Spillover and partial-volume correction for image-derived input functions for small-animal 18F-FDG PET studies

Yu-Hua Dean Fang, Raymond F. Muzic

Research output: Contribution to journalArticle

79 Citations (Scopus)

Abstract

We present and validate a method to obtain an input function from dynamic image data and 0 or 1 blood sample for small-animal 18F-FDG PET studies. The method accounts for spillover and partial-volume effects via a physiologic model to yield a model-corrected input function (MCIF). Methods: Image-derived input functions (IDIFs) from heart ventricles and myocardial time-activity curves were obtained from 14 Sprague-Dawley rats and 17 C57BL/6 mice. Each MCIF was expressed as amathematic equation with 7 parameters, which were estimated simultaneously with the myocardial model parameters by fitting the IDIFs and myocardium curves to a dual-output compartment model. Zero or 1 late blood sample was used in the simultaneous estimation. MCIF was validated by comparison with input measured from blood samples. Validation included computing errors in the areas under the curves (AUCs) and in the 18F-FDG influx constant Ki in 3 types of tissue. Results: For the rat data, the AUC error was 5.3% ± 19.0% in the 0-sample MCIF and -2.3% ± 14.8% in the 1-sample MCIF. When the MCIF was used to calculate the Ki of the myocardium, brain, and muscle, the overall errors were -6.3% ± 27.0% in the 0-sample method (correlation coefficient r = 0.967) and 3.1% ± 20.6% in the 1-sample method (r = 0.970). The t test failed to detect a significant difference (P > 0.05) in the Ki estimates from both the 0-sample and the 1-sample MCIF. For the mouse data, AUC errors were 4.3% ± 25.5% in the 0-sample MCIF and -1.7% ± 20.9% in the 1-sample MCIF. Ki errors averaged -8.0% ± 27.6% for the 0-sample method (r = 0.955) and -2.8% ± 22.7% for the 1-sample method (r = 0.971). The t test detected significant differences in the brain and muscle in the Ki for the 0-sample method but no significant differences with the 1-sample method. In both rat and mouse, 0-sample and 1-sample MCIFs both showed at least a 10-fold reduction in AUC and Ki errors compared with uncorrected IDIFs. Conclusion: MCIF provides a reliable, noninvasive estimate of the input function that can be used to accurately quantify the glucose metabolic rate in small-animal 18F-FDG PET studies. COPYRIGHT

Original languageEnglish
Pages (from-to)606-614
Number of pages9
JournalJournal of Nuclear Medicine
Volume49
Issue number4
DOIs
Publication statusPublished - 2008 Apr 1

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Fluorodeoxyglucose F18
Area Under Curve
Myocardium
Muscles
Brain
Inbred C57BL Mouse
Heart Ventricles
Sprague Dawley Rats
Glucose

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

Cite this

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title = "Spillover and partial-volume correction for image-derived input functions for small-animal 18F-FDG PET studies",
abstract = "We present and validate a method to obtain an input function from dynamic image data and 0 or 1 blood sample for small-animal 18F-FDG PET studies. The method accounts for spillover and partial-volume effects via a physiologic model to yield a model-corrected input function (MCIF). Methods: Image-derived input functions (IDIFs) from heart ventricles and myocardial time-activity curves were obtained from 14 Sprague-Dawley rats and 17 C57BL/6 mice. Each MCIF was expressed as amathematic equation with 7 parameters, which were estimated simultaneously with the myocardial model parameters by fitting the IDIFs and myocardium curves to a dual-output compartment model. Zero or 1 late blood sample was used in the simultaneous estimation. MCIF was validated by comparison with input measured from blood samples. Validation included computing errors in the areas under the curves (AUCs) and in the 18F-FDG influx constant Ki in 3 types of tissue. Results: For the rat data, the AUC error was 5.3{\%} ± 19.0{\%} in the 0-sample MCIF and -2.3{\%} ± 14.8{\%} in the 1-sample MCIF. When the MCIF was used to calculate the Ki of the myocardium, brain, and muscle, the overall errors were -6.3{\%} ± 27.0{\%} in the 0-sample method (correlation coefficient r = 0.967) and 3.1{\%} ± 20.6{\%} in the 1-sample method (r = 0.970). The t test failed to detect a significant difference (P > 0.05) in the Ki estimates from both the 0-sample and the 1-sample MCIF. For the mouse data, AUC errors were 4.3{\%} ± 25.5{\%} in the 0-sample MCIF and -1.7{\%} ± 20.9{\%} in the 1-sample MCIF. Ki errors averaged -8.0{\%} ± 27.6{\%} for the 0-sample method (r = 0.955) and -2.8{\%} ± 22.7{\%} for the 1-sample method (r = 0.971). The t test detected significant differences in the brain and muscle in the Ki for the 0-sample method but no significant differences with the 1-sample method. In both rat and mouse, 0-sample and 1-sample MCIFs both showed at least a 10-fold reduction in AUC and Ki errors compared with uncorrected IDIFs. Conclusion: MCIF provides a reliable, noninvasive estimate of the input function that can be used to accurately quantify the glucose metabolic rate in small-animal 18F-FDG PET studies. COPYRIGHT",
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Spillover and partial-volume correction for image-derived input functions for small-animal 18F-FDG PET studies. / Fang, Yu-Hua Dean; Muzic, Raymond F.

In: Journal of Nuclear Medicine, Vol. 49, No. 4, 01.04.2008, p. 606-614.

Research output: Contribution to journalArticle

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N2 - We present and validate a method to obtain an input function from dynamic image data and 0 or 1 blood sample for small-animal 18F-FDG PET studies. The method accounts for spillover and partial-volume effects via a physiologic model to yield a model-corrected input function (MCIF). Methods: Image-derived input functions (IDIFs) from heart ventricles and myocardial time-activity curves were obtained from 14 Sprague-Dawley rats and 17 C57BL/6 mice. Each MCIF was expressed as amathematic equation with 7 parameters, which were estimated simultaneously with the myocardial model parameters by fitting the IDIFs and myocardium curves to a dual-output compartment model. Zero or 1 late blood sample was used in the simultaneous estimation. MCIF was validated by comparison with input measured from blood samples. Validation included computing errors in the areas under the curves (AUCs) and in the 18F-FDG influx constant Ki in 3 types of tissue. Results: For the rat data, the AUC error was 5.3% ± 19.0% in the 0-sample MCIF and -2.3% ± 14.8% in the 1-sample MCIF. When the MCIF was used to calculate the Ki of the myocardium, brain, and muscle, the overall errors were -6.3% ± 27.0% in the 0-sample method (correlation coefficient r = 0.967) and 3.1% ± 20.6% in the 1-sample method (r = 0.970). The t test failed to detect a significant difference (P > 0.05) in the Ki estimates from both the 0-sample and the 1-sample MCIF. For the mouse data, AUC errors were 4.3% ± 25.5% in the 0-sample MCIF and -1.7% ± 20.9% in the 1-sample MCIF. Ki errors averaged -8.0% ± 27.6% for the 0-sample method (r = 0.955) and -2.8% ± 22.7% for the 1-sample method (r = 0.971). The t test detected significant differences in the brain and muscle in the Ki for the 0-sample method but no significant differences with the 1-sample method. In both rat and mouse, 0-sample and 1-sample MCIFs both showed at least a 10-fold reduction in AUC and Ki errors compared with uncorrected IDIFs. Conclusion: MCIF provides a reliable, noninvasive estimate of the input function that can be used to accurately quantify the glucose metabolic rate in small-animal 18F-FDG PET studies. COPYRIGHT

AB - We present and validate a method to obtain an input function from dynamic image data and 0 or 1 blood sample for small-animal 18F-FDG PET studies. The method accounts for spillover and partial-volume effects via a physiologic model to yield a model-corrected input function (MCIF). Methods: Image-derived input functions (IDIFs) from heart ventricles and myocardial time-activity curves were obtained from 14 Sprague-Dawley rats and 17 C57BL/6 mice. Each MCIF was expressed as amathematic equation with 7 parameters, which were estimated simultaneously with the myocardial model parameters by fitting the IDIFs and myocardium curves to a dual-output compartment model. Zero or 1 late blood sample was used in the simultaneous estimation. MCIF was validated by comparison with input measured from blood samples. Validation included computing errors in the areas under the curves (AUCs) and in the 18F-FDG influx constant Ki in 3 types of tissue. Results: For the rat data, the AUC error was 5.3% ± 19.0% in the 0-sample MCIF and -2.3% ± 14.8% in the 1-sample MCIF. When the MCIF was used to calculate the Ki of the myocardium, brain, and muscle, the overall errors were -6.3% ± 27.0% in the 0-sample method (correlation coefficient r = 0.967) and 3.1% ± 20.6% in the 1-sample method (r = 0.970). The t test failed to detect a significant difference (P > 0.05) in the Ki estimates from both the 0-sample and the 1-sample MCIF. For the mouse data, AUC errors were 4.3% ± 25.5% in the 0-sample MCIF and -1.7% ± 20.9% in the 1-sample MCIF. Ki errors averaged -8.0% ± 27.6% for the 0-sample method (r = 0.955) and -2.8% ± 22.7% for the 1-sample method (r = 0.971). The t test detected significant differences in the brain and muscle in the Ki for the 0-sample method but no significant differences with the 1-sample method. In both rat and mouse, 0-sample and 1-sample MCIFs both showed at least a 10-fold reduction in AUC and Ki errors compared with uncorrected IDIFs. Conclusion: MCIF provides a reliable, noninvasive estimate of the input function that can be used to accurately quantify the glucose metabolic rate in small-animal 18F-FDG PET studies. COPYRIGHT

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