Application of Blind Deblurring Reconstruction Technique to SPECT Imaging

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.

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

Journal of Electrical and Computer Engineering ◽

10.1155/2012/838967 ◽

2012 ◽

Vol 2012 ◽

pp. 1-9 ◽

Cited By ~ 2

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.

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Blind Deblurring Reconstruction Technique with Applications in PET Imaging

International Journal of Biomedical Imaging ◽

10.1155/2009/718157 ◽

2009 ◽

Vol 2009 ◽

pp. 1-6 ◽

Cited By ~ 3

Author(s):

Heng Li ◽

Osama R. Mawlawi ◽

Ronald X. Zhu ◽

Yibin Zheng

Keyword(s):

Experimental Data ◽

Synthetic Data ◽

Reconstruction Technique ◽

Blind Deblurring ◽

Spread Function ◽

System Reconstruction ◽

Reconstruction Image ◽

Image Blurring ◽

Account System

We developed an empirical PET model taking into account system blurring and a blind iterative reconstruction scheme that estimates both the actual image and the point spread function of the system. Reconstruction images of high quality can be acquired by using the proposed reconstruction technique for both synthetic and experimental data. In the synthetic data study, the algorithm reduces image blurring and preserves the edges without introducing extra artifacts. The localized measurement shows that the performance of the reconstruction image improved by up to 100%. In experimental data studies, the contrast and quality of reconstruction is substantially improved. The proposed method shows promise in tumor localization and quantification.

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Reconstruction algorithm of ultrasound CT based on simultaneous iterative reconstruction technique

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.01844 ◽

2010 ◽

Vol 30 (7) ◽

pp. 1844-1846 ◽

Cited By ~ 3

Author(s):

Hao-quan WANG

Keyword(s):

Iterative Reconstruction ◽

Reconstruction Algorithm ◽

Reconstruction Technique ◽

Iterative Reconstruction Technique ◽

Simultaneous Iterative Reconstruction Technique

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Performance Analysis of Surface Reconstruction Algorithms in Vertical Scanning Interferometry Based on Coherence Envelope Detection

Micromachines ◽

10.3390/mi12020164 ◽

2021 ◽

Vol 12 (2) ◽

pp. 164

Author(s):

Dongxu Wu ◽

Fusheng Liang ◽

Chengwei Kang ◽

Fengzhou Fang

Keyword(s):

Surface Reconstruction ◽

Measurement Accuracy ◽

Experimental Studies ◽

Reconstruction Algorithm ◽

Low Noise ◽

Surface Measurement ◽

Reconstruction Algorithms ◽

Centroid Method ◽

Envelope Detection ◽

Vertical Scanning Interferometry

Optical interferometry plays an important role in the topographical surface measurement and characterization in precision/ultra-precision manufacturing. An appropriate surface reconstruction algorithm is essential in obtaining accurate topography information from the digitized interferograms. However, the performance of a surface reconstruction algorithm in interferometric measurements is influenced by environmental disturbances and system noise. This paper presents a comparative analysis of three algorithms commonly used for coherence envelope detection in vertical scanning interferometry, including the centroid method, fast Fourier transform (FFT), and Hilbert transform (HT). Numerical analysis and experimental studies were carried out to evaluate the performance of different envelope detection algorithms in terms of measurement accuracy, speed, and noise resistance. Step height standards were measured using a developed interferometer and the step profiles were reconstructed by different algorithms. The results show that the centroid method has a higher measurement speed than the FFT and HT methods, but it can only provide acceptable measurement accuracy at a low noise level. The FFT and HT methods outperform the centroid method in terms of noise immunity and measurement accuracy. Even if the FFT and HT methods provide similar measurement accuracy, the HT method has a superior measurement speed compared to the FFT method.

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Optimization of a High Resolution Small Animal SPECT System using GATE and STIR Software

2018 IEEE Nuclear Science Symposium and Medical Imaging Conference Proceedings (NSS/MIC) ◽

10.1109/nssmic.2018.8824511 ◽

2018 ◽

Author(s):

Han Gyu Kang ◽

Hideaki Tashima ◽

Seong Jong Hong ◽

Taiga Yamaya

Keyword(s):

High Resolution ◽

Small Animal ◽

Spect System

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Investigation and Validation of the Reconstruction Algorithm for Small Animal Cone-beam CT Imaging

ACTA PHOTONICA SINICA ◽

10.3788/gzxb20103909.1588 ◽

2010 ◽

Vol 39 (9) ◽

pp. 1588-1593

Author(s):

缪辉 MIAO Hui ◽

王秋殷 WANG Qiu-yin ◽

赵会娟 ZHAO Hui-juan ◽

王婷婷 WANG Ting-ting ◽

高峰 GAO Feng

Keyword(s):

Cone Beam Ct ◽

Small Animal ◽

Reconstruction Algorithm ◽

Ct Imaging ◽

Cone Beam

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Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5761 ◽

2020 ◽

Vol 34 (04) ◽

pp. 3553-3560 ◽

Cited By ~ 1

Author(s):

Ze-Sen Chen ◽

Xuan Wu ◽

Qing-Guo Chen ◽

Yao Hu ◽

Min-Ling Zhang

Keyword(s):

Predictive Model ◽

Clustering Analysis ◽

Experimental Studies ◽

Ground Truth ◽

Two Stage ◽

Feature Vectors ◽

Similarity Graph ◽

Training Examples ◽

Guided Clustering

In multi-view multi-label learning (MVML), each training example is represented by different feature vectors and associated with multiple labels simultaneously. Nonetheless, the labeling quality of training examples is tend to be affected by annotation noises. In this paper, the problem of multi-view partial multi-label learning (MVPML) is studied, where the set of associated labels are assumed to be candidate ones and only partially valid. To solve the MVPML problem, a two-stage graph-based disambiguation approach is proposed. Firstly, the ground-truth labels of each training example are estimated by disambiguating the candidate labels with fused similarity graph. After that, the predictive model for each label is learned from embedding features generated from disambiguation-guided clustering analysis. Extensive experimental studies clearly validate the effectiveness of the proposed approach in solving the MVPML problem.

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