Impact of image reconstruction settings on texture features in 18F-FDG PET

Jianhua Yan, Jason Lim Chu-Shern, Hoi Yin Loi, Lih Kin Khor, Arvind K. Sinha, Swee Tian Quek, Ivan W K Tham, David Townsend

Research output: Contribution to journalArticle

86 Citations (Scopus)

Abstract

Evaluation of tumor heterogeneity based on texture parameters has recently attracted much interest in the PET imaging community. However, the impact of reconstruction settings on texture parameters is unclear, especially relating to time-offlight and point-spread function modeling. Their effects on 55 texture features (TFs) and 6 features based on first-order statistics (FOS) were investigated. Standardized uptake value (SUV) measures were also evaluated as peak SUV (SUVpeak), maximum SUV, and mean SUV (SUVmean). Methods: This study retrospectively recruited 20 patients with lesions in the lung who underwent whole-body 18F-FDG PET/CT. The coefficient of variation (COV) of each feature across different reconstructions was calculated. Results: SUVpeak, SUVmean, 18 TFs, and 1 FOS were the most robust (COV # 5%) whereas skewness, cluster shade, and zone percentage were the least robust (COV. 20%) with respect to reconstruction algorithms using default settings. Heterogeneity parameters had different sensitivities to iteration number. Twenty-four parameters including SUVpeak and SUVmean exhibited variation with a COV less than 5%; 28 parameters, including maximum SUV, showed variation with a COV in the range of 5%-10%. In addition, skewness, cluster shade, and zone percentage were the most sensitive to iteration number. In terms of sensitivity to full width at half maximum (FWHM), 15 TFs and 1 FOS had the best performance with a COV less than 5%, whereas SUVpeak and SUVmean had a COV between 5% and 10%. Grid size had the largest impact on image features, which was demonstrated by only 11 features, including SUVpeak and SUVmean, having a COV less than 10%. Conclusion: Different image features have different sensitivities to reconstruction settings. Iteration number and FWHM of the Gaussian filter have a similar impact on the image features. Grid size has a larger impact on the features than iteration number and FWHM. The features that exhibited large variations such as skewness in FOS, cluster shade, and zone percentage should be used with caution. The entropy in FOS, difference entropy, inverse difference normalized, inverse difference moment normalized, low gray-level run emphasis, high gray-level run emphasis, and low gray-level zone emphasis are the most robust features.

Original languageEnglish
Pages (from-to)1667-1673
Number of pages7
JournalJournal of Nuclear Medicine
Volume56
Issue number11
DOIs
Publication statusPublished - 1 Nov 2015
Externally publishedYes

Fingerprint

Computer-Assisted Image Processing
Fluorodeoxyglucose F18
Entropy
Lung
Neoplasms

Keywords

  • F-FDG PET
  • Point-spread function
  • Time-of-flight
  • Tumor texture

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Yan, J., Chu-Shern, J. L., Loi, H. Y., Khor, L. K., Sinha, A. K., Quek, S. T., ... Townsend, D. (2015). Impact of image reconstruction settings on texture features in 18F-FDG PET. Journal of Nuclear Medicine, 56(11), 1667-1673. https://doi.org/10.2967/jnumed.115.156927

Impact of image reconstruction settings on texture features in 18F-FDG PET. / Yan, Jianhua; Chu-Shern, Jason Lim; Loi, Hoi Yin; Khor, Lih Kin; Sinha, Arvind K.; Quek, Swee Tian; Tham, Ivan W K; Townsend, David.

In: Journal of Nuclear Medicine, Vol. 56, No. 11, 01.11.2015, p. 1667-1673.

Research output: Contribution to journalArticle

Yan, J, Chu-Shern, JL, Loi, HY, Khor, LK, Sinha, AK, Quek, ST, Tham, IWK & Townsend, D 2015, 'Impact of image reconstruction settings on texture features in 18F-FDG PET', Journal of Nuclear Medicine, vol. 56, no. 11, pp. 1667-1673. https://doi.org/10.2967/jnumed.115.156927
Yan J, Chu-Shern JL, Loi HY, Khor LK, Sinha AK, Quek ST et al. Impact of image reconstruction settings on texture features in 18F-FDG PET. Journal of Nuclear Medicine. 2015 Nov 1;56(11):1667-1673. https://doi.org/10.2967/jnumed.115.156927
Yan, Jianhua ; Chu-Shern, Jason Lim ; Loi, Hoi Yin ; Khor, Lih Kin ; Sinha, Arvind K. ; Quek, Swee Tian ; Tham, Ivan W K ; Townsend, David. / Impact of image reconstruction settings on texture features in 18F-FDG PET. In: Journal of Nuclear Medicine. 2015 ; Vol. 56, No. 11. pp. 1667-1673.
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abstract = "Evaluation of tumor heterogeneity based on texture parameters has recently attracted much interest in the PET imaging community. However, the impact of reconstruction settings on texture parameters is unclear, especially relating to time-offlight and point-spread function modeling. Their effects on 55 texture features (TFs) and 6 features based on first-order statistics (FOS) were investigated. Standardized uptake value (SUV) measures were also evaluated as peak SUV (SUVpeak), maximum SUV, and mean SUV (SUVmean). Methods: This study retrospectively recruited 20 patients with lesions in the lung who underwent whole-body 18F-FDG PET/CT. The coefficient of variation (COV) of each feature across different reconstructions was calculated. Results: SUVpeak, SUVmean, 18 TFs, and 1 FOS were the most robust (COV # 5{\%}) whereas skewness, cluster shade, and zone percentage were the least robust (COV. 20{\%}) with respect to reconstruction algorithms using default settings. Heterogeneity parameters had different sensitivities to iteration number. Twenty-four parameters including SUVpeak and SUVmean exhibited variation with a COV less than 5{\%}; 28 parameters, including maximum SUV, showed variation with a COV in the range of 5{\%}-10{\%}. In addition, skewness, cluster shade, and zone percentage were the most sensitive to iteration number. In terms of sensitivity to full width at half maximum (FWHM), 15 TFs and 1 FOS had the best performance with a COV less than 5{\%}, whereas SUVpeak and SUVmean had a COV between 5{\%} and 10{\%}. Grid size had the largest impact on image features, which was demonstrated by only 11 features, including SUVpeak and SUVmean, having a COV less than 10{\%}. Conclusion: Different image features have different sensitivities to reconstruction settings. Iteration number and FWHM of the Gaussian filter have a similar impact on the image features. Grid size has a larger impact on the features than iteration number and FWHM. The features that exhibited large variations such as skewness in FOS, cluster shade, and zone percentage should be used with caution. The entropy in FOS, difference entropy, inverse difference normalized, inverse difference moment normalized, low gray-level run emphasis, high gray-level run emphasis, and low gray-level zone emphasis are the most robust features.",
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