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Morphometry-Based Longitudinal Neurodegeneration Simulation with MR Imaging
lib:f60397d0484dbcff (v1.0.0)
Authors: Siqi Liu,Sidong Liu,Sonia Pujol,Ron Kikinis,Dagan Feng,Michael Fulham,Weidong Cai
ArXiv: 1508.05683
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1508.05683v1
We present a longitudinal MR simulation framework which simulates the future neurodegenerative progression by outputting the predicted follow-up MR image and the voxel-based morphometry (VBM) map. This framework expects the patients to have at least 2 historical MR images available. The longitudinal and cross-sectional VBM maps are extracted to measure the affinity between the target subject and the template subjects collected for simulation. Then the follow-up simulation is performed by resampling the latest available target MR image with a weighted sum of non-linear transformations derived from the best-matched templates. The leave-one-out strategy was used to compare different simulation methods. Compared to the state-of-the-art voxel-based method, our proposed morphometry-based simulation achieves better accuracy in most cases.
ArXiv: 1508.05683
Document: PDF DOI
Abstract URL: http://arxiv.org/abs/1508.05683v1
We present a longitudinal MR simulation framework which simulates the future neurodegenerative progression by outputting the predicted follow-up MR image and the voxel-based morphometry (VBM) map. This framework expects the patients to have at least 2 historical MR images available. The longitudinal and cross-sectional VBM maps are extracted to measure the affinity between the target subject and the template subjects collected for simulation. Then the follow-up simulation is performed by resampling the latest available target MR image with a weighted sum of non-linear transformations derived from the best-matched templates. The leave-one-out strategy was used to compare different simulation methods. Compared to the state-of-the-art voxel-based method, our proposed morphometry-based simulation achieves better accuracy in most cases.
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