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Mendeley readers
Attention Score in Context
| Title |
Simulating Longitudinal Brain MRIs with Known Volume Changes and Realistic Variations in Image Intensity
|
|---|---|
| Published in |
Frontiers in Neuroscience, March 2017
|
| DOI | 10.3389/fnins.2017.00132 |
| Pubmed ID | |
| Authors |
Bishesh Khanal, Nicholas Ayache, Xavier Pennec |
| Abstract |
This paper presents a simulator tool that can simulate large databases of visually realistic longitudinal MRIs with known volume changes. The simulator is based on a previously proposed biophysical model of brain deformation due to atrophy in AD. In this work, we propose a novel way of reproducing realistic intensity variation in longitudinal brain MRIs, which is inspired by an approach used for the generation of synthetic cardiac sequence images. This approach combines a deformation field obtained from the biophysical model with a deformation field obtained by a non-rigid registration of two images. The combined deformation field is then used to simulate a new image with specified atrophy from the first image, but with the intensity characteristics of the second image. This allows to generate the realistic variations present in real longitudinal time-series of images, such as the independence of noise between two acquisitions and the potential presence of variable acquisition artifacts. Various options available in the simulator software are briefly explained in this paper. In addition, the software is released as an open-source repository. The availability of the software allows researchers to produce tailored databases of images with ground truth volume changes; we believe this will help developing more robust brain morphometry tools. Additionally, we believe that the scientific community can also use the software to further experiment with the proposed model, and add more complex models of brain deformation and atrophy generation. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 40% |
| Switzerland | 1 | 20% |
| Unknown | 2 | 40% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 30 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 23% |
| Researcher | 5 | 17% |
| Student > Doctoral Student | 3 | 10% |
| Student > Bachelor | 3 | 10% |
| Student > Postgraduate | 3 | 10% |
| Other | 4 | 13% |
| Unknown | 5 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 27% |
| Engineering | 7 | 23% |
| Biochemistry, Genetics and Molecular Biology | 2 | 7% |
| Business, Management and Accounting | 1 | 3% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 3% |
| Other | 2 | 7% |
| Unknown | 9 | 30% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 11 January 2018.
All research outputs
#5,407,105
of 25,382,440 outputs
Outputs from Frontiers in Neuroscience
#4,079
of 11,542 outputs
Outputs of similar age
#88,998
of 322,842 outputs
Outputs of similar age from Frontiers in Neuroscience
#64
of 206 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
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 has gotten more attention than average, scoring higher than 64% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 322,842 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 206 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.