sparse representation approach during the iteration process. However, the images reconstructed by this method often suffer the smoothing problem; to improve the quality of reconstructed images, this paper proposed a hybrid reconstruction method combining TV and non-aliasing Contourlet transform (NACT) and using the Split-Bregman method to solve the optimization problem. Finally, the simulation results show that the proposed algorithm can reconstruct high-quality CT images from few-views projection using less iteration numbers, which is more effective in suppressing noise and artefacts than algebraic reconstruction technique (ART) and TV-based reconstruction method.

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