Joint parametric reconstruction and motion correction framework for dynamic PET data

Jieqing Jiao; Alexandre Bousse; Kris Thielemans; Pawel Markiewicz; Ninon Burgos; David Atkinson; Simon Arridge; Brian F. Hutton; Sébastien Ourselin

doi:10.1007/978-3-319-10404-1_15

Joint parametric reconstruction and motion correction framework for dynamic PET data

Jieqing Jiao^*, Alexandre Bousse, Kris Thielemans, Pawel Markiewicz, Ninon Burgos, David Atkinson, Simon Arridge, Brian F. Hutton, Sébastien Ourselin

^*Corresponding author for this work

Research output: Contribution to journal › Conference paper › peer-review

6 Citations (Scopus)

Abstract

In this paper we propose a novel algorithm for jointly performing data based motion correction and direct parametric reconstruction of dynamic PET data. We derive a closed form update for the penalised likelihood maximisation which greatly enhances the algorithm's computational efficiency for practical use. Our algorithm achieves sub-voxel motion correction residual with noisy data in the simulation-based validation and reduces the bias of the direct estimation of the kinetic parameter of interest. A preliminary evaluation on clinical brain data using [¹⁸F]Choline shows improved contrast for regions of high activity. The proposed method is based on a data-driven kinetic modelling method and is directly applicable to reversible and irreversible PET tracers, covering a range of clinical applications.

Original language	English
Pages (from-to)	114-121
Number of pages	8
Journal	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Volume	17
Issue number	Pt 1
DOIs	https://doi.org/10.1007/978-3-319-10404-1_15
Publication status	Published - 2014
Externally published	Yes
Event	17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - Boston, MA, United States Duration: 14 Sept 2014 → 18 Sept 2014

Keywords

Dynamic PET
direct parametric reconstruction
motion correction
optimisation transfer
kinetic analysis

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10.1007/978-3-319-10404-1_15

Cite this

Jiao, J., Bousse, A., Thielemans, K., Markiewicz, P., Burgos, N., Atkinson, D., Arridge, S., Hutton, B. F., & Ourselin, S. (2014). Joint parametric reconstruction and motion correction framework for dynamic PET data. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 17(Pt 1), 114-121. https://doi.org/10.1007/978-3-319-10404-1_15

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title = "Joint parametric reconstruction and motion correction framework for dynamic PET data",

abstract = "In this paper we propose a novel algorithm for jointly performing data based motion correction and direct parametric reconstruction of dynamic PET data. We derive a closed form update for the penalised likelihood maximisation which greatly enhances the algorithm's computational efficiency for practical use. Our algorithm achieves sub-voxel motion correction residual with noisy data in the simulation-based validation and reduces the bias of the direct estimation of the kinetic parameter of interest. A preliminary evaluation on clinical brain data using [18F]Choline shows improved contrast for regions of high activity. The proposed method is based on a data-driven kinetic modelling method and is directly applicable to reversible and irreversible PET tracers, covering a range of clinical applications.",

keywords = "Dynamic PET, direct parametric reconstruction, motion correction, optimisation transfer, kinetic analysis",

author = "Jieqing Jiao and Alexandre Bousse and Kris Thielemans and Pawel Markiewicz and Ninon Burgos and David Atkinson and Simon Arridge and Hutton, {Brian F.} and S{\'e}bastien Ourselin",

year = "2014",

doi = "10.1007/978-3-319-10404-1_15",

language = "English",

volume = "17",

pages = "114--121",

journal = "Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention",

number = "Pt 1",

note = "17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 ; Conference date: 14-09-2014 Through 18-09-2014",

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Jiao, J, Bousse, A, Thielemans, K, Markiewicz, P, Burgos, N, Atkinson, D, Arridge, S, Hutton, BF & Ourselin, S 2014, 'Joint parametric reconstruction and motion correction framework for dynamic PET data', Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 17, no. Pt 1, pp. 114-121. https://doi.org/10.1007/978-3-319-10404-1_15

Joint parametric reconstruction and motion correction framework for dynamic PET data. / Jiao, Jieqing; Bousse, Alexandre; Thielemans, Kris et al.
In: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, Vol. 17, No. Pt 1, 2014, p. 114-121.

Research output: Contribution to journal › Conference paper › peer-review

TY - JOUR

T1 - Joint parametric reconstruction and motion correction framework for dynamic PET data

AU - Jiao, Jieqing

AU - Bousse, Alexandre

AU - Thielemans, Kris

AU - Markiewicz, Pawel

AU - Burgos, Ninon

AU - Atkinson, David

AU - Arridge, Simon

AU - Hutton, Brian F.

AU - Ourselin, Sébastien

PY - 2014

Y1 - 2014

N2 - In this paper we propose a novel algorithm for jointly performing data based motion correction and direct parametric reconstruction of dynamic PET data. We derive a closed form update for the penalised likelihood maximisation which greatly enhances the algorithm's computational efficiency for practical use. Our algorithm achieves sub-voxel motion correction residual with noisy data in the simulation-based validation and reduces the bias of the direct estimation of the kinetic parameter of interest. A preliminary evaluation on clinical brain data using [18F]Choline shows improved contrast for regions of high activity. The proposed method is based on a data-driven kinetic modelling method and is directly applicable to reversible and irreversible PET tracers, covering a range of clinical applications.

AB - In this paper we propose a novel algorithm for jointly performing data based motion correction and direct parametric reconstruction of dynamic PET data. We derive a closed form update for the penalised likelihood maximisation which greatly enhances the algorithm's computational efficiency for practical use. Our algorithm achieves sub-voxel motion correction residual with noisy data in the simulation-based validation and reduces the bias of the direct estimation of the kinetic parameter of interest. A preliminary evaluation on clinical brain data using [18F]Choline shows improved contrast for regions of high activity. The proposed method is based on a data-driven kinetic modelling method and is directly applicable to reversible and irreversible PET tracers, covering a range of clinical applications.

KW - Dynamic PET

KW - direct parametric reconstruction

KW - motion correction

KW - optimisation transfer

KW - kinetic analysis

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U2 - 10.1007/978-3-319-10404-1_15

DO - 10.1007/978-3-319-10404-1_15

M3 - Conference paper

C2 - 25333108

AN - SCOPUS:84909589278

VL - 17

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EP - 121

JO - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

JF - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

IS - Pt 1

T2 - 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014

Y2 - 14 September 2014 through 18 September 2014

ER -

Jiao J, Bousse A, Thielemans K, Markiewicz P, Burgos N, Atkinson D et al. Joint parametric reconstruction and motion correction framework for dynamic PET data. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 2014;17(Pt 1):114-121. doi: 10.1007/978-3-319-10404-1_15

Joint parametric reconstruction and motion correction framework for dynamic PET data

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Keywords

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