Convolutional Neural Network-Based Robust Denoising of Low-Dose Computed Tomography Perfusion Maps

Abstract

The low-dose computed tomography (CT) perfusion data has low signal-to-noise ratio resulting in derived perfusion maps being noisy. These low-quality maps typically requires a denoising step to improve their utility in real-time. The existing methods, including state-of-the-art online sparse perfusion deconvolution (SPD), largely relies on the convolutional model that may not be applicable in all cases of brain perfusion. In this paper, a denoising convolutional neural network (DCNN) was proposed that relies only on computed perfusion maps for performing the denoising step. The network was trained with a large number of low-dose digital brain phantom perfusion maps to provide an approximation to the corresponding high-dose perfusion maps. The batch normalization coupled with residual learning makes the trained model invariant to the dynamic range of the input low-dose perfusion maps. The denoising of the raw-data using the convolutional neural network was also attempted here and shown to have limited applicability in the low-dose CT perfusion cases. The digital perfusion phantom as well as in-vivo results indicate that the proposed DCNN applied in the derived map domain provides superior improvement compared to the online SPD with an added advantage of being computationally efficient.