Background: Although black-blood MRI (BB-MRI) can identify plaques in the cervical carotid arteries, this modality has not been applied in intracranial arteries. We imaged the lumina and walls of stenotic middle cerebral arteries (MCAs) in symptomatic and asymptomatic patients using high-resolution BB-MRI, in order to characterize vulnerable plaques and to determine the diagnostic accuracy of BB-MRI in MCA stenosis. Methods: Multicontrast (T1, T2 and proton density)-weighted BB-MRIs were acquired in 15 patients with MCA stenosis and in 2 volunteers. Each MCA was classified into one of three groups based on MR angiographic findings and symptoms: normal, symptomatic stenosis, or asymptomatic stenosis. The plaque signal intensity was interpreted and the total wall thickness was measured at the most stenotic segment. These values were then compared between asymptomatic and symptomatic MCAs using t test. For assessment of lumen imaging, the MCA stenosis graded on BB-MR images was compared with that graded on conventional angiography (digital subtraction angiography). Results: Twenty-eight MCAs were evaluated (normal MCAs: 12, symptomatic stenoses: 7, and asymptomatic stenoses: 9). T1- and/or T2-hyperintense foci were demonstrated more frequently within the plaques of symptomatic stenoses than within the plaques of asymptomatic stenoses (57.1 vs. 22%). The total wall thickness in the symptomatic stenoses was significantly higher than that seen in the asymptomatic stenoses. The stenosis grade for the BB-MRI was significantly correlated with the digital subtraction angiography grade. Conclusion: High-resolution, multicontrast-weighted BB-MRI has the potential to characterize atherosclerotic plaques in the MCA and may be a useful modality for evaluating the degree of stenosis.
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AbstractAccording to the introduction of a dynamic operating mode in ground-coupled heat pump systems, a short time analysis within and around borehole heat exchangers is required in the modern geothermal system simulation. A numerical modelling could be a proper answer for this challenge. However, the numerical model is time consuming and necessitates a large memory particularly in such large systems. Therefore, the state model size reduction technique has been applied in this paper with various numerical techniques particularly in the finite elements method. As a result, the reduced model developed is: a) relevant with a validation using a traditional analytical model (using 100% modes) and b) efficient in calculation time, only using 6% modes and consequently reducing time consumption up to 95%.
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