SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT Perfusion

TL;DR

SDCNet is a deep learning model designed to enhance low-dose cerebral CT perfusion images, effectively reducing noise and improving image quality to mitigate radiation risks in medical imaging.

Contribution

The paper introduces SDCNet, a novel deep CNN architecture with skip-connections for denoising low-dose CT perfusion images, improving upon existing methods in both quality and efficiency.

Findings

SDCNet outperforms state-of-the-art approaches in denoising quality.

The model effectively enhances perfusion map accuracy.

It demonstrates promising computational efficiency.

Abstract

With substantial public concerns on potential cancer risks and health hazards caused by the accumulated radiation exposure in medical imaging, reducing radiation dose in X-ray based medical imaging such as Computed Tomography Perfusion (CTP) has raised significant research interests. In this paper, we embrace the deep Convolutional Neural Networks (CNN) based approaches and introduce Smoothed Dense-Convolution Neural Network (SDCNet) to recover high-dose quality CTP images from low-dose ones. SDCNet is composed of sub-network blocks cascaded by skip-connections to infer the noise (differentials) from paired low/high-dose CT scans. SDCNet can effectively remove the noise in real low-dose CT scans and enhance the quality of medical images. We evaluate the proposed architecture on thousands of CT perfusion frames for both reconstructed image denoising and perfusion map quantification including cerebral blood flow (CBF) and cerebral blood volume (CBV). SDCNet achieves high performance in both visual and quantitative results with promising computational efficiency, comparing favorably with state-of-the-art approaches. \textit{The code is available at \url{https://github.com/cswin/RC-Nets}}.