MR-guided PET motion correction in LOR space using generic projection data for image reconstruction with PRESTO

2013 ◽  
Vol 702 ◽  
pp. 64-66 ◽  
Author(s):  
J. Scheins ◽  
M. Ullisch ◽  
L. Tellmann ◽  
C. Weirich ◽  
E. Rota Kops ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Motion Correction ◽  
Projection Data ◽  
Generic Projection ◽  
Pet Motion Correction

Simultaneous respiratory motion correction and image reconstruction in 4D‐multi pinhole small animal SPECT

2019 ◽  
Vol 46 (11) ◽  
pp. 5047-5054 ◽  
Author(s):  
Maryam Naseri ◽  
Hossein Rajabi ◽  
Jing Wang ◽  
Mehrshad Abbasi ◽  
Faraz Kalantari
Keyword(s):  
Image Reconstruction ◽  
Motion Correction ◽  
Respiratory Motion ◽  
Small Animal ◽  
Respiratory Motion Correction

scholarly journals Implementation and validation of time-of-flight PET image reconstruction module for listmode and sinogram projection data in the STIR library

2019 ◽  
Vol 64 (3) ◽  
pp. 035004 ◽  
Author(s):  
Nikos Efthimiou ◽  
Elise Emond ◽  
Palak Wadhwa ◽  
Christopher Cawthorne ◽  
Charalampos Tsoumpas ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Time Of Flight ◽  
Projection Data ◽  
Pet Image Reconstruction

scholarly journals PET motion correction in context of integrated PET/MR: Current techniques, limitations, and future projections

2017 ◽  
Vol 44 (12) ◽  
pp. e430-e445 ◽  
Author(s):  
Ashley Gillman ◽  
Jye Smith ◽  
Paul Thomas ◽  
Stephen Rose ◽  
Nicholas Dowson
Keyword(s):  
Motion Correction ◽  
Future Projections ◽  
Pet Motion Correction

Ultra-limited-angle CT image reconstruction algorithm based on reweighting and edge-preserving

2022 ◽  
pp. 1-13
Author(s):  
Lei Shi ◽  
Gangrong Qu ◽  
Yunsong Zhao
Keyword(s):  
Image Reconstruction ◽  
Condition Number ◽  
Reconstruction Algorithm ◽  
Projection Data ◽  
Ct Image ◽  
Reconstruction Algorithms ◽  
Edge Preserving ◽  
Image Reconstruction Algorithm ◽  
Ct Image Reconstruction ◽  
Limited Angle

BACKGROUND: Ultra-limited-angle image reconstruction problem with a limited-angle scanning range less than or equal to π 2 is severely ill-posed. Due to the considerably large condition number of a linear system for image reconstruction, it is extremely challenging to generate a valid reconstructed image by traditional iterative reconstruction algorithms. OBJECTIVE: To develop and test a valid ultra-limited-angle CT image reconstruction algorithm. METHODS: We propose a new optimized reconstruction model and Reweighted Alternating Edge-preserving Diffusion and Smoothing algorithm in which a reweighted method of improving the condition number is incorporated into the idea of AEDS image reconstruction algorithm. The AEDS algorithm utilizes the property of image sparsity to improve partially the results. In experiments, the different algorithms (the Pre-Landweber, AEDS algorithms and our algorithm) are used to reconstruct the Shepp-Logan phantom from the simulated projection data with noises and the flat object with a large ratio between length and width from the real projection data. PSNR and SSIM are used as the quantitative indices to evaluate quality of reconstructed images. RESULTS: Experiment results showed that for simulated projection data, our algorithm improves PSNR and SSIM from 22.46db to 39.38db and from 0.71 to 0.96, respectively. For real projection data, our algorithm yields the highest PSNR and SSIM of 30.89db and 0.88, which obtains a valid reconstructed result. CONCLUSIONS: Our algorithm successfully combines the merits of several image processing and reconstruction algorithms. Thus, our new algorithm outperforms significantly other two algorithms and is valid for ultra-limited-angle CT image reconstruction.

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