3D Convolutional Neural Network to Enhance Small-Animal Positron Emission Tomography Images in the Sinogram Domain

Leandro José Rodríguez Hernández, Humberto de Jesús Ochoa Domínguez, Osslan Osiris Vergara Villegas, Vianey Guadalupe Cruz Sánchez, Juan Humberto Sossa Azuela, Javier Polanco González

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

Abstract

In this work, we propose a three dimensional (3D) convolutional neural network (CNN) to enhance sinograms acquired from a small-animal positron emission tomography (PET) scanner. The network consists of three convolutional layers created with 3D filters of sizes 9, 3, and 5, respectively. We extracted 15250 3D patches from low- and high-count sinograms to build the low- and high-resolution pairs for training. After training and prediction, the enhanced sinogram is reconstructed using the ordered subset expectation maximization (OSEM) algorithm. The results revealed that the proposed network improved the spillover ratio and the uniformity of the standard NU4-2008 phantom up to 8% and 75%, respectively.

Original languageEnglish
Title of host publicationPattern Recognition - 14th Mexican Conference, MCPR 2022, Proceedings
EditorsOsslan Osiris Vergara-Villegas, Vianey Guadalupe Cruz-Sánchez, Juan Humberto Sossa-Azuela, Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, José Arturo Olvera-López
PublisherSpringer Science and Business Media Deutschland GmbH
Pages94-104
Number of pages11
ISBN (Print)9783031077494
DOIs
StatePublished - 2022
Event14th Mexican Conference on Pattern Recognition, MCPR 2022 - Ciudad Juárez, Mexico
Duration: 22 Jun 202225 Jun 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13264 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th Mexican Conference on Pattern Recognition, MCPR 2022
Country/TerritoryMexico
CityCiudad Juárez
Period22/06/2225/06/22

Keywords

  • Convolutional neural network
  • Image enhancement
  • Positron emission tomography
  • Sinogram

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