scholarly journals Non-local averaging in EM: decreasing the required electron dose in crystal image reconstruction without losing spatial resolution

2016 ◽  
pp. 529-530
Author(s):  
Niklas Mevenkamp ◽  
Benjamin Berkels
Keyword(s):  
Image Reconstruction ◽  
Spatial Resolution ◽  
Electron Dose ◽  
Local Averaging ◽  
Non Local

Non‐local means improves total‐variation constrained photoacoustic image reconstruction

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Phaneendra K. Yalavarthy ◽  
Sandeep Kumar Kalva ◽  
Manojit Pramanik ◽  
Jaya Prakash
Keyword(s):  
Image Reconstruction ◽  
Total Variation ◽  
Local Means ◽  
Non Local

Using Deconvolution to Improve PET Spatial Resolution in OSEM Iterative Reconstruction

2007 ◽  
Vol 46 (02) ◽  
pp. 231-235 ◽  
Author(s):  
I. Castiglioni ◽  
G. Russo ◽  
M. Tana ◽  
F. Dell'Acqua ◽  
M. Gilardi ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Spatial Resolution ◽  
Penalty Function ◽  
Iterative Reconstruction ◽  
A Priori ◽  
Image Deconvolution ◽  
A Priori Information ◽  
Novel Approach ◽  
Priori Information ◽  
Pet Image Reconstruction

Summary Objectives : A novel approach to the PET image reconstruction is presented, based on the inclusion of image deconvolution during conventional OSEM reconstruction. Deconvolution is here used to provide a recovered PET image to be included as “a priori" information to guide OSEM toward an improved solution. Methods : Deconvolution was implemented using the Lucy-Richardson (LR) algorithm: Two different deconvolution schemes were tested, modifying the conventional OSEM iterative formulation: 1) We built a regularizing penalty function on the recovered PET image obtained by deconvolution and included i in the OSEM iteration. 2) After each conventional global OSEM iteration, we deconvolved the resulting PET image and used this “recovered" version as the initialization image for the next OSEM iteration. Tests were performed on both simulated and acquired data. Results : Compared to the conventional OSEM, both these strategies, applied to simulated and acquired data, showed an improvement in image spatial resolution with better behavior in the second case. In this way, small lesions, present on data, could be better discriminated in terms of contrast. Conclusions : Application of this approach to both simulated and acquired data suggests its efficacy in obtaining PET images of enhanced quality.

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