中国CT和MRI杂志2024,Vol.22Issue(12) :22-25.DOI:10.3969/j.issn.1672-5131.2024.12.008

基于3D卷积神经网络的3.0T磁共振32通道在急性脑梗死诊疗中的应用研究

Research on the Application of 3.0T Magnetic Resonance 32-Channel Based on 3D Convolutional Neural Network in the Diagnosis and Treatment of Acute Cerebral Infarction

闫力永
中国CT和MRI杂志2024,Vol.22Issue(12) :22-25.DOI:10.3969/j.issn.1672-5131.2024.12.008

基于3D卷积神经网络的3.0T磁共振32通道在急性脑梗死诊疗中的应用研究

Research on the Application of 3.0T Magnetic Resonance 32-Channel Based on 3D Convolutional Neural Network in the Diagnosis and Treatment of Acute Cerebral Infarction

闫力永1
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作者信息

  • 1. 复旦大学附属中山医院青浦分院放射科(上海青浦 201700)
  • 折叠

摘要

目的 探讨基于3D卷积神经网络(3D-CNN)的3.OT磁共振32通道在急性脑梗死(AIS)诊疗中的应用价值.方法 选择2022年11月至2023年6月住院的依据AIS早期诊治指南确诊的患者作为研究对象,共纳入280例研究样本.每位患者在卒中急性期接受3.OT MRI检查,序列包括T1、T1c、T2、CBF、CBV、DWI、Tmax和TTP.图像分辨率为2×2×2 mm3.180名患者的MRI数据作为训练集,100名接受过MRI检查的患者为测试集.利用3D-CNN模型对训练集进行训练,并对测试集进行预测,输出AIS病灶的分割结果.AIS病灶由缺血中心区及其周围的缺血半暗带组成,缺血中心区的脑组织完全坏死,不能恢复.本算法能够同时识别中心区和半暗带,而不仅仅是半暗带.本算法的性能是以DICE系数、精度、灵敏度、平均对称表面距离(ASSD)和Hoffman距离为因素进行评估的.结果 本算法在测试集上得到了较高的分割性能,DICE系数为0.87±0.05,精度为0.91±0.04,灵敏度为0.85±0.06,ASSD为 1.23±0.32 mm,Hoffman距离为 1.56±0.41 mm.与影像专家手动标记结果进行比较,差异无统计学意义(P>0.05).结论 基于3D-CNN的3.0T磁共振32通道在AIS诊疗中具有较高的应用价值,可以有效地分割AIS病灶,为临床医生提供更准确和更快速的诊断依据.

Abstract

Objective To explore the application value of 3.OT Magnetic Resonance 32-Channel based on 3D Convolutional Neural Network(3D-CNN)in the diagnosis and treatment of Acute Ischemic Stroke(AIS).Methods Patients diagnosed with AIS according to the early treatment guidelines between November 2022 and June 2023 were selected as research subjects,with a total of 280 study samples included.Each patient underwent a 3.OT MRI scan in the acute phase of the stroke,with sequences including T1,T1c,T2,CBF,CBV,DWI,Tmax,and TTP.The image resolution was 2x2x2 mm3.MRI data from 180 patients were used as the training set,and 100 patients who had undergone MRI examinations constituted the test set.The 3D-CNN model was used to train the training set and predict the test set,outputting the segmentation results of AIS lesions.AIS lesions consist of the ischemic core and the surrounding penumbra,with the brain tissue in the ischemic core being completely necrotic and irrecoverable.This algorithm can identify both the core and the penumbra,not just the penumbra.The performance of this algorithm was evaluated based on the DICE coefficient,accuracy,sensitivity,Average Symmetric Surface Distance(ASSD),and Hoffman distance.Results The algorithm demonstrated high segmentation performance on the test set,with a DICE coefficient of 0.87±0.05,accuracy of 0.91±0.04,sensitivity of 0.85±0.06,ASSD of 1.23±0.32 mm,and Hausdorff distance of 1.56±0.41 mm.Compared with manual marking results by imaging experts,there was no significant statistical difference(P>0.05).Conclusion The 3.0T Magnetic Resonance 32-Channel based on 3D-CNN has high application value in the diagnosis and treatment of AIS,effectively segmenting AIS lesions,and providing more accurate and faster diagnostic basis for clinical doctors.

关键词

急性脑梗死/半暗带/3D卷积神经网络/3.OT磁共振/32通道

Key words

Acute Cerebral Infarction/Penumbra/3D Convolutional Neural Network/3.0T Magnetic Resonance/32-Channel

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出版年

2024
中国CT和MRI杂志
北京大学深圳临床医学院 北京大学第一医院

中国CT和MRI杂志

CSTPCD
影响因子:1.578
ISSN:1672-5131
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