PET Image Super Resolution using Convolutional Neural Networks

Farnaz Garehdaghi, Saeed Meshgini, Reza Afrouzian, Ali Farzamnia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Positron Emission Tomography (PET) is a nuclear, in-vivo medical imaging technology which can make 3D images of tissue metabolic act, in which high dose of tracer is needed to obtain a high quality PET image, which affects patients' health. Due to the limitations of using high dose tracer and limitations of physical imaging systems, it is not easy to get an image in desired resolution. Simplest approach to generate a high resolution image is by post processing. Single Image Super Resolution (SISR) is a post processing procedure to retrieve a high resolution image from a low resolution input. We propose a convolutional neural network trained on PET images which can estimate a high resolution PET image from its input low resolution image.

Original languageEnglish
Title of host publication5th Iranian Conference on Signal Processing and Intelligent Systems, ICSPIS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages5
ISBN (Electronic)9781728153506
ISBN (Print)9781728153513
DOIs
Publication statusPublished - 16 Apr 2019
Externally publishedYes
Event5th Iranian Conference on Signal Processing and Intelligent Systems, - Shahrood, Iran, Islamic Republic of
Duration: 18 Dec 201919 Dec 2019
Conference number: 5

Conference

Conference5th Iranian Conference on Signal Processing and Intelligent Systems,
Abbreviated titleICSPIS 2019
Country/TerritoryIran, Islamic Republic of
CityShahrood
Period18/12/1919/12/19

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