基于数字减影血管造影图像的脑动静脉畸形患者特异性血流分析数据集

数据集获取（Figshare）

• 源站标题：Data_Sheet_2_Patient-Specific Blood Flow Analysis for Cerebral Arteriovenous Malformation Based on Digital Subtraction Angiography Images.zip
• DOI：10.3389/fbioe.2020.00775.s002
• 发布日期：2020-07-23
• 文件数：1 个
• 文件总体积：17.64 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Patient-Specific_Blood_Flow_Analysis_for_Cerebral_Arteriovenous_Malformation_Based_on_Digital_Subtraction_Angiography_Images_zip/12696191

命令行下载

curl -L -o "Data_Sheet_2_Patient-Specific Blood Flow Analysis for Cerebral Arteriovenous Malformation Based on Digital Subtraction Angiography Images.zip" "https://ndownloader.figshare.com/files/24042863"

文件清单

• Data_Sheet_2_Patient-Specific Blood Flow Analysis for Cerebral Arteriovenous Malformation Based on Digital Subtraction Angiography Images.zip（17.64 KB）

文件总体积 17.64 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Real-time digital subtraction angiography (DSA) is capable of revealing the cerebral vascular morphology and blood flow perfusion patterns of arterial venous malformations (AVMs). In this study, we analyze the DSA images of a subject-specific left posterior AVM case and customize a generic electric analog model for cerebral circulation accordingly. The generic model consists of electronic components representing 49 major cerebral arteries and veins, and yields their blood pressure and flow rate profiles. The model was adapted by incorporating the supplying and draining patterns of the AVM to simulate some typical AVM features such as the blood “steal” syndrome, where the flow rate in the left posterior artery increases by almost three times (∼300 ml/min vs 100 ml/min) compared with the healthy case. Meanwhile, the flow rate to the right posterior artery is reduced to ∼30 ml/min from 100 ml/min despite the presence of an autoregulation mechanism in the model. In addition, the blood pressure in the draining veins is increased from 9 to 22 mmHg, and the blood pressure in the feeding arteries is reduced from 85 to 30 mmHg due to the fistula effects of the AVM. In summary, a first DSA-based AVM model has been developed. More subject-specific AVM cases are required to apply the presented in silico model, and in vivo data are used to validate the simulation results.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fbioe.2020.00775.s002（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Patient-Specific_Blood_Flow_Analysis_for_Cerebral_Arteriovenous_Malformation_Based_on_Digital_Subtraction_Angiography_Images_zip/12696191）。