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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › 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	2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Publisher	IEEE
Pages	955-958
Number of pages	4
ISBN (Electronic)	9781467319591
DOIs	https://doi.org/10.1109/ISBI.2014.6868030
Publication status	Published - 29 Jul 2014
MoE publication type	A4 Article in a conference publication
Event	IEEE International Symposium on Biomedical Imaging - Beijing, China Duration: 29 Apr 2014 → 2 May 2014 Conference number: 11

Conference

Conference	IEEE International Symposium on Biomedical Imaging
Abbreviated title	ISBI
Country/Territory	China
City	Beijing
Period	29/04/2014 → 02/05/2014

Keywords

Bayesian modeling
Multilinear reference tissue method
Parametric imaging
PET
Regularization

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10.1109/ISBI.2014.6868030

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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 = "Bayesian modeling, Multilinear reference tissue method, Parametric imaging, PET, 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",

month = jul,

day = "29",

doi = "10.1109/ISBI.2014.6868030",

language = "English",

pages = "955--958",

booktitle = "2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014",

publisher = "IEEE",

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note = "IEEE International Symposium on Biomedical Imaging, ISBI ; Conference date: 29-04-2014 Through 02-05-2014",

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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 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014., 6868030, IEEE, pp. 955-958, 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.

2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014. IEEE, 2014. p. 955-958 6868030.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Improved resolution and reliability in dynamic PET using Bayesian regularization of MRTM2

AU - Agn, Mikael

AU - Svarer, Claus

AU - Frokjaer, Vibe G.

AU - Greve, Douglas N.

AU - Knudsen, Gitte M.

AU - Van Leemput, Koen

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N2 - 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.

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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ER -

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

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Keywords

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