A Novel Iterative Image Reconstruction Method for Low-Dose CT based on Artificial Neural Networks

Computed tomography (CT) is a commonly utilized diagnostic imaging method that relies on ionizing X-ray radiation, which can pose potential risks to patients. The challenge of reconstructing CT images under low doses of X-rays persists, as conventional algorithms often lead to considerable degradation in image quality. Although advanced iterative reconstruction techniques partially mitigate this problem, recent approaches based on deep learning have exhibited impressive performance, albeit at the expense of needing extensive, high-quality training datasets that are often scarce in the field of medical imaging. In this study, we introduce a novel iterative reconstruction technique that incorporates neural-network-based modeling into the reconstruction process without the need for any training data. This proposed method seeks to leverage the significant representational capabilities of neural networks while maintaining the flexibility and robustness characteristic of model-based iterative methods. Initial results obtained from phantom data containing human-like tissue inserts indicate that the method can successfully reconstruct high-quality images and evidence of its efficacy in scenarios involving reduced doses of X-rays.