NIFA: Low-dose CT imaging via noise intensity field aware networks

Computed tomography (CT) is one of the most widely used imaging modalities in clinical practice. While profound for disease diagnosis, the extensive use of CT has contributed to the major part of population-based radiation dose, raising public concerns about the potential risk of cancer. Therefore, low-dose CT (LDCT) has attracted much attention in the past decades. LDCT lowers x-ray dose to reduce health risks during data acquisition, but it introduces excessive image noise and artifacts, compromising image quality, which hinders its clinical use. To address this problem, studies using data-driven deep neural networks to improve the LDCT image quality have been investigated. Here we tackle the LDCT challenge by leveraging the data-driven paradigm together with CT imaging physics to develop a more clinically relevant predictive model. We formulate noise in LDCT images as a noise intensity field and denoising process as intensity value regression. Based on the formulation, a noise intensity field aware (NIFA) network which separately extracts low-intensity and high-intensity information is proposed to reduce the magnitude of the intensity field while preserving texture information and anatomical details. Extensive experiments are conducted to evaluate the performance of the proposed method, including ablation studies to demonstrate the effectiveness of the innovative design.