Impact on image noise of incorporating detector blurring into image reconstruction for a small animal PET scanner

Kisung Lee; Robert S. Miyaoka; Tom K. Lewellen; Adam M. Alessio; Paul E. Kinahan

doi:10.1109/TNS.2009.2021610

Impact on image noise of incorporating detector blurring into image reconstruction for a small animal PET scanner

Kisung Lee, Robert S. Miyaoka, Tom K. Lewellen, Adam M. Alessio, Paul E. Kinahan

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

5 Citations (Scopus)

Abstract

We study the noise characteristics of an image reconstruction algorithm that incorporates a model of the non-stationary detector blurring (DB) for a mouse-imaging positron emission tomography (PET) scanner. The algorithm uses ordered subsets expectation maximization (OSEM) image reconstruction, which is used to suppress statistical noise. Including the non-stationary detector blurring in the reconstruction process [OSEM(DB)] has been shown to increase contrast in images reconstructed from measured data acquired on the fully-3D MiCES PET scanner developed at the University of Washington. As an extension, this study uses simulation studies with a fully-3D acquisition mode and our proposed FORE + OSEM(DB) reconstruction process to evaluate the volumetric contrast versus noise trade-offs of this approach. Multiple realizations were simulated to estimate the true noise properties of the algorithm. The results show that incorporation of detector blurring FORE + OSEM(DB) into the reconstruction process improves the contrast/noise trade-offs compared to FORE + OSEM in a radially dependent manner. Adding post reconstruction 3D Gaussian smoothing to FORE + OSEM and FORE + OSEM(DB) reduces the contrast versus noise advantages of FORE + OSEM(DB).

Original language	English
Article number	5280515
Pages (from-to)	2769-2776
Number of pages	8
Journal	IEEE Transactions on Nuclear Science
Volume	56
Issue number	5
DOIs	https://doi.org/10.1109/TNS.2009.2021610
Publication status	Published - 2009 Oct

Bibliographical note

Funding Information:
Manuscript received August 27, 2008; revised March 02, 2009. Current version published October 07, 2009. This work was supported by National Institutes of Health under Grants R01-CA74135, R01-CA86892, R01-EB0217, and R01-CA115870.

Keywords

Detector blurring
Fourier rebinning
Noise property
Ordered subsets expectation maximization (OSEM)
Positron emission tomography

ASJC Scopus subject areas

Nuclear and High Energy Physics
Nuclear Energy and Engineering
Electrical and Electronic Engineering

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10.1109/TNS.2009.2021610

Cite this

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title = "Impact on image noise of incorporating detector blurring into image reconstruction for a small animal PET scanner",

abstract = "We study the noise characteristics of an image reconstruction algorithm that incorporates a model of the non-stationary detector blurring (DB) for a mouse-imaging positron emission tomography (PET) scanner. The algorithm uses ordered subsets expectation maximization (OSEM) image reconstruction, which is used to suppress statistical noise. Including the non-stationary detector blurring in the reconstruction process [OSEM(DB)] has been shown to increase contrast in images reconstructed from measured data acquired on the fully-3D MiCES PET scanner developed at the University of Washington. As an extension, this study uses simulation studies with a fully-3D acquisition mode and our proposed FORE + OSEM(DB) reconstruction process to evaluate the volumetric contrast versus noise trade-offs of this approach. Multiple realizations were simulated to estimate the true noise properties of the algorithm. The results show that incorporation of detector blurring FORE + OSEM(DB) into the reconstruction process improves the contrast/noise trade-offs compared to FORE + OSEM in a radially dependent manner. Adding post reconstruction 3D Gaussian smoothing to FORE + OSEM and FORE + OSEM(DB) reduces the contrast versus noise advantages of FORE + OSEM(DB).",

keywords = "Detector blurring, Fourier rebinning, Noise property, Ordered subsets expectation maximization (OSEM), Positron emission tomography",

author = "Kisung Lee and Miyaoka, {Robert S.} and Lewellen, {Tom K.} and Alessio, {Adam M.} and Kinahan, {Paul E.}",

note = "Funding Information: Manuscript received August 27, 2008; revised March 02, 2009. Current version published October 07, 2009. This work was supported by National Institutes of Health under Grants R01-CA74135, R01-CA86892, R01-EB0217, and R01-CA115870.",

year = "2009",

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doi = "10.1109/TNS.2009.2021610",

language = "English",

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AU - Lee, Kisung

AU - Miyaoka, Robert S.

AU - Lewellen, Tom K.

AU - Alessio, Adam M.

AU - Kinahan, Paul E.

N1 - Funding Information: Manuscript received August 27, 2008; revised March 02, 2009. Current version published October 07, 2009. This work was supported by National Institutes of Health under Grants R01-CA74135, R01-CA86892, R01-EB0217, and R01-CA115870.

PY - 2009/10

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N2 - We study the noise characteristics of an image reconstruction algorithm that incorporates a model of the non-stationary detector blurring (DB) for a mouse-imaging positron emission tomography (PET) scanner. The algorithm uses ordered subsets expectation maximization (OSEM) image reconstruction, which is used to suppress statistical noise. Including the non-stationary detector blurring in the reconstruction process [OSEM(DB)] has been shown to increase contrast in images reconstructed from measured data acquired on the fully-3D MiCES PET scanner developed at the University of Washington. As an extension, this study uses simulation studies with a fully-3D acquisition mode and our proposed FORE + OSEM(DB) reconstruction process to evaluate the volumetric contrast versus noise trade-offs of this approach. Multiple realizations were simulated to estimate the true noise properties of the algorithm. The results show that incorporation of detector blurring FORE + OSEM(DB) into the reconstruction process improves the contrast/noise trade-offs compared to FORE + OSEM in a radially dependent manner. Adding post reconstruction 3D Gaussian smoothing to FORE + OSEM and FORE + OSEM(DB) reduces the contrast versus noise advantages of FORE + OSEM(DB).

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Impact on image noise of incorporating detector blurring into image reconstruction for a small animal PET scanner

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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