Accurate event-driven motion compensation in high-resolution PET incorporating scattered and random events

Arman Rahmim; Katie Dinelle; Ju Chieh Cheng; Mikhail A. Shilov; William P. Segars; Sarah C. Lidstone; Stephan Blinder; Olivier G. Rousset; Hamid Vajihollahi; Benjamin M.W. Tsui; Dean F. Wong; Vesna Sossi

doi:10.1109/TMI.2008.917248

Accurate event-driven motion compensation in high-resolution PET incorporating scattered and random events

Arman Rahmim, Katie Dinelle, Ju Chieh Cheng, Mikhail A. Shilov, William P. Segars, Sarah C. Lidstone, Stephan Blinder, Olivier G. Rousset, Hamid Vajihollahi, Benjamin M.W. Tsui, Dean F. Wong, Vesna Sossi

Research output: Contribution to journal › Article › peer-review

113 Scopus citations

Abstract

With continuing improvements in spatial resolution of positron emission tomography (PET) scanners, small patient movements during PET imaging become a significant source of resolution degradation. This work develops and investigates a comprehensive formalism for accurate motion-compensated reconstruction which at the same time is very feasible in the context of high-resolution PET. In particular, this paper proposes an effective method to incorporate presence of scattered and random coincidences in the context of motion (which is similarly applicable to various other motion correction schemes). The overall reconstruction framework takes into consideration missing projection data which are not detected due to motion, and additionally, incorporates information from all detected events, including those which fall outside the fleld-of-view following motion correction. The proposed approach has been extensively validated using phantom experiments as well as realistic simulations of a new mathematical brain phantom developed in this work, and the results for a dynamic patient study are also presented.

Original language	English (US)
Article number	4446612
Pages (from-to)	1018-1033
Number of pages	16
Journal	IEEE transactions on medical imaging
Volume	27
Issue number	8
DOIs	https://doi.org/10.1109/TMI.2008.917248
State	Published - Aug 2008
Externally published	Yes

Keywords

Motion correction
Positron emission tomography (PET) iterative reconstruction
System response

ASJC Scopus subject areas

Software
Radiological and Ultrasound Technology
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TMI.2008.917248

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Rahmim, A., Dinelle, K., Cheng, J. C., Shilov, M. A., Segars, W. P., Lidstone, S. C., Blinder, S., Rousset, O. G., Vajihollahi, H., Tsui, B. M. W., Wong, D. F., & Sossi, V. (2008). Accurate event-driven motion compensation in high-resolution PET incorporating scattered and random events. IEEE transactions on medical imaging, 27(8), 1018-1033. Article 4446612. https://doi.org/10.1109/TMI.2008.917248

Rahmim, A, Dinelle, K, Cheng, JC, Shilov, MA, Segars, WP, Lidstone, SC, Blinder, S, Rousset, OG, Vajihollahi, H, Tsui, BMW, Wong, DF & Sossi, V 2008, 'Accurate event-driven motion compensation in high-resolution PET incorporating scattered and random events', IEEE transactions on medical imaging, vol. 27, no. 8, 4446612, pp. 1018-1033. https://doi.org/10.1109/TMI.2008.917248

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title = "Accurate event-driven motion compensation in high-resolution PET incorporating scattered and random events",

abstract = "With continuing improvements in spatial resolution of positron emission tomography (PET) scanners, small patient movements during PET imaging become a significant source of resolution degradation. This work develops and investigates a comprehensive formalism for accurate motion-compensated reconstruction which at the same time is very feasible in the context of high-resolution PET. In particular, this paper proposes an effective method to incorporate presence of scattered and random coincidences in the context of motion (which is similarly applicable to various other motion correction schemes). The overall reconstruction framework takes into consideration missing projection data which are not detected due to motion, and additionally, incorporates information from all detected events, including those which fall outside the fleld-of-view following motion correction. The proposed approach has been extensively validated using phantom experiments as well as realistic simulations of a new mathematical brain phantom developed in this work, and the results for a dynamic patient study are also presented.",

keywords = "Motion correction, Positron emission tomography (PET) iterative reconstruction, System response",

author = "Arman Rahmim and Katie Dinelle and Cheng, {Ju Chieh} and Shilov, {Mikhail A.} and Segars, {William P.} and Lidstone, {Sarah C.} and Stephan Blinder and Rousset, {Olivier G.} and Hamid Vajihollahi and Tsui, {Benjamin M.W.} and Wong, {Dean F.} and Vesna Sossi",

note = "Funding Information: Manuscript received August 23, 2007; revised December 20, 2007. First published February 2, 2008; last published July 25,2008 (projected). This work was supported in part by the National Institutes of Health under Grant DA00412 and Grant S10RR017219, in part by the TRIUMF Life Science Grant, in part by the Natural Sciences and Engineering Research Council of Canada, and in part by the Michael Smith Foundation for Health Research. Asterisk indicates corresponding author. *A. Rahmim is with the Department of Radiology, Johns Hopkins University, 601 N. Caroline Street, Baltimore, MD 21205 USA (e-mail: arahmim1@jhmi. edu).",

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doi = "10.1109/TMI.2008.917248",

language = "English (US)",

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AU - Dinelle, Katie

AU - Cheng, Ju Chieh

AU - Shilov, Mikhail A.

AU - Segars, William P.

AU - Lidstone, Sarah C.

AU - Blinder, Stephan

AU - Rousset, Olivier G.

AU - Vajihollahi, Hamid

AU - Tsui, Benjamin M.W.

AU - Wong, Dean F.

AU - Sossi, Vesna

N1 - Funding Information: Manuscript received August 23, 2007; revised December 20, 2007. First published February 2, 2008; last published July 25,2008 (projected). This work was supported in part by the National Institutes of Health under Grant DA00412 and Grant S10RR017219, in part by the TRIUMF Life Science Grant, in part by the Natural Sciences and Engineering Research Council of Canada, and in part by the Michael Smith Foundation for Health Research. Asterisk indicates corresponding author. *A. Rahmim is with the Department of Radiology, Johns Hopkins University, 601 N. Caroline Street, Baltimore, MD 21205 USA (e-mail: arahmim1@jhmi. edu).

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N2 - With continuing improvements in spatial resolution of positron emission tomography (PET) scanners, small patient movements during PET imaging become a significant source of resolution degradation. This work develops and investigates a comprehensive formalism for accurate motion-compensated reconstruction which at the same time is very feasible in the context of high-resolution PET. In particular, this paper proposes an effective method to incorporate presence of scattered and random coincidences in the context of motion (which is similarly applicable to various other motion correction schemes). The overall reconstruction framework takes into consideration missing projection data which are not detected due to motion, and additionally, incorporates information from all detected events, including those which fall outside the fleld-of-view following motion correction. The proposed approach has been extensively validated using phantom experiments as well as realistic simulations of a new mathematical brain phantom developed in this work, and the results for a dynamic patient study are also presented.

AB - With continuing improvements in spatial resolution of positron emission tomography (PET) scanners, small patient movements during PET imaging become a significant source of resolution degradation. This work develops and investigates a comprehensive formalism for accurate motion-compensated reconstruction which at the same time is very feasible in the context of high-resolution PET. In particular, this paper proposes an effective method to incorporate presence of scattered and random coincidences in the context of motion (which is similarly applicable to various other motion correction schemes). The overall reconstruction framework takes into consideration missing projection data which are not detected due to motion, and additionally, incorporates information from all detected events, including those which fall outside the fleld-of-view following motion correction. The proposed approach has been extensively validated using phantom experiments as well as realistic simulations of a new mathematical brain phantom developed in this work, and the results for a dynamic patient study are also presented.

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Accurate event-driven motion compensation in high-resolution PET incorporating scattered and random events

Abstract

Keywords

ASJC Scopus subject areas

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