| Title |
A semi-automated workflow solution for multimodal neuroimaging: application to patients with traumatic brain injury
|
|---|---|
| Published in |
Brain Informatics, December 2015
|
| DOI | 10.1007/s40708-015-0026-y |
| Pubmed ID | |
| Authors |
Koon-Pong Wong, Marvin Bergsneider, Thomas C. Glenn, Vladimir Kepe, Jorge R. Barrio, David A. Hovda, Paul M. Vespa, Sung-Cheng Huang |
| Abstract |
Traumatic brain injury (TBI) is a major cause of mortality and morbidity, placing a significant financial burden on the healthcare system worldwide. Non-invasive neuroimaging technologies have been playing a pivotal role in the study of TBI, providing important information for surgical planning and patient management. Advances in understanding the basic mechanisms and pathophysiology of the brain following TBI are hindered by a lack of reliable image analysis methods for accurate quantitative assessment of TBI-induced structural and pathophysiological changes seen on anatomical and functional images obtained from multiple imaging modalities. Conventional region-of-interest (ROI) analysis based on manual labeling of brain regions is time-consuming and the results could be inconsistent within and among investigators. In this study, we propose a workflow solution framework that combined the use of non-linear spatial normalization of structural brain images and template-based anatomical labeling to automate the ROI analysis process. The proposed workflow solution is applied to dynamic PET scanning with (15)O-water (0-10 min) and (18)F-FDDNP (0-6 min) for measuring cerebral blood flow in patients with TBI. |
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 % |
|---|---|---|
| Unknown | 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 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 5% |
| Unknown | 21 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 6 | 27% |
| Researcher | 5 | 23% |
| Student > Doctoral Student | 3 | 14% |
| Professor | 2 | 9% |
| Student > Postgraduate | 2 | 9% |
| Other | 3 | 14% |
| Unknown | 1 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 5 | 23% |
| Medicine and Dentistry | 3 | 14% |
| Computer Science | 2 | 9% |
| Biochemistry, Genetics and Molecular Biology | 2 | 9% |
| Neuroscience | 2 | 9% |
| Other | 5 | 23% |
| Unknown | 3 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 21 April 2020.
All research outputs
#6,963,891
of 22,835,198 outputs
Outputs from Brain Informatics
#37
of 103 outputs
Outputs of similar age
#109,695
of 387,566 outputs
Outputs of similar age from Brain Informatics
#4
of 5 outputs
Altmetric has tracked 22,835,198 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 103 research outputs from this source. They receive a mean Attention Score of 4.3. This one has gotten more attention than average, scoring higher than 63% of its peers.
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We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one.