一种MRA图像脑血管狭窄程度智能分级评估模型

By calculating the scanning influence and local attenuation coefficient of vascular segments in MRA images and correcting the signal intensity, the influence of vascular geometry and hemodynamic state on signal intensity is resolved, enabling a more accurate assessment of the degree of cerebral vascular stenosis.

CN122199563BActive Publication Date: 2026-07-17SANYA CENT HOSPITAL (THE THIRD PEOPLES HOSPITAL OF HAINAN PROVINCE) +1
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SANYA CENT HOSPITAL (THE THIRD PEOPLES HOSPITAL OF HAINAN PROVINCE)
Filing Date
2026-05-18
Publication Date
2026-07-17

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Abstract

本申请涉及一种MRA图像脑血管狭窄程度智能分级评估模型。模型包括：获取模块，获取MRA图像以及设备扫描参数；确定模块，根据走向向量与扫描平面法向量之间的夹角、血管段的长度，确定扫描影响度；计算模块，根据扫描影响度和信号强度分布，计算整体参考信号强度，根据信号强度与整体参考信号强度之间的差异，计算局部衰减系数；分析模块，对局部衰减系数的波动幅度进行分析，得到波动度，根据波动度与平均波动度之间的差异，确定波动一致性系数；评估模块，根据波动度和波动一致性系数，确定增强系数，以对信号强度进行校正，并进行血管边界检测，以对患者的脑血管狭窄程度进行评估。本申请能提高脑血管狭窄程度的评估准确性。
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