scholarly journals Prospective real-time head motion correction using inductively coupled wireless NMR probes

2013 ◽  
Vol 72 (4) ◽  
pp. 971-985 ◽  
Author(s):  
Saikat Sengupta ◽  
Sasidhar Tadanki ◽  
John C. Gore ◽  
E. Brian Welch
Keyword(s):  
Real Time ◽  
Motion Correction ◽  
Head Motion ◽  
Inductively Coupled

scholarly journals Robotic real-time translational and rotational head motion correction during frameless stereotactic radiosurgery

2015 ◽  
Vol 42 (6Part1) ◽  
pp. 2757-2763 ◽  
Author(s):  
Xinmin Liu ◽  
Andrew H. Belcher ◽  
Zachary Grelewicz ◽  
Rodney D. Wiersma
Keyword(s):  
Stereotactic Radiosurgery ◽  
Real Time ◽  
Motion Correction ◽  
Head Motion

scholarly journals 3D GABA imaging with real-time motion correction, shim update and reacquisition of adiabatic spiral MRSI

NeuroImage ◽  
2014 ◽  
Vol 103 ◽  
pp. 290-302 ◽  
Author(s):  
Wolfgang Bogner ◽  
Borjan Gagoski ◽  
Aaron T. Hess ◽  
Himanshu Bhat ◽  
M. Dylan Tisdall ◽  
...  
Keyword(s):  
Real Time ◽  
Motion Correction ◽  
Time Motion

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

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