Paper Reading AI Learner

Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation

2024-06-18 09:53:07
Sophie Loizillon (for The Alzheimer's Disease Neuroimaging Initiative, APPRIMAGE Study Group), Simona Bottani (for The Alzheimer's Disease Neuroimaging Initiative, APPRIMAGE Study Group), St\'ephane Mabille (for The Alzheimer's Disease Neuroimaging Initiative, APPRIMAGE Study Group), Yannick Jacob (for The Alzheimer's Disease Neuroimaging Initiative, APPRIMAGE Study Group), Aur\'elien Maire (for The Alzheimer's Disease Neuroimaging Initiative, APPRIMAGE Study Group), Sebastian Str\"oer (for The Alzheimer's Disease Neuroimaging Initiative, APPRIMAGE Study Group), Didier Dormont (for The Alzheimer's Disease Neuroimaging Initiative, APPRIMAGE Study Group), Olivier Colliot (for The Alzheimer's Disease Neuroimaging Initiative, APPRIMAGE Study Group), Ninon Burgos (for The Alzheimer's Disease Neuroimaging Initiative, APPRIMAGE Study Group)


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。



3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot