A Dynamic CT Image Reconstruction Method by Inducing Prior Information from PCA Analysis

A prototype of micron-CT for biological research is being developed at Tohoku University. This micron-CT uses a point X-ray source with a spot size of 1μm and an X-ray CCD with 1000×1000 pixels of 8μm×8μm, achieving a spatial resolutions of the order of micro-meter. The event data obtained by the X-ray CCD is statistically poor and the 3 dimensional filtered back projection (3D FBP) algorithm, generally used in image reconstruction of X-ray CT, is not suitable because it is highly sensitive to statistical noise. Hence, we applied the expectation maximization (EM) algorithm for image reconstruction and developed an image reconstruction method using 3D EM algorithm. To confirm the validity of the reconstruction method, we irradiated two hairs inside a micro tube and reconstructed the CT image applying both EM and FBP algorithm on projection data. With 200×200×200 voxels of 4μm×4μm×4μm in the field of view, the computation time was less than 2 mins per iteration on a DELL Precision 650 Workstation 3.2GHz. The resulting EM image showed a better contrast than FBP image, and in the EM reconstructed CT image, we were able to reconstruct the micro tube of 270μm diameter and 45μm wall thickness and to visualize the two hairs inside.

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A MAP regularization super-resolution image reconstruction method based on improved immune algorithm

Information Technology and Industrial Engineering ◽

10.2495/itie130171 ◽

2013 ◽

Author(s):

Hong Lei ◽

Jianwen Han

Keyword(s):

Image Reconstruction ◽

Super Resolution ◽

Immune Algorithm ◽

Reconstruction Method ◽

Resolution Image ◽

Image Reconstruction Method

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Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405615666190126130905 ◽

2020 ◽

Vol 16 (2) ◽

pp. 156-163

Author(s):

Jingwen Wang ◽

Xu Wang ◽

Dan Yang ◽

Kaiyang Wang

Keyword(s):

Inverse Problem ◽

Image Reconstruction ◽

Magnetic Induction ◽

Fourier Coefficients ◽

Signal Reconstruction ◽

Reconstruction Algorithm ◽

Reconstruction Method ◽

Measurement Equation ◽

Image Reconstruction Method ◽

Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

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