scholarly journals MR-PET head motion correction based on co-registration of multicontrast MR images

2019 ◽  
Author(s):  
Zhaolin Chen ◽  
Francesco Sforazzini ◽  
Jakub Baran ◽  
Thomas Close ◽  
Nadim Jon Shah ◽  
...  
Keyword(s):  
Motion Correction ◽  
Head Motion ◽  
Mr Images

scholarly journals MR-PET head motion correction based on co-registration of multi-contrast MR images

2018 ◽  
Author(s):  
Zhaolin Chen ◽  
Francesco Sforazzini ◽  
Jakub Baran ◽  
Thomas Close ◽  
N. Jon Shah ◽  
...  
Keyword(s):  
Motion Correction ◽  
Estimation Method ◽  
Correction Method ◽  
Head Motion ◽  
Mr Images ◽  
Image Sharpness ◽  
Positron Emission ◽  
Quantitative Accuracy ◽  
Slow Infusion ◽  
Pet Image Reconstruction

AbstractHead motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous Magnetic Resonance-Positron Emission Tomography (MR-PET) makes it possible to estimate head motion information from high-resolution MR images and then correct motion artefacts in PET images. In this paper, we introduce a fully automated PET motion correction method, MR-guided MAF, based on the co-registration of multi-contrast MR images. The performance of the MR-guided MAF method was evaluated using MR-PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18-F]FDG). Compared with conventional methods, MR guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR-PET scanners. The fully automated motion estimation method has been implemented as a publicly available web-service.

scholarly journals The efficacy of different preprocessing steps in reducing motion-related confounds in diffusion MRI connectomics

2020 ◽  
Author(s):  
Stuart Oldham ◽  
Aurina Arnatkevic̆iūtė ◽  
Robert E. Smith ◽  
Jeggan Tiego ◽  
Mark A. Bellgrove ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Motion Correction ◽  
Healthy Adult ◽  
Structural Connectivity ◽  
Head Motion ◽  
List Type ◽  
Edge Weighting ◽  
Quantitative Metrics ◽  
The Impact ◽  
Connectivity Strength

AbstractHead motion is a major confounding factor in neuroimaging studies. While numerous studies have investigated how motion impacts estimates of functional connectivity, the effects of motion on structural connectivity measured using diffusion MRI have not received the same level of attention, despite the fact that, like functional MRI, diffusion MRI relies on elaborate preprocessing pipelines that require multiple choices at each step. Here, we report a comprehensive analysis of how these choices influence motion-related contamination of structural connectivity estimates. Using a healthy adult sample (N = 252), we evaluated 240 different preprocessing pipelines, devised using plausible combinations of different choices related to explicit head motion correction, tractography propagation algorithms, track seeding methods, track termination constraints, quantitative metrics derived for each connectome edge, and parcellations. We found that an approach to motion correction that includes outlier replacement and within-slice volume correction led to a dramatic reduction in cross-subject correlations between head motion and structural connectivity strength, and that motion contamination is more severe when quantifying connectivity strength using mean tract fractional anisotropy rather than streamline count. We also show that the choice of preprocessing strategy can significantly influence subsequent inferences about network organization, with the location of network hubs varying considerably depending on the specific preprocessing steps applied. Our findings indicate that the impact of motion on structural connectivity can be successfully mitigated using recent motion-correction algorithms that include outlier replacement and within-slice motion correction.HighlightsWe assess how motion affects structural connectivity in 240 preprocessing pipelinesMotion contamination of structural connectivity depends on preprocessing choicesAdvanced motion correction tools reduce motion confoundsFA edge weighting is more susceptible to motion effects than streamline count

The feasibility of head motion tracking in helical CT: A step toward motion correction

2013 ◽  
Vol 40 (4) ◽  
pp. 041903 ◽  
Author(s):  
Jung-Ha Kim ◽  
Johan Nuyts ◽  
Zdenka Kuncic ◽  
Roger Fulton
Keyword(s):  
Motion Correction ◽  
Motion Tracking ◽  
Helical Ct ◽  
Head Motion

scholarly journals Comparison of prospective head motion correction with NMR field probes and an optical tracking system

2018 ◽  
Vol 81 (1) ◽  
pp. 719-729 ◽  
Author(s):  
Martin Eschelbach ◽  
Ali Aghaeifar ◽  
Jonas Bause ◽  
Jonas Handwerker ◽  
Jens Anders ◽  
...  
Keyword(s):  
Motion Correction ◽  
Tracking System ◽  
Optical Tracking ◽  
Head Motion ◽  
Optical Tracking System

Automatic motion correction of clinical shoulder MR images

1999 ◽  
Author(s):  
Armando Manduca ◽  
Kiaran P. McGee ◽  
Edward B. Welch ◽  
Joel P. Felmlee ◽  
Richard L. Ehman
Keyword(s):  
Motion Correction ◽  
Mr Images

scholarly journals Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion

NeuroImage ◽  
2020 ◽  
Vol 212 ◽  
pp. 116594
Author(s):  
Danilo Maziero ◽  
Carlo Rondinoni ◽  
Theo Marins ◽  
Victor Andrew Stenger ◽  
Thomas Ernst
Keyword(s):  
Resting State ◽  
Motion Correction ◽  
Head Motion ◽  
State Data ◽  
Large Head
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