SWI and CTP fusion model based on sparse representation method to predict cerebral infarction trend
Related Experiment Video
Updated: Jun 22, 2025
Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Guoqing Wu1, Hao Wang2, Xiaojun Ma3
1Department of Electronic Engineering, Fudan University, Shanghai, China.
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 patients' 7-10 days review to assess the validity and accuracy of the prediction.
Results:
The AUC of the SWI + CTP integrated model was 0.952, the ACC was 0.909, the SEN was 0.889, and the SPE was 0.933. The prediction performance is the highest. Compared with the value of AUC: the SWI model is 0.874, inferior to the performance of the SWI + CTP model, and the CTP model is 0.715.
Conclusion:
The prediction efficiency of the changing trend of infarction volume is further improved by the correlation between the combination of the two image features.
