An Attention Based Deep Learning Model for Direct Estimation of Pharmacokinetic Maps from DCE-MRI Images

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a useful imaging technique that can quantitatively measure the pharmacokinetic (PK) parameters to characterize the microvasculature of tissues. Typically, the PK parameters are extracted by fitting the MR signal intensity of the pixels on the time series with the nonlinear least-squares method. The main disadvantage is that there are thousands of voxels in a single MR slice and the time consumption of voxels fitting too btain the P K parameters is very large. Recently, deep learning methods based on convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) network have been applied to directly estimate the PK parameters from the acquired DCE-MRI image-temporal series. However, how to effectively extract discriminative spatial and temporal features within DCE-MRI for the estimation of PK parameters is still a challenging problem, due to the large intensity variation of tissue images in different temporal phases of DCE-MRI during the injection of contrast agents. In this work, we propose an attention based deep learning model for the estimation of PK parameters, which can improve the estimation performance of PK parameters by focusing on dominant spatial and temporal characteristics. Specifically, a temporal frame attention block (FAB) and a channel/spatial attention block (CSAB) are separately designed to focus on dominant features in specific temporal phases, channels and spatial areas for better estimation. Experimental results of clinical DCE-MRI from an open source RIDER-NEURO dataset with quantitative and qualitative evaluation demonstrate that the proposed method outperforms previously reported CNN-based and LSTM-based deep learning models for the estimation of PK maps, and the ablation study also demonstrates the effectiveness of the proposed attention-based modules. In addition, the visualization of the attention mechanism reflects interesting findings that are consistent with clinical interpretation.