LUNA: Loss-Construct Unsupervised Network Adjustment for Low-Dose CT Image Reconstruction

Abstract

Reconstructing low-dose CT imaging deals with handling the inherent noise within the data, which makes it a complex mathematical problem known as an ill-posed inverse problem. Recent attention has shifted towards deep learning-based techniques in CT image reconstruction. However, these approaches encounter limitations due to extensive data requirements for training and validation. We propose an unsupervised CT reconstruction technique that leverages the power of Deep convolutional neural networks (Deep CNNs), demonstrating that a randomly initialized neural network can serve as a prior. We have proposed a completely unsupervised deep learning technique called Loss-construct unsupervised network (LUNA) adjustment for low-dose CT image reconstruction. Our approach combines the traditional reconstruction technique, i.e., simultaneous algebraic reconstruction technique (SART), and integrates the image prior i.e., weighted total variation (WTV) regularization within the Deep CNN model. The overall reconstruction process is optimized using the alternating direction method of multipliers (ADMM) framework, that balances the neural network’s internal representation with the observed data, yielding a more consistent and accurate final image. The proposed method uses various loss functions to update the Deep CNN. The optimal update of the network depends on the various loss functions. Different loss functions, including perceptual loss, SSIM loss, WL2 loss, WTV loss, and sinogram loss, are used to guide the overall reconstruction. This approach effectively handles the constraints of data limitation of deep learning-based techniques, offering a robust and unsupervised solution for low-dose CT image reconstruction.

Keywords

• Low dose CT
• unsupervised learning
• SART
• ADMM based optimization
• loss functions