Deep Learning for Suppression of Resolution-Recovery Artefacts in MLEM PET Image Reconstruction

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Abstract

Resolution modelling in maximum likelihood expectation maximisation (MLEM) image reconstruction recovers resolution but at the cost of introducing ringing artefacts. Under-modelling, post-smoothing (PS) and regularisation methods which aim to suppress these artefacts nearly all result in a loss of resolution. This work proposes the use of deep convolutional neural networks (DCNNs) as a post-reconstruction image processing step to reduce reconstruction artefacts without compromising the resolution recovery.The DCNN results successfully suppress ringing arte-facts and furthermore result in an 80% lower normalised root mean squared error (NRMSE) versus MLEM, compared to a best decrease of only 0.2% when an optimal level of PS of MLEM is performed. The resultant images from the DCNN have lower noise, reduced ringing and partial volume effects, as well as sharper edges and improved resolution.
Original languageEnglish
Title of host publication2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)978-1-5386-2282-7
ISBN (Print)978-1-5386-2283-4
DOIs
Publication statusPublished - 15 Nov 2018
Event2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC) - Atlanta, United States
Duration: 21 Oct 201728 Oct 2017
http://www.nss-mic.org/2017/News.asp

Conference

Conference2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)
Abbreviated titleIEEE NSS/MIC 2017
Country/TerritoryUnited States
CityAtlanta
Period21/10/201728/10/2017
Internet address

Keywords

  • Deep learning
  • PET
  • MR
  • CNN
  • Convolutional neural networks

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