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Automatic flow analysis of digital subtraction angiography using independent component analysis in patients with carotid stenosis

PURPOSE: Current time—density curve analysis of digital subtraction angiography (DSA) provides intravascular flow information but requires manual vasculature selection. We developed an angiographic marker that represents cerebral perfusion by using automatic independent component analysis. MATERIALS...

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Detalles Bibliográficos
Autores principales: Lee, Han-Jui, Hong, Jia-Sheng, Lin, Chung-Jung, Kao, Yi-Hsuan, Chang, Feng-Chi, Luo, Chao-Bao, Chu, Wei-Fa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5614569/
https://www.ncbi.nlm.nih.gov/pubmed/28949999
http://dx.doi.org/10.1371/journal.pone.0185330
Descripción
Sumario:PURPOSE: Current time—density curve analysis of digital subtraction angiography (DSA) provides intravascular flow information but requires manual vasculature selection. We developed an angiographic marker that represents cerebral perfusion by using automatic independent component analysis. MATERIALS AND METHODS: We retrospectively analyzed the data of 44 patients with unilateral carotid stenosis higher than 70% according to North American Symptomatic Carotid Endarterectomy Trial criteria. For all patients, magnetic resonance perfusion (MRP) was performed one day before DSA. Fixed contrast injection protocols and DSA acquisition parameters were used before stenting. The cerebral circulation time (CCT) was defined as the difference in the time to peak between the parietal vein and cavernous internal carotid artery in a lateral angiogram. Both anterior-posterior and lateral DSA views were processed using independent component analysis, and the capillary angiogram was extracted automatically. The full width at half maximum of the time—density curve in the capillary phase in the anterior-posterior and lateral DSA views was defined as the angiographic mean transient time (aMTT; i.e., aMTT(AP) and aMTT(Lat)). The correlations between the degree of stenosis, CCT, aMTT(AP) and aMTT(Lat), and MRP parameters were evaluated. RESULTS: The degree of stenosis showed no correlation with CCT, aMTT(AP), aMTT(Lat), or any MRP parameter. CCT showed a strong correlation with aMTT(AP) (r = 0.67) and aMTT(Lat) (r = 0.72). Among the MRP parameters, CCT showed only a moderate correlation with MTT (r = 0.67) and Tmax (r = 0.40). aMTT(AP) showed a moderate correlation with Tmax (r = 0.42) and a strong correlation with MTT (r = 0.77). aMTT(Lat) also showed similar correlations with Tmax (r = 0.59) and MTT (r = 0.73). CONCLUSION: Apart from vascular anatomy, aMTT estimates brain parenchyma hemodynamics from DSA and is concordant with MRP. This process is completely automatic and provides immediate measurement of quantitative peritherapeutic brain parenchyma changes during stenting.