Comparison of Susceptibility-Weighted Imaging and Perfusion-Weighted Imaging in the Estimation of the Amount of Reversible Ischemic Tissue (Penumbra)

Sheikhbabaei, Meisam; Gharekhanloo, Farideh; Khazaei, Mojtaba; Pirdehghan, Azar

doi:10.1007/s42399-022-01274-2

Cited by 1 publication

(1 citation statement)

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“…Since stroke patients are caused by the blockage of cerebrovascular and the rehabilitation of patients is affected by the recovery degree of blood flow transmission in brain tissue [15][16][17] , the transmission state of cerebral blood flow can reflect the rehabilitation ability of patients to a certain extent [18] . Previous studies paid more attention to the information calculated from diseased tissue, ignoring the overall state of whole-brain tissue.…”

Section: Introductionmentioning

confidence: 99%

Ischemic stroke outcome prediction based on the whole-brain features extracted from minimum intensity projection of DSC-PWI images by pre-trained Med3D network

Guo,

Yang,

Zeng

et al. 2023

Third International Conference on Computer Graphics, Image, and Virtualization (ICCGIV 2023)

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Accurate stroke outcome prediction is of great significance to making treatment plans and evaluating the rehabilitation state of patients. Previous works paid more attention to the basic information and volume of ischemic tissue for predicting outcomes, ignoring the role of the whole-brain. The purpose of this paper was to prove the value of wholebrain features in outcome prediction. In detail, the pre-trained Med3D model was used to extract whole-brain features from minimum intensity projection (MinIP) of PWI-DSC images, the Least absolute shrinkage and selection operator was used to select outstanding whole-brain features, and ten machine learning models were applied to validate the role of the selected outstanding whole-brain features on predicting outcomes. As the results, when taking ResNet10, ResNet18, ResNet34, and ResNet50 as encoders in the Med3D model, the best AUC of outstanding whole-brain features were 0.88, 0.939, 0.781, and 0.883, and the mean ± std on the ten machine models were 0.756 ± 0.097, 0.766 ± 0.123, 0.714 ± 0.044, and 0.761 ± 0.105, respectively. It can be concluded that the outstanding whole-brain features extracted from the MinIP image can predict good outcomes and poor outcomes for ischemic stroke patients, and the whole-brain features from ResNet18 performed best. The method provided in this study may provide new insight for ischemic stroke research.

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