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Attention Score in Context
| Title |
Bayesian Tractography Using Geometric Shape Priors
|
|---|---|
| Published in |
Frontiers in Neuroscience, September 2017
|
| DOI | 10.3389/fnins.2017.00483 |
| Pubmed ID | |
| Authors |
Xiaoming Dong, Zhengwu Zhang, Anuj Srivastava |
| Abstract |
The problem of estimating neuronal fiber tracts connecting different brain regions is important for various types of brain studies, including understanding brain functionality and diagnosing cognitive impairments. The popular techniques for tractography are mostly sequential-tracts are grown sequentially following principal directions of local water diffusion profiles. Despite several advancements on this basic idea, the solutions easily get stuck in local solutions, and can't incorporate global shape information. We present a global approach where fiber tracts between regions of interest are initialized and updated via deformations based on gradients of a posterior energy. This energy has contributions from diffusion data, global shape models, and roughness penalty. The resulting tracts are relatively immune to issues such as tensor noise and fiber crossings, and achieve more interpretable tractography results. We demonstrate this framework using both simulated and real dMRI and HARDI data. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 21% |
| Student > Master | 2 | 14% |
| Professor | 1 | 7% |
| Student > Bachelor | 1 | 7% |
| Lecturer > Senior Lecturer | 1 | 7% |
| Other | 2 | 14% |
| Unknown | 4 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 3 | 21% |
| Engineering | 3 | 21% |
| Psychology | 1 | 7% |
| Medicine and Dentistry | 1 | 7% |
| Neuroscience | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 5 | 36% |
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 12 September 2017.
All research outputs
#19,951,180
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#8,671
of 11,542 outputs
Outputs of similar age
#236,011
of 323,159 outputs
Outputs of similar age from Frontiers in Neuroscience
#133
of 159 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 18th percentile – i.e., 18% of other outputs scored the same or lower than it.
So far Altmetric has tracked 11,542 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.0. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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We're also able to compare this research output to 159 others from the same source and published within six weeks on either side of this one. This one is in the 9th percentile – i.e., 9% of its contemporaries scored the same or lower than it.