scholarly journals Quantitative Evaluations of Geometrical Distortion Corrections in Cortical Surface‐Based Analysis of High‐Resolution Functional MRI Data at 7T

2020 ◽  
Author(s):  
Tetsuya Yamamoto ◽  
Masaki Fukunaga ◽  
Sho K. Sugawara ◽  
Yuki H. Hamano ◽  
Norihiro Sadato
Keyword(s):  
High Resolution ◽  
Functional Mri ◽  
Cortical Surface ◽  
Geometrical Distortion ◽  
Quantitative Evaluations

scholarly journals Cortical Surface Reconstruction from High-Resolution MR Brain Images

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Sergey Osechinskiy ◽  
Frithjof Kruggel
Keyword(s):  
High Resolution ◽  
Cortical Layer ◽  
Cortical Surface ◽  
Computationally Efficient ◽  
Mr Images ◽  
Brain Images ◽  
Standard Resolution ◽  
Geometric Deformable Model ◽  
Young Subjects ◽  
Mr Brain Images

Reconstruction of the cerebral cortex from magnetic resonance (MR) images is an important step in quantitative analysis of the human brain structure, for example, in sulcal morphometry and in studies of cortical thickness. Existing cortical reconstruction approaches are typically optimized for standard resolution (~1 mm) data and are not directly applicable to higher resolution images. A new PDE-based method is presented for the automated cortical reconstruction that is computationally efficient and scales well with grid resolution, and thus is particularly suitable for high-resolution MR images with submillimeter voxel size. The method uses a mathematical model of a field in an inhomogeneous dielectric. This field mapping, similarly to a Laplacian mapping, has nice laminar properties in the cortical layer, and helps to identify the unresolved boundaries between cortical banks in narrow sulci. The pial cortical surface is reconstructed by advection along the field gradient as a geometric deformable model constrained by topology-preserving level set approach. The method’s performance is illustrated on exvivo images with 0.25–0.35 mm isotropic voxels. The method is further evaluated by cross-comparison with results of the FreeSurfer software on standard resolution data sets from the OASIS database featuring pairs of repeated scans for 20 healthy young subjects.

High resolution functional MRI of subliminal and conscious vision at 7 Tesla

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S156
Author(s):  
V. Schöpf ◽  
F.Ph Fischmeister ◽  
C. Windischberger ◽  
H. Bauer ◽  
E. Moser
Keyword(s):  
High Resolution ◽  
Functional Mri ◽  
7 Tesla

scholarly journals High-resolution functional MRI at 3 T: 3D/2D echo-planar imaging with optimized physiological noise correction

2012 ◽  
Vol 69 (6) ◽  
pp. 1657-1664 ◽  
Author(s):  
Antoine Lutti ◽  
David L. Thomas ◽  
Chloe Hutton ◽  
Nikolaus Weiskopf
Keyword(s):  
High Resolution ◽  
Functional Mri ◽  
Echo Planar Imaging ◽  
Planar Imaging ◽  
Physiological Noise ◽  
Noise Correction ◽  
Echo Planar

scholarly journals High resolution data analysis strategies for mesoscale human functional MRI at 7 and 9.4 T

NeuroImage ◽  
2018 ◽  
Vol 164 ◽  
pp. 48-58 ◽  
Author(s):  
Valentin G. Kemper ◽  
Federico De Martino ◽  
Thomas C. Emmerling ◽  
Essa Yacoub ◽  
Rainer Goebel
Keyword(s):  
Data Analysis ◽  
High Resolution ◽  
Functional Mri ◽  
High Resolution Data ◽  
Analysis Strategies ◽  
Resolution Data

High resolution real-time functional MRI

NeuroImage ◽  
1998 ◽  
Vol 7 (4) ◽  
pp. S568
Author(s):  
James T. Voyvodic
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Functional Mri

scholarly journals Automatic cortical surface reconstruction of high-resolution T1 echo planar imaging data

NeuroImage ◽  
2016 ◽  
Vol 134 ◽  
pp. 338-354 ◽  
Author(s):  
Ville Renvall ◽  
Thomas Witzel ◽  
Lawrence L. Wald ◽  
Jonathan R. Polimeni
Keyword(s):  
High Resolution ◽  
Surface Reconstruction ◽  
Echo Planar Imaging ◽  
Cortical Surface ◽  
Imaging Data ◽  
Planar Imaging ◽  
Echo Planar
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