To evaluate the significance of circulating tight-junction (TJ) proteins as predictors of hemorrhagic transformation (HT) in ischemic stroke patients.We examined 458 consecutive ischemic stroke patients, 7.2% of whom had clinically evident HT. None of the patients was treated with thrombolytic drugs. Serum levels of standard markers of blood-brain barrier (BBB) breakdown (S100B, neuron-specific enolase), TJ proteins (occludin [OCLN], claudin 5 [CLDN5], zonula occludens 1 [ZO1]), and molecules involved in BBB disintegration (matrix metalloproteinase 9 and vascular endothelial growth factor [VEGF]) were assessed upon admission to the emergency department. A clinical deterioration caused by HT (cdHT) was defined as an increase of ≥4 points in the NIH Stroke Scale score in combination with a visible HT on a CT scan performed immediately after the onset of new neurologic symptoms.Patients with cdHT had higher concentrations of OCLN, S100B, and the CLDN5/ZO1 ratio, and a lower level of VEGF than those without cdHT. CLDN5 levels also correlated with cdHT occurrence when estimated within 3 hours of stroke onset. We also demonstrated correlations between the levels of circulating TJ molecules and the level of S100B, which is a previously established marker of BBB disruption.Analyzing serum levels of TJ proteins, like CLDN5, OCLN, and CLDN5/ZO1 ratio, as well as S100B and VEGF, is an effective way to screen for clinical deterioration caused by HT in ischemic stroke patients, both within and after the IV thrombolysis time window.
During the design and simulation process of MEMS medical devices used in neurosurgery, there is a need to build a brain model with detailed anatomy and physical properties incorporated as a platform to conduct numerical analysis. This paper presents a study on constructing a brain model for simulation of medical device interventions during neurosurgery. A brain atlas was utilized to develop a detailed model consisting of multiple structures. Two types of atlas model were generated employing different mesh types and biomechanical properties suited for various applications. The developed model was able to capture the detailed anatomy of the brain and reflect the application-dependant biomechanical behaviour based on material modelling of brain tissue under surgical intervention.
Cerebrospinal fluid filled ventricular system is an essential part of brain. The volume, shape and size of this ventricular system remain more or less constant and various pathologies directly or indirectly affect them. Morphometric analysis of cerebral ventricular system is important for evaluating changes due to growth, aging, intrinsic and extrinsic pathologies. Previous quantification efforts using ex vivo techniques suffered considerable error due to deformation of slices during sectioning, and numerous other factors. In vivo studies using air or contrast media also introduce volumetric changes in the ventricles thus giving erroneous quantitative information. Imaging of ventricular anatomy avoids these problems and allows repetitive studies following progression of ventricular system changes due to disease or natural processes. We have developed a methodology for automated extraction of ventricular system from MR neuroimages. Once extracted, landmarks are located on the surface of ventricular system automatically. These landmarks are then used for calculation of the ventricular shape, volume and size. A total of 20 brain ventricular systems were analyzed. The morphometric dimensions of the ventricles are presented in this paper. This study forms an initial basis for more advanced work on ventricular segmentation and morphometry.
Outcome prediction is critical in stroke patient management. We propose a novel approach combining imaging with parameters (including history, hospitalization, demographics, clinical and outcome) for a population of patients in the Probabilistic Stroke Atlas (PSA) along with prediction engine. The PSA aggregates multiplicity of data for a population of stroke patients and presents them in image format. The PSA is composed from a series of three-dimensional (3D) image volumes including scans and parameters. A cohort of over 700 ischemic stroke generally treated patients with 176 parameters per patient, and CT scan performed at admission and on day 7 was acquired. Outcome measurements were assessed up to one year after stroke onset. Cases with old infarcts, infarcts in both hemispheres, and hemorrhagic transformations were rejected. This data was post-processed to build the PSA and then the PSA was used for prediction. The infarcts were delineated on CT scans and their 3D surface models constructed and normalized. The PSA was calculated from the normalized 3D infarct models as frequency of stroke occurrence. Similar maps were calculated for the following parameters: Age; Sex; Survival; NIH Stroke Scale (NIHSS); Barthel Index (BI) at 30, 90, 180, 360 days; modified Rankin Scale (mRS) at 7, 30, 90, 180, 360 days; White blood cell count; C-reative protein; Glucose at emergency department; History of hypertension; and History of diabetes. The PSA was used for prediction of mRS and BI for 50 stroke subjects. For a given case to be predicted, the infarct was delineated and analyzed by the PSA mapped on the scan. The predicted values of the parameters from the PSA were compared with the actual values of the parameters measured in up to 1-year neurological follow up. The accuracy was defined as 100*(1-(actual value-predicted value)/actual value)%. The mean prediction accuracy of mRS at (7, 30, 90, 180, 360) days is (89.7, 90.7, 92.1, 87.0, 83.3)% and that for BI at (30, 90, 180, 360) days is (90.0, 95.4, 94.4, 92.2)% respectively. This novel prediction method has high prediction rates. It can be applied to any other parameters. The PSA is dynamic and its power can increase with additional cases.
The accuracy of scan-to-atlas registration highly depends on the number of landmarks and the precision of landmark identification. An extended landmark, cerebellum inferior (CBI), is introduced in this paper. The extracted brain and midsagittal plane are applied to identify the modified Talairach landmarks and the new introduced landmark CBI. The AC-PC plane is firstly determined and then anatomical information is applied to estimate the other landmarks. The proposed method is fully automatic and has been validated on 49 FDG-PET normal and pathological scans qualitatively, and 15 cases quantitatively. The average processing time is about 3 seconds on a standard personal computer.
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