The Experts below are selected from a list of 5310 Experts worldwide ranked by ideXlab platform
Jonathan R Polimeni - One of the best experts on this subject based on the ideXlab platform.
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intracortical smoothing of small voxel fmri data can provide increased detection power without spatial resolution losses compared to conventional large voxel fmri data
NeuroImage, 2019Co-Authors: Anna I Blazejewska, Bruce Fischl, Lawrence L Wald, Jonathan R PolimeniAbstract:Abstract Continued improvement in MRI acquisition technology has made functional MRI (fMRI) with small isotropic voxel sizes down to 1 mm and below more commonly available. Although many conventional fMRI studies seek to investigate regional patterns of cortical activation for which conventional voxel sizes of 3 mm and larger provide sufficient spatial resolution, smaller voxels can help avoid contamination from adjacent white matter (WM) and cerebrospinal fluid (CSF), and thereby increase the specificity of fMRI to signal changes within the gray matter. Unfortunately, temporal signal-to-noise ratio (tSNR), a metric of fMRI sensitivity, is reduced in high-resolution acquisitions, which offsets the benefits of small voxels. Here we introduce a framework that combines small, isotropic fMRI voxels acquired at 7 T field strength with a novel anatomically-informed, surface mesh-navigated spatial smoothing that can provide both higher detection power and higher resolution than conventional voxel sizes. Our smoothing approach uses a family of intracortical surface meshes and allows for Kernels of various shapes and sizes, including curved 3D Kernels that adapt to and track the cortical folding pattern. Our goal is to restrict smoothing to the cortical gray matter ribbon and avoid noise contamination from CSF and signal dilution from WM via partial volume effects. We found that the intracortical Kernel that maximizes tSNR does not maximize percent signal change (ΔS/S), and therefore the Kernel Configuration that optimizes detection power cannot be determined from tSNR considerations alone. However, several Kernel Configurations provided a favorable balance between boosting tSNR and ΔS/S, and allowed a 1.1-mm isotropic fMRI acquisition to have higher performance after smoothing (in terms of both detection power and spatial resolution) compared to an unsmoothed 3.0-mm isotropic fMRI acquisition. Overall, the results of this study support the strategy of acquiring voxels smaller than the cortical thickness, even for studies not requiring high spatial resolution, and smoothing them down within the cortical ribbon with a Kernel of an appropriate shape to achieve the best performance—thus decoupling the choice of fMRI voxel size from the spatial resolution requirements of the particular study. The improvement of this new intracortical smoothing approach over conventional surface-based smoothing is expected to be modest for conventional resolutions, however the improvement is expected to increase with higher resolutions. This framework can also be applied to anatomically-informed intracortical smoothing of higher-resolution data (e.g. along columns and layers) in studies with prior information about the spatial structure of activation.
Lawrence L Wald - One of the best experts on this subject based on the ideXlab platform.
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intracortical smoothing of small voxel fmri data can provide increased detection power without spatial resolution losses compared to conventional large voxel fmri data
NeuroImage, 2019Co-Authors: Anna I Blazejewska, Bruce Fischl, Lawrence L Wald, Jonathan R PolimeniAbstract:Abstract Continued improvement in MRI acquisition technology has made functional MRI (fMRI) with small isotropic voxel sizes down to 1 mm and below more commonly available. Although many conventional fMRI studies seek to investigate regional patterns of cortical activation for which conventional voxel sizes of 3 mm and larger provide sufficient spatial resolution, smaller voxels can help avoid contamination from adjacent white matter (WM) and cerebrospinal fluid (CSF), and thereby increase the specificity of fMRI to signal changes within the gray matter. Unfortunately, temporal signal-to-noise ratio (tSNR), a metric of fMRI sensitivity, is reduced in high-resolution acquisitions, which offsets the benefits of small voxels. Here we introduce a framework that combines small, isotropic fMRI voxels acquired at 7 T field strength with a novel anatomically-informed, surface mesh-navigated spatial smoothing that can provide both higher detection power and higher resolution than conventional voxel sizes. Our smoothing approach uses a family of intracortical surface meshes and allows for Kernels of various shapes and sizes, including curved 3D Kernels that adapt to and track the cortical folding pattern. Our goal is to restrict smoothing to the cortical gray matter ribbon and avoid noise contamination from CSF and signal dilution from WM via partial volume effects. We found that the intracortical Kernel that maximizes tSNR does not maximize percent signal change (ΔS/S), and therefore the Kernel Configuration that optimizes detection power cannot be determined from tSNR considerations alone. However, several Kernel Configurations provided a favorable balance between boosting tSNR and ΔS/S, and allowed a 1.1-mm isotropic fMRI acquisition to have higher performance after smoothing (in terms of both detection power and spatial resolution) compared to an unsmoothed 3.0-mm isotropic fMRI acquisition. Overall, the results of this study support the strategy of acquiring voxels smaller than the cortical thickness, even for studies not requiring high spatial resolution, and smoothing them down within the cortical ribbon with a Kernel of an appropriate shape to achieve the best performance—thus decoupling the choice of fMRI voxel size from the spatial resolution requirements of the particular study. The improvement of this new intracortical smoothing approach over conventional surface-based smoothing is expected to be modest for conventional resolutions, however the improvement is expected to increase with higher resolutions. This framework can also be applied to anatomically-informed intracortical smoothing of higher-resolution data (e.g. along columns and layers) in studies with prior information about the spatial structure of activation.
Bruce Fischl - One of the best experts on this subject based on the ideXlab platform.
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intracortical smoothing of small voxel fmri data can provide increased detection power without spatial resolution losses compared to conventional large voxel fmri data
NeuroImage, 2019Co-Authors: Anna I Blazejewska, Bruce Fischl, Lawrence L Wald, Jonathan R PolimeniAbstract:Abstract Continued improvement in MRI acquisition technology has made functional MRI (fMRI) with small isotropic voxel sizes down to 1 mm and below more commonly available. Although many conventional fMRI studies seek to investigate regional patterns of cortical activation for which conventional voxel sizes of 3 mm and larger provide sufficient spatial resolution, smaller voxels can help avoid contamination from adjacent white matter (WM) and cerebrospinal fluid (CSF), and thereby increase the specificity of fMRI to signal changes within the gray matter. Unfortunately, temporal signal-to-noise ratio (tSNR), a metric of fMRI sensitivity, is reduced in high-resolution acquisitions, which offsets the benefits of small voxels. Here we introduce a framework that combines small, isotropic fMRI voxels acquired at 7 T field strength with a novel anatomically-informed, surface mesh-navigated spatial smoothing that can provide both higher detection power and higher resolution than conventional voxel sizes. Our smoothing approach uses a family of intracortical surface meshes and allows for Kernels of various shapes and sizes, including curved 3D Kernels that adapt to and track the cortical folding pattern. Our goal is to restrict smoothing to the cortical gray matter ribbon and avoid noise contamination from CSF and signal dilution from WM via partial volume effects. We found that the intracortical Kernel that maximizes tSNR does not maximize percent signal change (ΔS/S), and therefore the Kernel Configuration that optimizes detection power cannot be determined from tSNR considerations alone. However, several Kernel Configurations provided a favorable balance between boosting tSNR and ΔS/S, and allowed a 1.1-mm isotropic fMRI acquisition to have higher performance after smoothing (in terms of both detection power and spatial resolution) compared to an unsmoothed 3.0-mm isotropic fMRI acquisition. Overall, the results of this study support the strategy of acquiring voxels smaller than the cortical thickness, even for studies not requiring high spatial resolution, and smoothing them down within the cortical ribbon with a Kernel of an appropriate shape to achieve the best performance—thus decoupling the choice of fMRI voxel size from the spatial resolution requirements of the particular study. The improvement of this new intracortical smoothing approach over conventional surface-based smoothing is expected to be modest for conventional resolutions, however the improvement is expected to increase with higher resolutions. This framework can also be applied to anatomically-informed intracortical smoothing of higher-resolution data (e.g. along columns and layers) in studies with prior information about the spatial structure of activation.
Anna I Blazejewska - One of the best experts on this subject based on the ideXlab platform.
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intracortical smoothing of small voxel fmri data can provide increased detection power without spatial resolution losses compared to conventional large voxel fmri data
NeuroImage, 2019Co-Authors: Anna I Blazejewska, Bruce Fischl, Lawrence L Wald, Jonathan R PolimeniAbstract:Abstract Continued improvement in MRI acquisition technology has made functional MRI (fMRI) with small isotropic voxel sizes down to 1 mm and below more commonly available. Although many conventional fMRI studies seek to investigate regional patterns of cortical activation for which conventional voxel sizes of 3 mm and larger provide sufficient spatial resolution, smaller voxels can help avoid contamination from adjacent white matter (WM) and cerebrospinal fluid (CSF), and thereby increase the specificity of fMRI to signal changes within the gray matter. Unfortunately, temporal signal-to-noise ratio (tSNR), a metric of fMRI sensitivity, is reduced in high-resolution acquisitions, which offsets the benefits of small voxels. Here we introduce a framework that combines small, isotropic fMRI voxels acquired at 7 T field strength with a novel anatomically-informed, surface mesh-navigated spatial smoothing that can provide both higher detection power and higher resolution than conventional voxel sizes. Our smoothing approach uses a family of intracortical surface meshes and allows for Kernels of various shapes and sizes, including curved 3D Kernels that adapt to and track the cortical folding pattern. Our goal is to restrict smoothing to the cortical gray matter ribbon and avoid noise contamination from CSF and signal dilution from WM via partial volume effects. We found that the intracortical Kernel that maximizes tSNR does not maximize percent signal change (ΔS/S), and therefore the Kernel Configuration that optimizes detection power cannot be determined from tSNR considerations alone. However, several Kernel Configurations provided a favorable balance between boosting tSNR and ΔS/S, and allowed a 1.1-mm isotropic fMRI acquisition to have higher performance after smoothing (in terms of both detection power and spatial resolution) compared to an unsmoothed 3.0-mm isotropic fMRI acquisition. Overall, the results of this study support the strategy of acquiring voxels smaller than the cortical thickness, even for studies not requiring high spatial resolution, and smoothing them down within the cortical ribbon with a Kernel of an appropriate shape to achieve the best performance—thus decoupling the choice of fMRI voxel size from the spatial resolution requirements of the particular study. The improvement of this new intracortical smoothing approach over conventional surface-based smoothing is expected to be modest for conventional resolutions, however the improvement is expected to increase with higher resolutions. This framework can also be applied to anatomically-informed intracortical smoothing of higher-resolution data (e.g. along columns and layers) in studies with prior information about the spatial structure of activation.
R S Cant - One of the best experts on this subject based on the ideXlab platform.
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effects of strain rate and curvature on the propagation of a spherical flame Kernel in the thin reaction zones regime
Combustion and Flame, 2006Co-Authors: Karl W Jenkins, M Klein, Nilanjan Chakraborty, R S CantAbstract:Abstract Strain rate and curvature effects on the propagation of turbulent premixed flame Kernels have been investigated in the thin-reaction-zones regime using three-dimensional compressible direct numerical simulations (DNS) with single-step Arrhenius chemistry. An initially spherical laminar flame Kernel is allowed to interact with the surrounding turbulent fluid motion to provide a propagating turbulent flame with a strong mean spherical curvature. The statistical behavior of the local displacement speed in response to strain and curvature is investigated in detail. The results demonstrate clearly that the mean curvature inherent to the flame Kernel Configuration has a significant influence on the propagation of the flame. It has been found that the mean density-weighted displacement speed ρ S d in the case of flame Kernels varies significantly over the flame brush and remains different from ρ 0 S L (where ρ 0 is the reactant density and S L is laminar flame speed), unlike statistically planar flames. It is also shown that the magnitude of reaction progress variable gradient | ∇ c | is negatively correlated with curvature in the case of flame Kernels, in contrast to the weak correlation between | ∇ c | and curvature in the case of planar flames. This correlation induces a net positive correlation between the combined reaction and normal diffusion components of displacement speed ( S r + S n ) and curvature in flame Kernels, whereas the previous studies based on statistically planar flames did not observe any appreciable correlation between ( S r + S n ) and curvature.
