SR x‐ray fluorescence imaging by image reconstruction technique

1989 ◽  
Vol 60 (7) ◽  
pp. 2458-2461 ◽  
Author(s):  
Atsuo Iida ◽  
Mamoru Takahashi ◽  
Kenji Sakurai ◽  
Yohichi Gohshi
Author(s):  
Golshan Mahmoudi ◽  
Hossein Ghadiri

This article is an Editorial and does not include an Abstract.


2015 ◽  
Vol 8 (3) ◽  
pp. 161
Author(s):  
Samuel Gideon

This research was conducted as a learning alternatives for study of CT (computed tomograpghy) imaging using image reconstruction technique which are inversion matrix, back projection and filtered back projection. CT imaging can produce images of objects that do not overlap. Objects more easily distinguishable although given the relatively low contrast. The image is generated on CT imaging is the result of reconstruction of the original object. Matlab allows us to create and write imaging algorithms easily, easy to undersand and gives applied and exciting other imaging features. In this study, an example cross-sectional image recon-struction performed on the body of prostate tumors using. With these methods, medical prac-titioner (such as oncology clinician, radiographer and medical physicist) allows to simulate the reconstruction of CT images which almost resembles the actual CT visualization techniques.Keywords : computed tomography (CT), image reconstruction, Matlab


RSC Advances ◽  
2015 ◽  
Vol 5 (17) ◽  
pp. 13175-13183 ◽  
Author(s):  
Shilpa Dilipkumar ◽  
Ravi Manjithaya ◽  
Partha Pratim Mondal

We have developed a real-time imaging method for two-color widefield fluorescence microscopy using a combined approach that integrates multi-spectral imaging and Bayesian image reconstruction technique.


2011 ◽  
Vol 2011 ◽  
pp. 1-7
Author(s):  
Hengyong Yu ◽  
Changguo Ji ◽  
Ge Wang

To maximize the time-integrated X-ray flux from multiple X-ray sources and shorten the data acquisition process, a promising way is to allow overlapped projections from multiple sources being simultaneously on without involving the source multiplexing technology. The most challenging task in this configuration is to perform image reconstruction effectively and efficiently from overlapped projections. Inspired by the single-source simultaneous algebraic reconstruction technique (SART), we hereby develop a multisource SART-type reconstruction algorithm regularized by a sparsity-oriented constraint in the soft-threshold filtering framework to reconstruct images from overlapped projections. Our numerical simulation results verify the correctness of the proposed algorithm and demonstrate the advantage of image reconstruction from overlapped projections.


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