A Temporal Deep Learning Approach for MR Perfusion Parameter Estimation in Stroke

Perfusion magnetic resonance (MR) images are often used in the assessment of acute ischemic stroke to distinguish between salvageable tissue and infarcted core. Deconvolution methods such as singular value decomposition have been used to approximate model-based perfusion parameters from these images. However, studies have shown that these existing deconvolution algorithms can introduce distortions that may negatively influence the utility of these parameter maps. There is limited previous work on utilizing machine learning algorithms to estimate perfusion parameters. In this work, we present a novel bi-input convolutional neural network (bi-CNN) to approximate four perfusion parameters without using an explicit deconvolution method. These bi-CNNs produced good approximations for all four parameters, with relative average root-mean-square errors (ARMSEs) ≤ 5% of the maximum values. We further demonstrate the utility of the estimated perfusion maps for quantifying the salvageable tissue volume in stroke, with more than 80% agreement with the ground truth. These results show that deep learning techniques are a promising tool for perfusion parameter estimation without requiring a standard deconvolution process.