MDUNet: deep-prior unrolling network with multi-parameter data integration for low-dose computed tomography reconstruction

Abstract

The goal of this study is to reconstruct a high-quality computed tomography (CT) image from low-dose acquisition using an unrolling deep learning-based reconstruction network with less computational complexity and a more generalized model. We propose a MDUNet: Multi-parameters deep-prior unrolling network that employs the cascaded convolutional and deconvolutional blocks to unroll the model-based iterative reconstruction within a finite number of iterations by data-driven training. Furthermore, the embedded data consistency constraint in MDUNet ensures that the input low-dose images and the low-dose sinograms are consistent as well as incorporate the physics imaging geometry. Additionally, multi-parameter training was employed to enhance the model's generalization during the training process. Experimental results based on AAPM Low-dose CT datasets show that the proposed MDUNet significantly outperforms other state-of-the-art (SOTA) methods quantitatively and qualitatively. Also, the cascaded blocks reduce the computational complexity with reduced training parameters and generalize well on different datasets. In addition, the proposed MDUNet is validated on 8 different organs of interest, with more detailed structures recovered and high-quality images generated. The experimental results demonstrate that the proposed MDUNet generates favorable improvement over other competing methods in terms of visual quality, quantitative performance, and computational efficiency. The MDUNet has improved image quality with reduced computational cost and good generalization which effectively lowers radiation dose and reduces scanning time, making it favorable for future clinical deployment.