Parametric dynamic F-18-FDG PET/CT breast imaging

Alphonso Magri; David Feiglin; Edward Lipson; James Mandel; Wendy McGraw; Wei Lee; Andrzej Krol

doi:10.1117/12.770984

Parametric dynamic F-18-FDG PET/CT breast imaging

Alphonso Magri, David Feiglin, Edward Lipson, James Mandel, Wendy McGraw, Wei Lee, Andrzej Krol

Department of Physics

Research output: Chapter in Book/Entry/Poem › Conference contribution

Abstract

This study was undertaken to estimate metabolic tissue properties from dynamic breast F-18-FDG PET/CT image series and to display them as 3D parametric images. Each temporal PET series was obtained immediately after injection of 10 mCi of F-18-FDG and consisted of fifty 1- minute frames. Each consecutive frame was nonrigidly registered to the first frame using a finite element method (FEM) based model and fiducial skin markers. Nonlinear curve fitting of activity vs. time based on a realistic two-compartment model was performed for each voxel of the volume. Curve fitting was accomplished by application of the Levenburg-Marquardt algorithm (LMA) that minimized X². We evaluated which parameters are most suitable to determine the spatial extent and malignancy in suspicious lesions. In addition, Patlak modeling was applied to the data. A mixture model was constructed and provided a classification system for the breast tissue. It produced unbiased estimation of the spatial extent of the lesions. We conclude that nonrigid registration followed by voxel-by-voxel based nonlinear fitting to a realistic two-compartment model yields better quality parametric images, as compared to unprocessed dynamic breast PET time series. By comparison with the mixture model, we established that the total cumulated activity and maximum activity parametric images provide the best delineation of suspicious breast tissue lesions and hyperactive subregions within the lesion that cannot be discerned in unprocessed images.

Original language	English (US)
Title of host publication	Medical Imaging 2008 - Physiology, Function, and Structure from Medical Images
DOIs	https://doi.org/10.1117/12.770984
State	Published - 2008
Event	Medical Imaging 2008 - Physiology, Function, and Structure from Medical Images - San Diego, CA, United States Duration: Feb 17 2008 → Feb 19 2008

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	6916
ISSN (Print)	1605-7422

Other

Other	Medical Imaging 2008 - Physiology, Function, and Structure from Medical Images
Country/Territory	United States
City	San Diego, CA
Period	2/17/08 → 2/19/08

Keywords

Breast lesion segmentation
Curve fitting to two-compartment model
Dynamic breast F-18-FDG-PET/CT
FEM modeling
Mixture model
Nonrigid registration
Parametric images
Patlak model

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Radiology Nuclear Medicine and imaging
Biomaterials

Access to Document

10.1117/12.770984

Cite this

Magri, A, Feiglin, D, Lipson, E, Mandel, J, McGraw, W, Lee, W & Krol, A 2008, Parametric dynamic F-18-FDG PET/CT breast imaging. in Medical Imaging 2008 - Physiology, Function, and Structure from Medical Images., 69161D, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 6916, Medical Imaging 2008 - Physiology, Function, and Structure from Medical Images, San Diego, CA, United States, 2/17/08. https://doi.org/10.1117/12.770984

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title = "Parametric dynamic F-18-FDG PET/CT breast imaging",

abstract = "This study was undertaken to estimate metabolic tissue properties from dynamic breast F-18-FDG PET/CT image series and to display them as 3D parametric images. Each temporal PET series was obtained immediately after injection of 10 mCi of F-18-FDG and consisted of fifty 1- minute frames. Each consecutive frame was nonrigidly registered to the first frame using a finite element method (FEM) based model and fiducial skin markers. Nonlinear curve fitting of activity vs. time based on a realistic two-compartment model was performed for each voxel of the volume. Curve fitting was accomplished by application of the Levenburg-Marquardt algorithm (LMA) that minimized X2. We evaluated which parameters are most suitable to determine the spatial extent and malignancy in suspicious lesions. In addition, Patlak modeling was applied to the data. A mixture model was constructed and provided a classification system for the breast tissue. It produced unbiased estimation of the spatial extent of the lesions. We conclude that nonrigid registration followed by voxel-by-voxel based nonlinear fitting to a realistic two-compartment model yields better quality parametric images, as compared to unprocessed dynamic breast PET time series. By comparison with the mixture model, we established that the total cumulated activity and maximum activity parametric images provide the best delineation of suspicious breast tissue lesions and hyperactive subregions within the lesion that cannot be discerned in unprocessed images.",

keywords = "Breast lesion segmentation, Curve fitting to two-compartment model, Dynamic breast F-18-FDG-PET/CT, FEM modeling, Mixture model, Nonrigid registration, Parametric images, Patlak model",

author = "Alphonso Magri and David Feiglin and Edward Lipson and James Mandel and Wendy McGraw and Wei Lee and Andrzej Krol",

year = "2008",

doi = "10.1117/12.770984",

language = "English (US)",

isbn = "9780819471000",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

booktitle = "Medical Imaging 2008 - Physiology, Function, and Structure from Medical Images",

note = "Medical Imaging 2008 - Physiology, Function, and Structure from Medical Images ; Conference date: 17-02-2008 Through 19-02-2008",

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AU - Magri, Alphonso

AU - Feiglin, David

AU - Lipson, Edward

AU - Mandel, James

AU - McGraw, Wendy

AU - Lee, Wei

AU - Krol, Andrzej

PY - 2008

Y1 - 2008

N2 - This study was undertaken to estimate metabolic tissue properties from dynamic breast F-18-FDG PET/CT image series and to display them as 3D parametric images. Each temporal PET series was obtained immediately after injection of 10 mCi of F-18-FDG and consisted of fifty 1- minute frames. Each consecutive frame was nonrigidly registered to the first frame using a finite element method (FEM) based model and fiducial skin markers. Nonlinear curve fitting of activity vs. time based on a realistic two-compartment model was performed for each voxel of the volume. Curve fitting was accomplished by application of the Levenburg-Marquardt algorithm (LMA) that minimized X2. We evaluated which parameters are most suitable to determine the spatial extent and malignancy in suspicious lesions. In addition, Patlak modeling was applied to the data. A mixture model was constructed and provided a classification system for the breast tissue. It produced unbiased estimation of the spatial extent of the lesions. We conclude that nonrigid registration followed by voxel-by-voxel based nonlinear fitting to a realistic two-compartment model yields better quality parametric images, as compared to unprocessed dynamic breast PET time series. By comparison with the mixture model, we established that the total cumulated activity and maximum activity parametric images provide the best delineation of suspicious breast tissue lesions and hyperactive subregions within the lesion that cannot be discerned in unprocessed images.

AB - This study was undertaken to estimate metabolic tissue properties from dynamic breast F-18-FDG PET/CT image series and to display them as 3D parametric images. Each temporal PET series was obtained immediately after injection of 10 mCi of F-18-FDG and consisted of fifty 1- minute frames. Each consecutive frame was nonrigidly registered to the first frame using a finite element method (FEM) based model and fiducial skin markers. Nonlinear curve fitting of activity vs. time based on a realistic two-compartment model was performed for each voxel of the volume. Curve fitting was accomplished by application of the Levenburg-Marquardt algorithm (LMA) that minimized X2. We evaluated which parameters are most suitable to determine the spatial extent and malignancy in suspicious lesions. In addition, Patlak modeling was applied to the data. A mixture model was constructed and provided a classification system for the breast tissue. It produced unbiased estimation of the spatial extent of the lesions. We conclude that nonrigid registration followed by voxel-by-voxel based nonlinear fitting to a realistic two-compartment model yields better quality parametric images, as compared to unprocessed dynamic breast PET time series. By comparison with the mixture model, we established that the total cumulated activity and maximum activity parametric images provide the best delineation of suspicious breast tissue lesions and hyperactive subregions within the lesion that cannot be discerned in unprocessed images.

KW - Breast lesion segmentation

KW - Curve fitting to two-compartment model

KW - Dynamic breast F-18-FDG-PET/CT

KW - FEM modeling

KW - Mixture model

KW - Nonrigid registration

KW - Parametric images

KW - Patlak model

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T2 - Medical Imaging 2008 - Physiology, Function, and Structure from Medical Images

Y2 - 17 February 2008 through 19 February 2008

ER -

Parametric dynamic F-18-FDG PET/CT breast imaging

Abstract

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Keywords

ASJC Scopus subject areas

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