Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Israel; The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Israel.
The Zlotowski Center for Neuroscience, Ben-Gurion University of the Negev, Israel; Department of Physiology and Cell Biology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Israel.
Med Image Anal. 2017 Dec;42:145-159. doi: 10.1016/j.media.2017.07.006. Epub 2017 Aug 2.
Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging protocol where MRI scans are acquired repetitively throughout the injection of a contrast agent. The analysis of dynamic scans is widely used for the detection and quantification of blood-brain barrier (BBB) permeability. Extraction of the pharmacokinetic (PK) parameters from the DCE-MRI concentration curves allows quantitative assessment of the integrity of the BBB functionality. However, curve fitting required for the analysis of DCE-MRI data is error-prone as the dynamic scans are subject to non-white, spatially-dependent and anisotropic noise. We present a novel spatio-temporal framework based on Deep Neural Networks (DNNs) to address the DCE-MRI denoising challenges. This is accomplished by an ensemble of expert DNNs constructed as deep autoencoders, where each is trained on a specific subset of the input space to accommodate different noise characteristics and curve prototypes. Spatial dependencies of the PK dynamics are captured by incorporating the curves of neighboring voxels in the entire process. The most likely reconstructed curves are then chosen using a classifier DNN followed by a quadratic programming optimization. As clean signals (ground-truth) for training are not available, a fully automatic model for generating realistic training sets with complex nonlinear dynamics is introduced. The proposed approach has been successfully applied to full and even temporally down-sampled DCE-MRI sequences, from two different databases, of stroke and brain tumor patients, and is shown to favorably compare to state-of-the-art denoising methods.
动态对比增强磁共振成像（DCE-MRI）是一种在注射造影剂的过程中重复采集磁共振扫描的成像方案。动态扫描的分析广泛用于检测和量化血脑屏障（BBB）的通透性。从 DCE-MRI 浓度曲线中提取药代动力学（PK）参数可以定量评估 BBB 功能的完整性。然而，由于动态扫描受到非白色、空间依赖和各向异性噪声的影响，因此用于分析 DCE-MRI 数据的曲线拟合容易出错。我们提出了一种基于深度神经网络（DNN）的新的时空框架来解决 DCE-MRI 去噪挑战。这是通过构建深度自动编码器的专家 DNN 集合来实现的，每个自动编码器都在输入空间的特定子集上进行训练，以适应不同的噪声特征和曲线原型。通过在整个过程中包含相邻体素的曲线来捕获 PK 动力学的空间依赖性。然后使用分类器 DNN 选择最有可能的重建曲线，然后进行二次规划优化。由于没有干净的信号（真实信号）用于训练，因此引入了一种全自动模型，用于生成具有复杂非线性动力学的逼真训练集。该方法已成功应用于来自两个不同数据库（中风和脑肿瘤患者）的全甚至时间下采样的 DCE-MRI 序列，并显示出优于最先进的去噪方法。
