论文标题
自动伪影清除具有深神经网络的静止状态fMRI
Automatic artifact removal of resting-state fMRI with Deep Neural Networks
论文作者
论文摘要
功能磁共振成像（fMRI）是一种用于研究脑活动的非侵入性技术。在功能磁共振成像的会话中，受试者执行一组任务（与任务相关的fMRI研究）或没有任务（静止状态fMRI），并获得了一系列3-D脑图像以进行进一步分析。在fMRI过程中，某些激活来源是由噪声和伪影引起的。在分析大脑激活之前，必须去除这些来源。深度神经网络（DNN）结构可用于去核和伪影。 DNN模型的主要优点是鉴于原始数据，自动学习抽象和有意义的功能。这项工作使用静止状态fMRI会话中的空间和时间信息介绍了用于噪声和人工制品分类的高级DNN体系结构。使用来自所有域中的信息，通过投票模式来实现最高的性能，平均准确性超过98％，敏感性和特异性指标之间的平均平衡非常好（分别为98.5％和97.5％）。
Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for studying brain activity. During an fMRI session, the subject executes a set of tasks (task-related fMRI study) or no tasks (resting-state fMRI), and a sequence of 3-D brain images is obtained for further analysis. In the course of fMRI, some sources of activation are caused by noise and artifacts. The removal of these sources is essential before the analysis of the brain activations. Deep Neural Network (DNN) architectures can be used for denoising and artifact removal. The main advantage of DNN models is the automatic learning of abstract and meaningful features, given the raw data. This work presents advanced DNN architectures for noise and artifact classification, using both spatial and temporal information in resting-state fMRI sessions. The highest performance is achieved by a voting schema using information from all the domains, with an average accuracy of over 98% and a very good balance between the metrics of sensitivity and specificity (98.5% and 97.5% respectively).
