SDCNet: Smoothed Dense-Convolution Network for Restoring Low-Dose Cerebral CT Perfusion
This work addresses the need for safer medical imaging by restoring image quality in low-dose CT scans, which is an incremental improvement in a domain-specific application.
The paper tackles the problem of reducing radiation dose in cerebral CT perfusion imaging by introducing SDCNet, a deep CNN-based method that recovers high-dose quality images from low-dose ones, achieving high performance in visual and quantitative results with promising computational efficiency compared to state-of-the-art approaches.
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}}.