FDG-PET parametric imaging by total variation minimization

Hongbin Guo; Rosemary Renaut; Kewei Chen; Eric Reiman

doi:10.1016/j.compmedimag.2009.01.005

FDG-PET parametric imaging by total variation minimization

Hongbin Guo, Rosemary Renaut, Kewei Chen, Eric Reiman

Mathematical and Statistical Sciences, School of (SoMSS)

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

17 Scopus citations

Abstract

Parametric imaging of the cerebral metabolic rate for glucose (CMRGlc) using [¹⁸F]-fluoro deoxyglucose positron emission tomography is considered. Traditional imaging is hindered due to low signal-to-noise ratios at individual voxels. We propose to minimize the total variation of the tracer uptake rates while requiring good fit of traditional Patlak equations. This minimization guarantees spatial homogeneity within brain regions and good distinction between brain regions. Brain phantom simulations demonstrate significant improvement in quality of images by the proposed method as compared to Patlak images with post-filtering using Gaussian or median filters.

Original language	English (US)
Pages (from-to)	295-303
Number of pages	9
Journal	Computerized Medical Imaging and Graphics
Volume	33
Issue number	4
DOIs	https://doi.org/10.1016/j.compmedimag.2009.01.005
State	Published - Jun 2009

Keywords

Alzheimer's disease
FDG
Graphical analysis
PET quantification
Parametric imaging
Patlak plot
Total variation
Uptake rate

ASJC Scopus subject areas

Radiological and Ultrasound Technology
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Health Informatics
Computer Graphics and Computer-Aided Design

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10.1016/j.compmedimag.2009.01.005

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@article{8d27f700c75b48bda0a28a2d1a071525,

title = "FDG-PET parametric imaging by total variation minimization",

abstract = "Parametric imaging of the cerebral metabolic rate for glucose (CMRGlc) using [18F]-fluoro deoxyglucose positron emission tomography is considered. Traditional imaging is hindered due to low signal-to-noise ratios at individual voxels. We propose to minimize the total variation of the tracer uptake rates while requiring good fit of traditional Patlak equations. This minimization guarantees spatial homogeneity within brain regions and good distinction between brain regions. Brain phantom simulations demonstrate significant improvement in quality of images by the proposed method as compared to Patlak images with post-filtering using Gaussian or median filters.",

keywords = "Alzheimer's disease, FDG, Graphical analysis, PET quantification, Parametric imaging, Patlak plot, Total variation, Uptake rate",

author = "Hongbin Guo and Rosemary Renaut and Kewei Chen and Eric Reiman",

note = "Funding Information: This work was supported by grants from the state of Arizona, the NIH, R01 MH057899 and P30 AG19610 and the NSF DMS 0652833 and DMS 0513214. The authors thank Chi-Chuan Chen for providing the transition matrix, generated by the Monte Carlo method, which is used in the PET image reconstructions and Drs. Bouman and Musfata for offering their MAP reconstruction code, which provides comparisons between reconstruction methods. We also acknowledge Dr. Laszlo Balkay and Dr. Jeffrey Fessler for providing PET simulator and PET reconstruction software, respectively. ",

year = "2009",

month = jun,

doi = "10.1016/j.compmedimag.2009.01.005",

language = "English (US)",

volume = "33",

pages = "295--303",

journal = "Computerized Medical Imaging and Graphics",

issn = "0895-6111",

publisher = "Elsevier Limited",

number = "4",

}

TY - JOUR

T1 - FDG-PET parametric imaging by total variation minimization

AU - Guo, Hongbin

AU - Renaut, Rosemary

AU - Chen, Kewei

AU - Reiman, Eric

N1 - Funding Information: This work was supported by grants from the state of Arizona, the NIH, R01 MH057899 and P30 AG19610 and the NSF DMS 0652833 and DMS 0513214. The authors thank Chi-Chuan Chen for providing the transition matrix, generated by the Monte Carlo method, which is used in the PET image reconstructions and Drs. Bouman and Musfata for offering their MAP reconstruction code, which provides comparisons between reconstruction methods. We also acknowledge Dr. Laszlo Balkay and Dr. Jeffrey Fessler for providing PET simulator and PET reconstruction software, respectively.

PY - 2009/6

Y1 - 2009/6

N2 - Parametric imaging of the cerebral metabolic rate for glucose (CMRGlc) using [18F]-fluoro deoxyglucose positron emission tomography is considered. Traditional imaging is hindered due to low signal-to-noise ratios at individual voxels. We propose to minimize the total variation of the tracer uptake rates while requiring good fit of traditional Patlak equations. This minimization guarantees spatial homogeneity within brain regions and good distinction between brain regions. Brain phantom simulations demonstrate significant improvement in quality of images by the proposed method as compared to Patlak images with post-filtering using Gaussian or median filters.

AB - Parametric imaging of the cerebral metabolic rate for glucose (CMRGlc) using [18F]-fluoro deoxyglucose positron emission tomography is considered. Traditional imaging is hindered due to low signal-to-noise ratios at individual voxels. We propose to minimize the total variation of the tracer uptake rates while requiring good fit of traditional Patlak equations. This minimization guarantees spatial homogeneity within brain regions and good distinction between brain regions. Brain phantom simulations demonstrate significant improvement in quality of images by the proposed method as compared to Patlak images with post-filtering using Gaussian or median filters.

KW - Alzheimer's disease

KW - FDG

KW - Graphical analysis

KW - PET quantification

KW - Parametric imaging

KW - Patlak plot

KW - Total variation

KW - Uptake rate

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DO - 10.1016/j.compmedimag.2009.01.005

M3 - Article

C2 - 19261438

AN - SCOPUS:62949096246

SN - 0895-6111

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EP - 303

JO - Computerized Medical Imaging and Graphics

JF - Computerized Medical Imaging and Graphics

IS - 4

ER -

FDG-PET parametric imaging by total variation minimization

Abstract

Keywords

ASJC Scopus subject areas

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