AI Article Synopsis
- The study compares Bayesian estimation algorithms with singular value decomposition (SVD) in analyzing CT perfusion (CTP) data from patients with acute cerebral infarction.
- CTP data from 13 patients were used to assess visual clarity of ischemic areas and calculate ratios between healthy and affected brain regions using both methods.
- Results indicated that Bayesian estimation outperformed SVD, particularly in measuring cerebral blood flow (CBF) and mean transit time (MTT), making it more useful for evaluating acute stroke patients.
Article Abstract
Purpose: There are various analysis methods for CT perfusion (CTP). Although the advantages of Bayesian estimation algorithms have been newly suggested, comparisons with other analysis methods on clinical data are still limited. In this study, we compared the Bayesian estimation method with the singular value decomposition (SVD) method in the evaluation of patients with acute cerebral infarction and examined its usefulness.
Methods: CTP data from 13 patients with acute stroke were analyzed using the SVD and Bayesian estimation methods implemented in Vitrea. Evaluation of visual clarity of the ischemic area and quantitative values of the healthy side-affected side ratio using the mean values of the left and right region of interest (ROI) on the images were compared using the SVD and Bayesian estimation methods.
Results: In visual evaluation, there were significant differences in CBV in four cases, and in CBF, MTT, and TTP in many cases. The healthy side-affected side ratio of the SVD and Bayesian estimation methods were as follows: CBF 1.19, 1.84; CBV 1.09, 1.02; MTT 1.12, 1.79; and TTP 1.48, 1.19. For CBF and MTT, the Bayesian estimation method had a larger ratio of the healthy side to the affected side, and for TTP, the SVD method had a larger ratio of the test side to the affected side.
Conclusion: We suggest that the Bayesian estimation method is more useful than the SVD method for assessing CBF and MTT in CTP analysis of patients with acute stroke.
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| http://dx.doi.org/10.6009/jjrt.2023-1301 | DOI Listing |
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