Journal Article

Full-Dose PET Image Estimation from Low-Dose PET Image Using Deep Learning: a Pilot Study

Yang-Ming, Zhu, Kaplan, Sydney

2019

Overview

Positron emission tomography (PET) imaging is an effective tool used in determining disease stage and lesion malignancy; however, radiation exposure to patients and technicians during PET scans continues to draw concern. One way to minimize radiation exposure is to reduce the dose of radioactive tracer administered in order to obtain the scan. Yet, low-dose images are inherently noisy and have poor image quality making them difficult to read. This paper proposes the use of a deep learning model that takes specific image features into account in the loss function to denoise low-dose PET image slices and estimate their full-dose image quality equivalent. Testing on low-dose image slices indicates a significant improvement in image quality that is comparable to the ground truth full–dose image slices. Additionally, this approach can lower the cost of conducting a PET scan since less radioactive material is required per scan, which may promote the usage of PET scans for medical diagnosis.

• Publisher: Springer Nature B.V
• Subject: Deep learning / Emission analysis / Exposure / Ground truth / Image processing / Image quality / Machine learning / Malignancy / Medical imaging / Noise reduction / Positron emission / Positron emission tomography / Radiation / Radiation effects / Radioactive materials / Radioactive tracers / Technicians / Tomography