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Published on: December 19, 2017
Study of an image-derived SUV and a modified SUV using mouse FDG-PET
Xiujuan Zheng1, Chin-Lung Yu, Wei Sha
1Department of Electronic Information Engineering, the Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. zhengxj@eie.polyu.edu.hk
This study introduces image-derived SUV (iSUV) and modified SUV (mSUV) to improve accuracy in 2-deoxy-2-[18F]fluoro-D-glucose (FDG) PET imaging. These methods overcome limitations of standard SUV, enhancing tumor detection in mice.
Area of Science:
- Nuclear medicine
- Preclinical imaging
- Radiochemistry
Background:
- Standard uptake value (SUV) in 2-deoxy-2-[18F]fluoro-D-glucose (FDG) Positron Emission Tomography (PET) is susceptible to variability from factors like renal function and measurement errors.
- Current SUV calculations do not account for plasma FDG clearance, leading to potential inaccuracies in assessing metabolic activity.
Purpose of the Study:
- To develop and validate an image-based method for calculating an image-derived SUV (iSUV) and a modified SUV (mSUV).
- To overcome the limitations of standard SUV measurements in preclinical FDG-PET studies, particularly in mouse models.
Main Methods:
- Utilized micro-PET/CT imaging in 31 tumor-bearing SCID mice approximately 60 minutes post-FDG injection.
- Derived body weight and injected dose from microPET/CT images to calculate iSUV.
- Quantified FDG volumes in the bladder and whole body from images to compute mSUV, comparing both iSUV and mSUV against standard SUV.
Main Results:
- The iSUV factor demonstrated a low average percentage error of -0.7% compared to the standard SUV factor.
- Linear regression showed a strong correlation between SUV and iSUV (slope=0.99, R=0.95).
- The mSUV exhibited reduced coefficient of variation and improved tumor-to-background separation compared to both SUV and iSUV.
Conclusions:
- The image-based method allows iSUV to serve as a reliable alternative to standard SUV when direct measurements are unavailable or inaccurate.
- mSUV effectively minimizes inter-subject variability and enhances the distinction between tumors and background tissues in mouse FDG-PET imaging.
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