Clinicoradiological Profile of Superficial Middle Cerebral Vein Thrombosis

Mathukumalli, Neeharika L; Dandu, Ravivarma; Kanikannan, Meena A; Kaul, Subhash

doi:10.4103/0028-3886.280640

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…A nonparametric variable model based on histogram analysis is proposed, and its performance is compared with the PD and GD models. The diagnostic sensitivity, specificity, and overall performance of the proposed algorithm are significantly higher than those of the GD and PD algorithms, and the difference is statistically significant ( P < 0.05), which is similar to the results obtained by Mathukumalli et al [ 21 ]. It shows that the nonparametric variable model based on the histogram analysis designed in this study has a good segmentation effect on MRI images and can effectively improve the quality of the original image.…”

Section: Discussionsupporting

confidence: 88%

Traumatic Brain Magnetic Resonance Imaging Feature Extraction Based on Variable Model Algorithm in Stroke Examination

Yang

et al. 2022

Contrast Media & Molecular Imaging

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The purpose of this study was to explore the diagnostic value of different sequence scanning of nonparametric variable model-based cranial magnetic resonance imaging (MRI) for ischemic stroke. A histogram analysis-based nonparametric variable model was proposed first, which was compared with the parametric deformation (PD) model and geometric deformation (GD) model. Then, 116 patients with acute ischemic stroke were selected as the research subjects. Routine MRI (T2WI, T1WI, FLAIR, DWI, SWI, and 3D TOF MRA) and MR SCALE-PWI were performed. The results showed that the nonparametric variable model algorithm was relatively complete in the actual segmentation results of MRI images, and the display clarity of lesions was better than PD and GD algorithms. The diagnostic sensitivity, specificity, and overall performance of the variable model algorithm were significantly higher than those of the other two algorithms ( P < 0.05 ). According to ROC curve analysis, the AUC areas of DWI, SWI, 3D TOF MRA, and MR SCALE-PWI for the diagnosis of ischemic penumbra were 0.793, 0.825, 0.871, and 0.933, respectively. In summary, the segmentation results of MRI images by the nonparametric variable model based on histogram analysis were relatively complete, and the clarity of lesions was better than that of the traditional model. MRI images can effectively identify the occurrence of ischemic stroke. Moreover, MR SCALE-PWI had a good early identification effect on ischemic penumbra, which can reduce unnecessary treatment for patients.

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Section: Discussionsupporting

confidence: 88%

Traumatic Brain Magnetic Resonance Imaging Feature Extraction Based on Variable Model Algorithm in Stroke Examination

Yang

et al. 2022

Contrast Media & Molecular Imaging

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“…The T2‐weighted echo‐planar susceptibility‐weighted images (T2*SWI) and gradient‐echo imaging (T2*GRE) are considered the most sensitive MRI sequences for diagnosing ICVT, with a sensitivity of 95% 1,2,5,35 . Compared with the T1 sequence, the T2*SWI sequence improves the detection of ICVT due to deoxyhemoglobin's susceptibility effect on the thrombosed cortical vein.…”

Section: Radiological Findingsmentioning

confidence: 99%

“…1,26,31 The T2-weighted echo-planar susceptibility-weighted images (T2*SWI) and gradient-echo imaging (T2*GRE) are considered the most sensitive MRI sequences for diagnosing ICVT, with a sensitivity of 95%. 1,2,5,35 Compared with the T1 sequence, the T2*SWI sequence improves the detection of ICVT due to deoxyhemoglobin's susceptibility effect on the thrombosed cortical vein. A clearly demarcated and curvilinear hypointensity in the T2*GRE and T2*SWI sequences with an associated "blooming" effect, as shown in Figure 5A, is noted in thrombosed superficial cortical veins.…”

Section: R Adiolog Ic Al Finding Smentioning

confidence: 99%