Kim Joonghyun, Chae Allison, Duda Jeffrey, Borthakur Ari, Rader Daniel, Gee James C, Kahn Charles E, BioBank Penn Medicine, Witschey Walter R, Sagreiya Hersh
University of Pennsylvania.
Res Sq. 2024 Dec 9:rs.3.rs-5334453. doi: 10.21203/rs.3.rs-5334453/v1.
Advances in magnetic resonance imaging (MRI) have revolutionized disease detection and treatment planning. However, as the volume and complexity of MRI data grow with increasing heterogeneity between institutions in imaging protocol, scanner technology, and data labeling, there is a need for a standardized methodology to efficiently identify, characterize, and label MRI sequences. Such a methodology is crucial for advancing research efforts that incorporate MRI data from diverse populations to develop robust machine learning models. This research utilizes convolutional neural networks (CNNs) to automatically classify sequence, orientation, and contrast, specifically tailored for abdominal MRI. Three distinct CNN models with similar backbone architectures were trained to classify single image slices into one of 12 sequences, 4 orientations, and 2 contrast classes. Results derived from this method demonstrate high levels of performance for the three specialized CNN models, with model accuracies for sequence, orientation, and contrast of 96.9%, 97.4%, and 97.3%, respectively.
磁共振成像（MRI）技术的进步彻底改变了疾病检测和治疗方案规划。然而，随着MRI数据量和复杂性的增加，各机构在成像协议、扫描仪技术和数据标注方面的异质性也日益凸显，因此需要一种标准化方法来高效识别、表征和标注MRI序列。这种方法对于推进整合来自不同人群的MRI数据以开发强大机器学习模型的研究工作至关重要。本研究利用卷积神经网络（CNN）对腹部MRI进行序列、方向和对比度的自动分类。训练了三个具有相似骨干架构的不同CNN模型，将单个图像切片分类为12种序列、4种方向和2种对比度类别之一。该方法得出的结果表明，这三个专门的CNN模型具有很高的性能，序列、方向和对比度的模型准确率分别为96.9%、97.4%和97.3%。
