Abstract
The emergence of clinical data warehouses (CDWs), which contain the medical data of millions of patients, has paved the way for vast data sharing for research. The quality of MRIs gathered in CDWs differs greatly from what is observed in research settings and reflects a certain clinical reality. Consequently, a significant proportion of these images turns out to be unusable due to their poor quality. Given the massive volume of MRIs contained in CDWs, the manual rating of image quality is impossible. Thus, it is necessary to develop an automated solution capable of effectively identifying corrupted images in CDWs. This study presents an innovative transfer learning method for automated quality control of 3D gradient echo T1-weighted brain MRIs within a CDW, leveraging artefact simulation. We first intentionally corrupt images from research datasets by inducing poorer contrast, adding noise and introducing motion artefacts. Subsequently, three artefact-specific models are pre-trained using these corrupted images to detect distinct types of artefacts. Finally, the models are generalised to routine clinical data through a transfer learning technique, utilising 3660 manually annotated images. The overall image quality is inferred from the results of the three models, each designed to detect a specific type of artefact. Our method was validated on an independent test set of 385 3D gradient echo T1-weighted MRIs. Our proposed approach achieved excellent results for the detection of bad quality MRIs, with a balanced accuracy of over 87%, surpassing our previous approach by 3.5 percent points. Additionally, we achieved a satisfactory balanced accuracy of 79% for the detection of moderate quality MRIs, outperforming our previous performance by 5 percent points. Our framework provides a valuable tool for exploiting the potential of MRIs in CDWs.
Abstract (translated)
临床数据仓库（CDWs）的出现为大量患者的医疗数据的共享铺平了道路，同时也为研究提供了广泛的数据共享。在CDW中收集的MRI质量与研究环境中观察到的质量差异很大，反映了某些临床现实。因此，由于质量差，相当比例的这些图像无法使用。在CDW中包含的MRI数量如此之多，因此手动评估图像质量是不可能的。因此，有必要开发一种自动解决方案，能够有效地在CDW中识别损坏的图像。 本研究介绍了一种创新性的自动质量控制方法，用于在CDW中自动检查3D梯度回波T1加权脑MRI的质量，利用伪影模拟。首先，我们故意通过诱导对比度较差、添加噪声和引入运动伪影等方法损坏研究数据集中的图像。然后，使用这些损坏的图像预训练三个伪影特定模型，以检测各种伪影。最后，通过迁移学习技术将模型扩展到常规临床数据上，利用3660个手动注释的图像。 总体图像质量是从三个模型的结果中推断的，每个模型专门设计来检测一种伪影。我们的方法在385个3D梯度回波T1加权MRI的独立测试集上进行了验证。我们提出的方法在检测低质量MRI方面表现出优异的结果，平衡准确度超过了87%，比我们的前人方法高出3.5个百分点。此外，我们还实现了中等质量MRI的平衡准确度为79%，超过了我们的前人性能，5个百分点。我们的框架为利用CDW中MRI的潜力提供了宝贵的工具。
URL
https://arxiv.org/abs/2406.12448
