With the increasing size of datasets used in medical imaging research, the need for automated data curation is arising. One important data curation task is the structured organization of a dataset for preserving integrity and ensuring reusability. Therefore, we investigated whether this data organization step can be automated. To this end, we designed a convolutional neural network (CNN) that automatically recognizes eight different brain magnetic resonance imaging (MRI) scan types based on visual appearance. Thus, our method is unaffected by inconsistent or missing scan metadata. It can recognize pre-contrast T1-weighted (T1w),post-contrast T1-weighted (T1wC), T2-weighted (T2w), proton density-weighted (PDw) and derived maps (e.g. apparent diffusion coefficient and cerebral blood flow). In a first experiment,we used scans of subjects with brain tumors: 11065 scans of 719 subjects for training, and 2369 scans of 192 subjects for testing. The CNN achieved an overall accuracy of 98.7%. In a second experiment, we trained the CNN on all 13434 scans from the first experiment and tested it on 7227 scans of 1318 Alzheimer’s subjects. Here, the CNN achieved an overall accuracy of 98.5%. In conclusion, our method can accurately predict scan type, and can quickly and automatically sort a brain MRI dataset virtually without the need for manual verification. In this way, our method can assist with properly organizing a dataset, which maximizes the shareability and integrity of the data.

随着医学成像研究中使用的数据集规模的不断增加，对自动化数据管理的需求不断增加。一项重要的数据管理任务是数据集的结构化组织，以保持完整性并确保可重用性。因此，我们研究了这个数据组织步骤是否可以自动化。为此，我们设计了一个卷积神经网络（CNN），可以根据视觉外观自动识别八种不同的脑部磁共振成像（MRI）扫描类型。因此，我们的方法不受扫描元数据不一致或丢失的影响。它可以识别造影前T1加权（T1w）、造影后T1加权（T1wC）、T2加权（T2w）、质子密度加权（PDw）和衍生图（例如表观扩散系数和脑血流量） 。在第一个实验中，我们使用了脑肿瘤受试者的扫描：719 名受试者的 11065 次扫描用于训练，192 名受试者的 2369 次扫描用于测试。 CNN 的总体准确率达到 98.7%。在第二个实验中，我们在第一个实验的所有 13434 次扫描上训练了 CNN，并在 1318 名阿尔茨海默病受试者的 7227 次扫描上进行了测试。在此，CNN 的总体准确率达到了 98.5%。总之，我们的方法可以准确预测扫描类型，并且可以快速、自动地对大脑 MRI 数据集进行虚拟排序，而无需手动验证。通过这种方式，我们的方法可以帮助正确组织数据集，从而最大限度地提高数据的可共享性和完整性。