发明名称 Systems and methods for modeling and processing functional magnetic resonance image data using full-brain vector auto-regressive model
摘要 Systems and methods for modeling functional magnetic resonance image datasets using a multivariate auto-regressive model which captures temporal dynamics in the data, and creates a reduced representation of the dataset representative of functional connectivity of voxels with respect to brain activity. Raw spatio-temporal data is processed using a multivariate auto-regressive model, wherein coefficients in the model with high weights are retained as indices that best describe the full spatio-temporal data. When there are a relatively small number of temporal samples of the data, sparse regression techniques are used to build the model. The model coefficients are used to perform data processing functions such as indexing, prediction, and classification.
申请公布号 US8861815(B2) 申请公布日期 2014.10.14
申请号 US201113197011 申请日期 2011.08.03
申请人 International Business Machines Corporation 发明人 Cecchi Guillermo Alberto;Garg Rahul;Rao Ravishankar
分类号 G06K9/00;G06K9/62;A61B5/05;G06T7/00;G06F19/00;G06T11/00 主分类号 G06K9/00
代理机构 Ryan, Mason & Lewis, LLP 代理人 Alexanian Vazken;Ryan, Mason & Lewis, LLP
主权项 1. A method to perform an image data processing operation, comprising: obtaining a raw spatio-temporal dataset acquired from scanning a brain of a subject performing a given task; constructing a full spatio-temporal model using the raw spatio-temporal dataset, wherein the full spatio-temporal model represents brain activity that occurs in all regions of the subject's brain in response to the subject performing the given task; selecting model parameters from the full spatio-temporal model which meet or exceed a threshold parameter that defines a level of causal relation between voxels in the acquired dataset; generating a reduced model representation of the full spatio-temporal model using the selected model parameters; generating a vector representing the reduced model, wherein generating a vector representing the reduced model comprises: labeling each voxel with a spatial index Si and a prediction value Pi;concatenating voxels having a common spatial index Si; andgenerating a vector comprising sets of concatenated voxels and a corresponding prediction value for each set of concatenated voxels; and using the vector to perform an image data processing operation, wherein the method is performed by a computer.
地址 Armonk NY US