Adaptive 3D noise level‐guided restoration network for low‐dose positron emission tomography imaging

Abstract

Many deep learning methods have been proposed to improve the quality of low‐dose PET images (LPET), which usually construct end‐to‐end networks with certain radiation dose inputs. However, these approaches have omitted the noise disparity in PET images, which may differ among manufacturers or populations. Therefore, we tend to exploit these noise differences among PET images to achieve adaptive restoration. We proposed a 3D noise level‐guided PET restoration network for LPET including (1) adaptive noise level‐aware subnetwork and (2) LPET restoration subnetwork. The first subnetwork aims to predict the noise level of the given LPET, while the second subnetwork treats the estimated noise level as a priori information to guide the restoration process from LPET to standard‐dose PET images. Experiments were performed on real human head and neck datasets while the peak signal‐to‐noise ratio and structural similarity index measure were used to evaluate LPET recovery performance. Moreover, we also compared the proposed network with several deep‐learning approaches. Experimental results demonstrate that our network with dual‐stage design can perform adaptive restoration for LPET, yielding better visual and quantitative results. In future work, we attempt to apply our method to other imaging tasks and adapt it for clinical practice.

Few‐shot segmentation framework for lung nodules via an optimized active contour model

“…With the development of medical imaging equipment, X-rays, computed tomography (CT), 1 magnetic resonance imaging (MRI), 2 positron emission tomography (PET), 3 and ultrasonography have become important medical aids for patient health. Images acquired with these technologies benefit many applications, such as disease diagnosis, prognostic assessment, and surgical planning.…”