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PET-MR respiratory signal estimation using semi-supervised manifold alignment

Research output: Chapter in Book/Report/Conference proceedingConference paper

Original languageEnglish
Title of host publication2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PublisherIEEE Computer Society
Pages599-603
Number of pages5
Volume2018-April
ISBN (Electronic)9781538636367
DOIs
StateE-pub ahead of print - 24 May 2018
Event15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 - Washington, United States
Duration: 4 Apr 20187 Apr 2018

Conference

Conference15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
CountryUnited States
CityWashington
Period4/04/20187/04/2018

King's Authors

Abstract

In simultaneous PET-MR scanning, respiratory motion can lead to artefacts and blurring in both PET and MR images, negatively impacting research and clinical applications. This can be compensated for by estimating respiratory motion through a respiratory signal. Here, we propose a data-driven dimensionality-reduction-based technique which aligns manifolds formed from both PET and MR data to produce a robust signal even in situations where MR data are unavailable, as expected in realistic workflows. To handle the missing MR data, 3 methods for semi-supervised manifold alignment alignment were tested using a semi-synthetic dataset consisting of 500 0.64 s dynamic MR volumes and PET sinograms. It was found that implicit correspondences for unlabelled PET data were most effective on average for signal estimation, at 81 ± 4% mean correlation to a gold standard diaphragmatic navigator, compared to 89 ± 0.2% when using MR only with no missing data. Two explicit correspondence estimators, based on graph theory, performed poorly, with 1-to-1 and many-to-1 correspondences achieving 34 ±16% correlation and 31 ± 9% correlation, respectively.

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