Improved resolution and reliability in dynamic PET using Bayesian regularization of MRTM2

Mikael Agn; Claus Svarer; Vibe G. Frokjaer; Douglas N.  Greve; Gitte M. Knudsen; Koen Van Leemput

doi:10.1109/ISBI.2014.6868030

Improved resolution and reliability in dynamic PET using Bayesian regularization of MRTM2

Mikael Agn, Claus Svarer, Vibe G. Frokjaer, Douglas N. Greve, Gitte M. Knudsen, Koen Van Leemput

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

This paper presents a mathematical model that regularizes dynamic PET data by using a Bayesian framework. We base the model on the well known two-parameter multilinear reference tissue method MRTM2 and regularize on the assumption that spatially close regions have similar parameters. The developed model is compared to the conventional approach of improving the low signal-to-noise ratio of PET data, i.e., spatial filtering of each time frame independently by a Gaussian kernel. We show that the model handles high levels of noise better than the conventional approach, while at the same time retaining a higher resolution. In addition, it results in a higher reliability between scans on individual subject data, measured by intraclass correlation for absolute agreement.

Original language	English
Title of host publication	Proceedings of the 11th IEEE International Symposium on Biomedical Imaging (ISBI 2014 )
Publisher	IEEE
Publication date	2014
Pages	955-658
ISBN (Print)	978-1-4673-1961-4
DOIs	https://doi.org/10.1109/ISBI.2014.6868030
Publication status	Published - 2014
Event	11th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Beijing, China Duration: 29 Apr 2014 → 2 May 2014 Conference number: 11 http://biomedicalimaging.org/2014/

Conference

Conference	11th IEEE International Symposium on Biomedical Imaging
Number	11
Country	China
City	Beijing
Period	29/04/2014 → 02/05/2014
Internet address	http://biomedicalimaging.org/2014/

Keywords

PET
Bayesian modeling
multilinear reference tissue method
parametric imaging
regularization

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@inproceedings{ec029c04d0f444edb1d4b07727caefa5,

title = "Improved resolution and reliability in dynamic PET using Bayesian regularization of MRTM2",

abstract = "This paper presents a mathematical model that regularizes dynamic PET data by using a Bayesian framework. We base the model on the well known two-parameter multilinear reference tissue method MRTM2 and regularize on the assumption that spatially close regions have similar parameters. The developed model is compared to the conventional approach of improving the low signal-to-noise ratio of PET data, i.e., spatial filtering of each time frame independently by a Gaussian kernel. We show that the model handles high levels of noise better than the conventional approach, while at the same time retaining a higher resolution. In addition, it results in a higher reliability between scans on individual subject data, measured by intraclass correlation for absolute agreement.",

keywords = "PET, Bayesian modeling, multilinear reference tissue method, parametric imaging, regularization",

author = "Mikael Agn and Claus Svarer and Frokjaer, {Vibe G.} and Greve, {Douglas N.} and Knudsen, {Gitte M.} and {Van Leemput}, Koen",

year = "2014",

doi = "10.1109/ISBI.2014.6868030",

language = "English",

isbn = "978-1-4673-1961-4",

pages = "955--658",

booktitle = "Proceedings of the 11th IEEE International Symposium on Biomedical Imaging (ISBI 2014 )",

publisher = "IEEE",

address = "United States",

note = "11th IEEE International Symposium on Biomedical Imaging : From Nano to Macro, ISBI 2014 ; Conference date: 29-04-2014 Through 02-05-2014",

url = "http://biomedicalimaging.org/2014/",

}

Agn, M, Svarer, C, Frokjaer, VG, Greve, DN, Knudsen, GM & Van Leemput, K 2014, Improved resolution and reliability in dynamic PET using Bayesian regularization of MRTM2. in Proceedings of the 11th IEEE International Symposium on Biomedical Imaging (ISBI 2014 ). IEEE, pp. 955-658, 11th IEEE International Symposium on Biomedical Imaging, Beijing, China, 29/04/2014. https://doi.org/10.1109/ISBI.2014.6868030

Improved resolution and reliability in dynamic PET using Bayesian regularization of MRTM2. / Agn, Mikael; Svarer, Claus; Frokjaer, Vibe G.; Greve, Douglas N. ; Knudsen, Gitte M.; Van Leemput, Koen.

Proceedings of the 11th IEEE International Symposium on Biomedical Imaging (ISBI 2014 ). IEEE, 2014. p. 955-658.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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AU - Van Leemput, Koen

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AB - This paper presents a mathematical model that regularizes dynamic PET data by using a Bayesian framework. We base the model on the well known two-parameter multilinear reference tissue method MRTM2 and regularize on the assumption that spatially close regions have similar parameters. The developed model is compared to the conventional approach of improving the low signal-to-noise ratio of PET data, i.e., spatial filtering of each time frame independently by a Gaussian kernel. We show that the model handles high levels of noise better than the conventional approach, while at the same time retaining a higher resolution. In addition, it results in a higher reliability between scans on individual subject data, measured by intraclass correlation for absolute agreement.

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KW - Bayesian modeling

KW - multilinear reference tissue method

KW - parametric imaging

KW - regularization

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