2020 年 37 巻 3 号 p. 58-61
In this paper, we propose an unsupervised dynamic positron emission tomography (PET) image denoising scheme using a deep image prior with anatomical information as convolutional neural network input. The proposed conditional DIP method is an unsupervised deep learning technique with no need to prepare any prior training of image datasets including high- and low-quality image pairs, and only learns using a single data pair of a target dynamic PET image and the anatomical information of a patient’s own magnetic resonance image. A numerical simulation is performed using a three-dimensional brain phantom with 18F-FDG kinetics. Results show that using the anatomical information, the proposed conditional DIP method yielded improved image quality and quantitative performance.