Image Reconstruction of Single Photon Emission Computed Tomography (SPECT) on a Bubble Column Using Expectation Maximization and Exact Inversion Algorithms: Comparison Study by Means of Numerical Phantom

2015 ◽  
Vol 1087 ◽  
pp. 424-428
Author(s):  
Azhani Mohd Razali ◽  
Jaafar Abdullah
Keyword(s):  
Image Reconstruction ◽  
Expectation Maximization ◽  
Chemical Engineering ◽  
Single Photon ◽  
Bubble Column ◽  
Photon Emission ◽  
Expectation Maximization Algorithm ◽  
Emission Computed Tomography ◽  
Mathematical Methods ◽  
Reconstruction Methods

Expectation Maximization Algorithm and the Exact Inversion Formula are two mathematical methods that have been developed for various computational applications, such as in medical imaging, nuclear industries, econometric and sociological studies, as well as chemical engineering industries. These image reconstruction methods are usually used to create the SPECT scan images. However, most of the improvement and development of the images are made by using a medical phantom, such as the human brain phantom. Here, in this paper the reconstruction of images by both algorithms are made by using a numerical phantom of laboratory scale bubble columns due to its wide application in the chemical reaction engineering studies. The results for both algorithms are compared, evaluated and discussed.

scholarly journals Reduction of Collimator Correction Artefacts with Bayesian Reconstruction in Spect

2011 ◽  
Vol 2011 ◽  
pp. 1-6 ◽  
Author(s):  
Tuija Kangasmaa ◽  
Antti Sohlberg ◽  
Jyrki T. Kuikka
Keyword(s):  
Single Photon ◽  
Photon Emission ◽  
Emission Computed Tomography ◽  
Good Resolution ◽  
Bayesian Reconstruction ◽  
Reconstruction Methods ◽  
Lower Contrast ◽  
Photon Emission Computed Tomography ◽  
Patient Studies ◽  
Median Root Prior

Poor resolution of single photon emission computed tomography (SPECT) has degraded its use in clinical practice. Collimator correction has been shown to improve the reconstructed resolution, but the correction can generate ringing artefacts, which lower image quality. This paper investigates whether Bayesian reconstruction methods could reduce these artefacts. We have applied and tested three Bayesian reconstruction methods: smoothing prior, median root prior, and anatomical prior. To demonstrate the efficacy of these methods, we compared their physical and visual performance both in phantom and patient studies. All the three Bayesian reconstruction methods reduced the collimator correction artefacts. Images reconstructed using the smoothing prior and the median root prior had slightly lower contrast than the standard reconstruction with collimator correction, whereas the anatomical prior produced images with good resolution and contrast.

scholarly journals A Length Penalized Probabilistic Principal Curve Algorithm With Applications To Handwritten Digits And Pharmacologic Colon Imaging

2020 ◽  
Author(s):  
Huan Chen ◽  
Ethel Weld ◽  
Craig Hendrix ◽  
Brian Caffo
Keyword(s):  
Single Photon ◽  
Antiviral Agents ◽  
Photon Emission ◽  
Three Dimensional ◽  
Expectation Maximization Algorithm ◽  
Principal Component ◽  
Emission Computed Tomography ◽  
Mixture Regression ◽  
Principal Curve ◽  
Computed Tomography Images

The classical Principal Curve algorithm was developed as a nonlinear version of principal component analysis to model curves. However, existing principal curve algorithms with classical penalties, such as smoothness or ridge penalties, lack the ability to deal with complex curve shapes. In this manuscript, we introduce a robust and stable length penalty which solves issues of unnecessary curve complexity, such as the self-looping, that arise widely in principal curve algorithms. A novel probabilistic mixture regression model is formulated. A modified penalized EM(Expectation Maximization) Algorithm was applied to the model to obtain the penalized MLE. Two applications of the algorithm were performed. In the first, the algorithm was applied to the MNIST dataset of handwritten digits to find the centerline, not unlike defining a TrueType font. We demonstrate that the centerline can be recovered with this algorithm. In the second application, the algorithm was applied to construct a three dimensional centerline through single photon emission computed tomography images of the colon arising from the study of pre-exposure prophylaxis for HIV. The centerline in this application is crucial for understanding the distribution of the antiviral agents in the colon for HIV prevention. The new algorithms improves on previous applications of principal curves to this data.

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