Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters.
BACKGROUND
Characterization of textural features (spatial distributions of image intensity levels) has been considered as a tool for automatic tumor segmentation. The purpose of this work is to study the variability of the textural features in PET images due to different acquisition modes and reconstruction parameters.


METHODS
Twenty patients with solid tumors underwent PET/CT scans on a GE Discovery VCT scanner, 45-60 minutes post-injection of 10 mCi of [(18)F]FDG. Scans were acquired in both 2D and 3D modes. For each acquisition the raw PET data was reconstructed using five different reconstruction parameters. Lesions were segmented on a default image using the threshold of 40% of maximum SUV. Fifty different texture features were calculated inside the tumors. The range of variations of the features were calculated with respect to the average value.


RESULTS
Fifty textural features were classified based on the range of variation in three categories: small, intermediate and large variability. Features with small variability (range ≤ 5%) were entropy-first order, energy, maximal correlation coefficient (second order feature) and low-gray level run emphasis (high-order feature). The features with intermediate variability (10% ≤ range ≤ 25%) were entropy-GLCM, sum entropy, high gray level run emphsis, gray level non-uniformity, small number emphasis, and entropy-NGL. Forty remaining features presented large variations (range > 30%).


CONCLUSIONS
Textural features such as entropy-first order, energy, maximal correlation coefficient, and low-gray level run emphasis exhibited small variations due to different acquisition modes and reconstruction parameters. Features with low level of variations are better candidates for reproducible tumor segmentation. Even though features such as contrast-NGTD, coarseness, homogeneity, and busyness have been previously used, our data indicated that these features presented large variations, therefore they could not be considered as a good candidates for tumor segmentation.
Galavis
Paulina E
PE
Department of Medical Physics, University of Wisconsin, Madison, WI, USA. galavis@wisc.edu
Hollensen
Christian
C
Jallow
Ngoneh
N
Paliwal
Bhudatt
B
Jeraj
Robert
R
D003951:Diagnostic Errors / Q000706:statistics & numerical data; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods / Q000592:standards; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000592:standards / Q000706:statistics & numerical data; D009369:Neoplasms / Q000000981:diagnostic imaging*; D015588:Observer Variation; D049268:Positron-Emission Tomography / Q000379:methods* / Q000592:standards; D012189:Retrospective Studies; D014055:Tomography, Emission-Computed / Q000379:methods
D019788:Fluorodeoxyglucose F18