| Title |
A Spatial Registration Toolbox for Structural MR Imaging of the Aging Brain
|
|---|---|
| Published in |
Neuroinformatics, January 2018
|
| DOI | 10.1007/s12021-018-9355-3 |
| Pubmed ID | |
| Authors |
Marco Ganzetti, Quanying Liu, Dante Mantini, Alzheimer’s Disease Neuroimaging Initiative |
| Abstract |
During aging the brain undergoes a series of structural changes, in size, shape as well as tissue composition. In particular, cortical atrophy and ventricular enlargement are often present in the brain of elderly individuals. This poses serious challenges in the spatial registration of structural MR images. In this study, we addressed this open issue by proposing an enhanced framework for MR registration and segmentation. Our solution was compared with other approaches based on the tools available in SPM12, a widely used software package. Performance of the different methods was assessed on 229 T1-weighted images collected in healthy individuals, with age ranging between 55 and 90 years old. Our method showed a consistent improvement as compared to other solutions, especially for subjects with enlarged lateral ventricles. It also provided a superior inter-subject alignment in cortical regions, with the most marked improvement in the frontal lobe. We conclude that our method is a valid alternative to standard approaches based on SPM12, and is particularly suitable for the processing of structural MR images of brains with cortical atrophy and ventricular enlargement. The method is integrated in our software toolbox MRTool, which is freely available to the scientific community. |
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 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 28 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 21% |
| Student > Ph. D. Student | 5 | 18% |
| Student > Master | 3 | 11% |
| Lecturer | 2 | 7% |
| Student > Doctoral Student | 2 | 7% |
| Other | 2 | 7% |
| Unknown | 8 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Neuroscience | 7 | 25% |
| Medicine and Dentistry | 3 | 11% |
| Engineering | 2 | 7% |
| Physics and Astronomy | 1 | 4% |
| Computer Science | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 13 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 04 August 2021.
All research outputs
#14,964,325
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Outputs from Neuroinformatics
#234
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Outputs of similar age
#256,189
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Outputs of similar age from Neuroinformatics
#9
of 13 outputs
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