Forward Model and Deep Learning Based Iterative Deconvolution for Robust Dynamic CT Perfusion.

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Abstract

Perfusion maps obtained from low-dose computed tomography (CT) data suffer from poor signal to noise ratio. To enhance the quality of the perfusion maps, several works rely on denoising the low-dose CT (LD-CT) images followed by conventional regularized deconvolution. Recent works employ deep neural networks (DNN) for learning a direct mapping between the noisy and the clean perfusion maps ignoring the convolution-based forward model. DNN-based methods are not robust to practical variations in the data that are seen in real-world applications such as stroke. In this work, we propose an iterative framework that combines the perfusion forward model with a DNN-based regularizer to obtain perfusion maps directly from the LD-CT dynamic data. To improve the robustness of the DNN, we leverage the anatomical information from the contrast-enhanced LD-CT images to learn the mapping between low-dose and standard-dose perfusion maps. Through empirical experiments, we show that our model is robust both qualitatively and quantitatively to practical perturbations in the data.