Estimating the input function non-invasively for FDG-PET quantification with multiple linear regression analysis: Simulation and verification with in vivo data

Yu Hua Fang, Tsair Kao, Ren Shyan Liu, Liang Chih Wu

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

A novel statistical method, namely Regression-Estimated Input Function (REIF), is proposed in this study for the purpose of non-invasive estimation of the input function for fluorine-18 2-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) quantitative analysis. We collected 44 patients who had undergone a blood sampling procedure during their FDG-PET scans. First, we generated tissue time-activity curves of the grey matter and the whole brain with a segmentation technique for every subject. Summations of different intervals of these two curves were used as a feature vector, which also included the net injection dose. Multiple linear regression analysis was then applied to find the correlation between the input function and the feature vector. After a simulation study with in vivo data, the data of 29 patients were applied to calculate the regression coefficients, which were then used to estimate the input functions of the other 15 subjects. Comparing the estimated input functions with the corresponding real input functions, the averaged error percentages of the area under the curve and the cerebral metabolic rate of glucose (CMRGlc) were 12.13±8.85 and 16.60±9.61, respectively. Regression analysis of the CMRGlc values derived from the real and estimated input functions revealed a high correlation (r=0.91). No significant difference was found between the real CMRGlc and that derived from our regression-estimated input function (Student's t test, P>0.05). The proposed REIF method demonstrated good abilities for input function and CMRGlc estimation, and represents a reliable replacement for the blood sampling procedures in FDG-PET quantification.

Original languageEnglish
Pages (from-to)692-702
Number of pages11
JournalEuropean Journal of Nuclear Medicine and Molecular Imaging
Volume31
Issue number5
DOIs
Publication statusPublished - 2004 May

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Estimating the input function non-invasively for FDG-PET quantification with multiple linear regression analysis: Simulation and verification with in vivo data'. Together they form a unique fingerprint.

Cite this