SWI and CTP fusion model based on sparse representation method to predict cerebral infarction trend.

ABSTRACT:

Objective

SWI image signal is related to venous reflux disorder and perfusion defect. Computed tomography perfusion (CTP) contains perfusion information in space and time. There is a complementary basis between them to affect the prognosis of cerebral infarction.

Methods

Sixty-six patients included in the retrospective study were designated as the training set. Effective perfusion indicator features and imaging radiomic features of the peri-infarction area on Susceptibility weighted imaging (SWI) and CTP modality images were extracted from each case. Thirty-three patients from the prospectively included group were designated as the test set of the machine learning model based on a sparse representation method. The predicted results were compared with the DWI results of the p

SUBMITTER: Wu G 

PROVIDER: S-EPMC11222412 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature