scholarly journals Incoherent artefact correction using PPI

2006 ◽  
Vol 19 (3) ◽  
pp. 362-367
Author(s):  
David Atkinson
Keyword(s):  
2019 ◽  
Vol 48 (8) ◽  
pp. 20190235
Author(s):  
Hugo Gaêta-Araujo ◽  
Nicolly Oliveira-Santos ◽  
Danieli Moura Brasil ◽  
Eduarda Helena Leandro do Nascimento ◽  
Daniela Verardi Madlum ◽  
...  

Objectives: To evaluate the influence of the level of three micro-CT reconstruction tools: beam-hardening correction (BHC), smoothing filter (SF), and ring artefact correction (RAC) on the fractal dimension (FD) analysis of trabecular bone. Methods: Five Wistar rats’ maxillae were individually scanned in a SkyScan 1174 micro-CT device, under the following settings: 50 kV, 800 µA, 10.2 µm voxel size, 0.5 mm Al filter, rotation step 0.5°, two frames average, 180° rotation and scan time of 35 min. The raw images were reconstructed under the standard protocol (SP) recommended by the manufacturer, a protocol without any artefact correction tools (P0) and 35 additional protocols with different combinations of SF, RAC and BHC levels. The same volume of interest was established in all reconstructions for each maxilla and the FD was calculated using the Kolmogorov (box counting) method. One-way ANOVA with Dunnet’s post-hoc test was used to compare the FD of each reconstruction protocol (P0–P35) with the SP (α = 5%). Multiple linear regression verified the dependency of reconstruction tools in FD. Results: Overall, FD values are not dependent on RAC (p = 0.965), but increased significantly when the level of BHC and SF increased (p < 0.001). FD values from protocols with BHC at 45% combined with SF of 2, and BHC at 30% combined with SF of 4 or 6 had no statistical difference compared to SP. Conclusions: BHC and SF tools affect the FD values of micro-CT images of the trabecular bone. Therefore, these reconstruction parameters should be standardized when the FD is analyzed.


NeuroImage ◽  
2011 ◽  
Vol 54 (1) ◽  
pp. 1-3 ◽  
Author(s):  
Sebastian Olbrich ◽  
Johannes Jödicke ◽  
Christian Sander ◽  
Hubertus Himmerich ◽  
Ulrich Hegerl
Keyword(s):  
Eeg Data ◽  

Author(s):  
Robert C. Atwood ◽  
Andrew J. Bodey ◽  
Stephen W. T. Price ◽  
Mark Basham ◽  
Michael Drakopoulos

Tomographic datasets collected at synchrotrons are becoming very large and complex, and, therefore, need to be managed efficiently. Raw images may have high pixel counts, and each pixel can be multidimensional and associated with additional data such as those derived from spectroscopy. In time-resolved studies, hundreds of tomographic datasets can be collected in sequence, yielding terabytes of data. Users of tomographic beamlines are drawn from various scientific disciplines, and many are keen to use tomographic reconstruction software that does not require a deep understanding of reconstruction principles. We have developed Savu , a reconstruction pipeline that enables users to rapidly reconstruct data to consistently create high-quality results. Savu is designed to work in an ‘orthogonal’ fashion, meaning that data can be converted between projection and sinogram space throughout the processing workflow as required. The Savu pipeline is modular and allows processing strategies to be optimized for users' purposes. In addition to the reconstruction algorithms themselves, it can include modules for identification of experimental problems, artefact correction, general image processing and data quality assessment. Savu is open source, open licensed and ‘facility-independent’: it can run on standard cluster infrastructure at any institution.


NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S100 ◽  
Author(s):  
L Kasper ◽  
S Marti ◽  
SJ Vannesjö ◽  
C Hutton ◽  
R Dolan ◽  
...  
Keyword(s):  

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
José L. Ferreira ◽  
Yan Wu ◽  
René M. H. Besseling ◽  
Rolf Lamerichs ◽  
Ronald M. Aarts

Over the past years, coregistered EEG-fMRI has emerged as a powerful tool for neurocognitive research and correlated studies, mainly because of the possibility of integrating the high temporal resolution of the EEG with the high spatial resolution of fMRI. However, additional work remains to be done in order to improve the quality of the EEG signal recorded simultaneously with fMRI data, in particular regarding the occurrence of the gradient artefact. We devised and presented in this paper a novel approach for gradient artefact correction based upon optimised moving-average filtering (OMA). OMA makes use of the iterative application of a moving-average filter, which allows estimation and cancellation of the gradient artefact by integration. Additionally, OMA is capable of performing the attenuation of the periodic artefact activity without accurate information about MRI triggers. By using our proposed approach, it is possible to achieve a better balance than the slice-average subtraction as performed by the established AAS method, regarding EEG signal preservation together with effective suppression of the gradient artefact. Since the stochastic nature of the EEG signal complicates the assessment of EEG preservation after application of the gradient artefact correction, we also propose a simple and effective method to account for it.


2014 ◽  
Vol 18 (7) ◽  
pp. 1132-1142 ◽  
Author(s):  
Pankaj Daga ◽  
Tejas Pendse ◽  
Marc Modat ◽  
Mark White ◽  
Laura Mancini ◽  
...  

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