Chapter title |
Simulating neurodegeneration through longitudinal population analysis of structural and diffusion weighted MRI data.
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Chapter number | 8 |
Book title |
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014
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Published in |
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, October 2014
|
DOI | 10.1007/978-3-319-10443-0_8 |
Pubmed ID | |
Book ISBNs |
978-3-31-910442-3, 978-3-31-910443-0
|
Authors |
Modat M, Simpson IJ, Cardoso MJ, Cash DM, Toussaint N, Fox NC, Ourselin S, Modat, Marc, Simpson, Ivor J. A., Cardoso, Manual Jorge, Cash, David M., Toussaint, Nicolas, Fox, Nick C., Ourselin, Sébastien, Marc Modat, Ivor J. A. Simpson, Manual Jorge Cardoso, David M. Cash, Nicolas Toussaint, Nick C. Fox, Sébastien Ourselin |
Abstract |
Neuroimaging biomarkers play a prominent role for disease diagnosis or tracking neurodegenerative processes. Multiple methods have been proposed by the community to extract robust disease specific markers from various imaging modalities. Evaluating the accuracy and robustness of developed methods is difficult due to the lack of a biologically realistic ground truth. We propose a proof-of-concept method for a patient- and disease-specific brain neurodegeneration simulator. The proposed scheme, based on longitudinal multi-modal data, has been applied to a population of normal controls and patients diagnosed with Alzheimer's disease or frontotemporal dementia. We simulated follow-up images from baseline scans and compared them to real repeat images. Additionally, simulated maps of volume change are generated, which can be compared to maps estimated from real longitudinal data. The results indicate that the proposed simulator reproduces realistic patient-specific patterns of longitudinal brain change for the given populations. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 4% |
Unknown | 44 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 9 | 20% |
Researcher | 9 | 20% |
Other | 5 | 11% |
Student > Master | 5 | 11% |
Student > Postgraduate | 2 | 4% |
Other | 5 | 11% |
Unknown | 11 | 24% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 8 | 17% |
Neuroscience | 7 | 15% |
Medicine and Dentistry | 6 | 13% |
Engineering | 5 | 11% |
Psychology | 2 | 4% |
Other | 5 | 11% |
Unknown | 13 | 28% |