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Attention Score in Context
| Title |
Estimation of Bounded and Unbounded Trajectories in Diffusion MRI
|
|---|---|
| Published in |
Frontiers in Neuroscience, March 2016
|
| DOI | 10.3389/fnins.2016.00129 |
| Pubmed ID | |
| Authors |
Lipeng Ning, Carl-Fredrik Westin, Yogesh Rathi |
| Abstract |
Disentangling the tissue microstructural information from the diffusion magnetic resonance imaging (dMRI) measurements is quite important for extracting brain tissue specific measures. The autocorrelation function of diffusing spins is key for understanding the relation between dMRI signals and the acquisition gradient sequences. In this paper, we demonstrate that the autocorrelation of diffusion in restricted or bounded spaces can be well approximated by exponential functions. To this end, we propose to use the multivariate Ornstein-Uhlenbeck (OU) process to model the matrix-valued exponential autocorrelation function of three-dimensional diffusion processes with bounded trajectories. We present detailed analysis on the relation between the model parameters and the time-dependent apparent axon radius and provide a general model for dMRI signals from the frequency domain perspective. For our experimental setup, we model the diffusion signal as a mixture of two compartments that correspond to diffusing spins with bounded and unbounded trajectories, and analyze the corpus-callosum in an ex-vivo data set of a monkey brain. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 6% |
| Unknown | 16 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 29% |
| Student > Doctoral Student | 3 | 18% |
| Student > Ph. D. Student | 3 | 18% |
| Student > Bachelor | 1 | 6% |
| Professor | 1 | 6% |
| Other | 4 | 24% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 4 | 24% |
| Physics and Astronomy | 4 | 24% |
| Engineering | 2 | 12% |
| Business, Management and Accounting | 1 | 6% |
| Unspecified | 1 | 6% |
| Other | 3 | 18% |
| Unknown | 2 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 31 March 2016.
All research outputs
#22,759,802
of 25,374,917 outputs
Outputs from Frontiers in Neuroscience
#10,137
of 11,541 outputs
Outputs of similar age
#272,335
of 315,347 outputs
Outputs of similar age from Frontiers in Neuroscience
#154
of 177 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,541 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 177 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.