Virtual high-count PET image generation using a deep learning method.

ABSTRACT:

Purpose

Recently, deep learning-based methods have been established to denoise the low-count positron emission tomography (PET) images and predict their standard-count image counterparts, which could achieve reduction of injected dosage and scan time, and improve image quality for equivalent lesion detectability and clinical diagnosis. In clinical settings, the majority scans are still acquired using standard injection dose with standard scan time. In this work, we applied a 3D U-Net network to reduce the noise of standard-count PET images to obtain the virtual-high-count (VHC) PET images for identifying the potential benefits of the obtained VHC PET images.

Methods

The training datasets, including down-sampled standard-count PET images as the network input and high-count im

SUBMITTER: Liu J 

PROVIDER: S-EPMC9474624 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature