Automated image quality evaluation of T2-weighted liver MRI utilizing deep learning architecture
Automated image quality evaluation of T2-weighted liver MRI utilizing deep learning architecture
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DOI：
10.1002/jmri.25779
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发表时间：
2018-03-01
影响因子：
4.4
通讯作者：
Chandarana, Hersh
中科院分区：
文献类型：
--
作者：
Esses, Steven J.;Lu, Xiaoguang;Chandarana, Hersh
目的开发和测试一种名为卷积神经网络(CNN)的深度学习方法，用于对非诊断性图像的T-2加权(T2WI)肝脏采集进行自动筛选，并比较两位放射科医生对这种自动化方法的评估。材料和方法我们评估了2014年11月至2016年5月在我所进行的522例1.5T和3T的肝脏磁共振成像(MRI)检查，以进行CNN的培训和验证。CNN由输入层、卷积层、全连接层和输出层组成。351T(2)加权像匿名进行训练。每个病例都标有诊断性或非诊断性的标签，用于检测病变和评估肝脏形态。另一组独立收集的171例病例被隔离进行盲测。这些171T(2)加权像由两位放射科医生独立评估，并注明为诊断或非诊断。将这些171T(2)WI提交给CNN算法，并将该算法的图像质量(IQ)输出与两位放射科医生的IQ输出进行比较。结果Reader1与CNN之间的IQ标记符合率为79%，Reader2与CNN之间的IQ标记符合率为73%。与阅读器1和阅读器2相比，CNN算法识别非诊断性智商的灵敏度和特异度分别为67%和81%，对阅读器2的灵敏度和特异度分别为47%和80%，阴性预测值分别为94%和86%(相对于阅读器1和2)。证据级别：2技术效力：阶段2 J.马根。雷森。成像2018；47：723-728。
PurposeTo develop and test a deep learning approach named Convolutional Neural Network (CNN) for automated screening of T-2-weighted (T2WI) liver acquisitions for nondiagnostic images, and compare this automated approach to evaluation by two radiologists.Materials and MethodsWe evaluated 522 liver magnetic resonance imaging (MRI) exams performed at 1.5T and 3T at our institution between November 2014 and May 2016 for CNN training and validation. The CNN consisted of an input layer, convolutional layer, fully connected layer, and output layer. 351T(2)WI were anonymized for training. Each case was annotated with a label of being diagnostic or nondiagnostic for detecting lesions and assessing liver morphology. Another independently collected 171 cases were sequestered for a blind test. These 171T(2)WI were assessed independently by two radiologists and annotated as being diagnostic or nondiagnostic. These 171T(2)WI were presented to the CNN algorithm and image quality (IQ) output of the algorithm was compared to that of two radiologists.ResultsThere was concordance in IQ label between Reader 1 and CNN in 79% of cases and between Reader 2 and CNN in 73%. The sensitivity and the specificity of the CNN algorithm in identifying nondiagnostic IQ was 67% and 81% with respect to Reader 1 and 47% and 80% with respect to Reader 2. The negative predictive value of the algorithm for identifying nondiagnostic IQ was 94% and 86% (relative to Readers 1 and 2).ConclusionWe demonstrate a CNN algorithm that yields a high negative predictive value when screening for nondiagnostic T2WI of the liver. Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:723-728.