An adaptive thresholding method for BTV estimation incorporating PET reconstruction parameters: A multicenter study of the robustness and the reliability

M. Brambilla, R. Matheoud, C. Basile, C. Bracco, I. Castiglioni, C. Cavedon, M. Cremonesi, S. Morzenti, F. Fioroni, M. Giri, F. Botta, F. Gallivanone, E. Grassi, M. Pacilio, E. De Ponti, M. Stasi, S. Pasetto, S. Valzano, D. Zanni

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Objective. The aim of this work was to assess robustness and reliability of an adaptive thresholding algorithm for the biological target volume estimation incorporating reconstruction parameters. Method. In a multicenter study, a phantom with spheres of different diameters (6.5-57.4 mm) was filled with 18F-FDG at different target-to-background ratios (TBR: 2.5-70) and scanned for different acquisition periods (2-5 min). Image reconstruction algorithms were used varying number of iterations and postreconstruction transaxial smoothing. Optimal thresholds (TS) for volume estimation were determined as percentage of the maximum intensity in the cross section area of the spheres. Multiple regression techniques were used to identify relevant predictors of TS. Results. The goodness of the model fit was high (R2: 0.74-0.92). TBR was the most significant predictor of TS. For all scanners, except the Gemini scanners, FWHM was an independent predictor of TS. Significant differences were observed between scanners of different models, but not between different scanners of the same model. The shrinkage on cross validation was small and indicative of excellent reliability of model estimation. Conclusions. Incorporation of postreconstruction filtering FWHM in an adaptive thresholding algorithm for the BTV estimation allows obtaining a robust and reliable method to be applied to a variety of different scanners, without scanner-specific individual calibration.

Original languageEnglish
Article number571473
JournalComputational and Mathematical Methods in Medicine
Volume2015
DOIs
Publication statusPublished - 2015

Fingerprint

Adaptive Thresholding
Scanner
Multicenter Studies
Robustness
Adaptive algorithms
Full width at half maximum
Predictors
Computer-Assisted Image Processing
Fluorodeoxyglucose F18
Calibration
Image reconstruction
Target
Multiple Regression
Reconstruction Algorithm
Image Reconstruction
Phantom
Shrinkage
Cross-validation
Model
Percentage

ASJC Scopus subject areas

  • Applied Mathematics
  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Medicine(all)
  • Immunology and Microbiology(all)

Cite this

An adaptive thresholding method for BTV estimation incorporating PET reconstruction parameters : A multicenter study of the robustness and the reliability. / Brambilla, M.; Matheoud, R.; Basile, C.; Bracco, C.; Castiglioni, I.; Cavedon, C.; Cremonesi, M.; Morzenti, S.; Fioroni, F.; Giri, M.; Botta, F.; Gallivanone, F.; Grassi, E.; Pacilio, M.; De Ponti, E.; Stasi, M.; Pasetto, S.; Valzano, S.; Zanni, D.

In: Computational and Mathematical Methods in Medicine, Vol. 2015, 571473, 2015.

Research output: Contribution to journalArticle

Brambilla, M, Matheoud, R, Basile, C, Bracco, C, Castiglioni, I, Cavedon, C, Cremonesi, M, Morzenti, S, Fioroni, F, Giri, M, Botta, F, Gallivanone, F, Grassi, E, Pacilio, M, De Ponti, E, Stasi, M, Pasetto, S, Valzano, S & Zanni, D 2015, 'An adaptive thresholding method for BTV estimation incorporating PET reconstruction parameters: A multicenter study of the robustness and the reliability', Computational and Mathematical Methods in Medicine, vol. 2015, 571473. https://doi.org/10.1155/2015/571473
Brambilla, M. ; Matheoud, R. ; Basile, C. ; Bracco, C. ; Castiglioni, I. ; Cavedon, C. ; Cremonesi, M. ; Morzenti, S. ; Fioroni, F. ; Giri, M. ; Botta, F. ; Gallivanone, F. ; Grassi, E. ; Pacilio, M. ; De Ponti, E. ; Stasi, M. ; Pasetto, S. ; Valzano, S. ; Zanni, D. / An adaptive thresholding method for BTV estimation incorporating PET reconstruction parameters : A multicenter study of the robustness and the reliability. In: Computational and Mathematical Methods in Medicine. 2015 ; Vol. 2015.
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abstract = "Objective. The aim of this work was to assess robustness and reliability of an adaptive thresholding algorithm for the biological target volume estimation incorporating reconstruction parameters. Method. In a multicenter study, a phantom with spheres of different diameters (6.5-57.4 mm) was filled with 18F-FDG at different target-to-background ratios (TBR: 2.5-70) and scanned for different acquisition periods (2-5 min). Image reconstruction algorithms were used varying number of iterations and postreconstruction transaxial smoothing. Optimal thresholds (TS) for volume estimation were determined as percentage of the maximum intensity in the cross section area of the spheres. Multiple regression techniques were used to identify relevant predictors of TS. Results. The goodness of the model fit was high (R2: 0.74-0.92). TBR was the most significant predictor of TS. For all scanners, except the Gemini scanners, FWHM was an independent predictor of TS. Significant differences were observed between scanners of different models, but not between different scanners of the same model. The shrinkage on cross validation was small and indicative of excellent reliability of model estimation. Conclusions. Incorporation of postreconstruction filtering FWHM in an adaptive thresholding algorithm for the BTV estimation allows obtaining a robust and reliable method to be applied to a variety of different scanners, without scanner-specific individual calibration.",
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AU - Brambilla, M.

AU - Matheoud, R.

AU - Basile, C.

AU - Bracco, C.

AU - Castiglioni, I.

AU - Cavedon, C.

AU - Cremonesi, M.

AU - Morzenti, S.

AU - Fioroni, F.

AU - Giri, M.

AU - Botta, F.

AU - Gallivanone, F.

AU - Grassi, E.

AU - Pacilio, M.

AU - De Ponti, E.

AU - Stasi, M.

AU - Pasetto, S.

AU - Valzano, S.

AU - Zanni, D.

PY - 2015

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N2 - Objective. The aim of this work was to assess robustness and reliability of an adaptive thresholding algorithm for the biological target volume estimation incorporating reconstruction parameters. Method. In a multicenter study, a phantom with spheres of different diameters (6.5-57.4 mm) was filled with 18F-FDG at different target-to-background ratios (TBR: 2.5-70) and scanned for different acquisition periods (2-5 min). Image reconstruction algorithms were used varying number of iterations and postreconstruction transaxial smoothing. Optimal thresholds (TS) for volume estimation were determined as percentage of the maximum intensity in the cross section area of the spheres. Multiple regression techniques were used to identify relevant predictors of TS. Results. The goodness of the model fit was high (R2: 0.74-0.92). TBR was the most significant predictor of TS. For all scanners, except the Gemini scanners, FWHM was an independent predictor of TS. Significant differences were observed between scanners of different models, but not between different scanners of the same model. The shrinkage on cross validation was small and indicative of excellent reliability of model estimation. Conclusions. Incorporation of postreconstruction filtering FWHM in an adaptive thresholding algorithm for the BTV estimation allows obtaining a robust and reliable method to be applied to a variety of different scanners, without scanner-specific individual calibration.

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