A strategy to account for noise in the X-variable to reduce underestimation in Logan graphical analysis for quantifying receptor density in positron emission tomography

Paulus K Shigwedha; Takahiro Yamada; Kohei Hanaoka; Kazunari Ishii; Yuichi Kimura; Yutaka Fukuoka

doi:10.1186/s12880-020-0421-6

A strategy to account for noise in the X-variable to reduce underestimation in Logan graphical analysis for quantifying receptor density in positron emission tomography

BMC Med Imaging. 2020 Feb 10;20(1):15. doi: 10.1186/s12880-020-0421-6.

Authors

Paulus K Shigwedha¹, Takahiro Yamada², Kohei Hanaoka³, Kazunari Ishii⁴, Yuichi Kimura², Yutaka Fukuoka⁵

Affiliations

¹ Department of Electrical Engineering and Electronics, Graduate School of Engineering, Kogakuin University, Shinjuku, Tokyo, Japan.
² Department of Computational Systems Biology, Faculty of Biology-Oriented Science and Technology, Kindai University, Kinokawa, Wakayama, Japan.
³ Institute of Advanced Clinical Medicine, Kindai University, Osakasayama, Osaka, Japan.
⁴ Department of Radiology, Faculty of Medicine, Kindai University, Osakasayama, Osaka, Japan.
⁵ Department of Electrical Engineering and Electronics, Graduate School of Engineering, Kogakuin University, Shinjuku, Tokyo, Japan. fukuoka@cc.kogakuin.ac.jp.

Abstract

Background: The Logan graphical analysis (LGA) algorithm is widely used to quantify receptor density for parametric imaging in positron emission tomography (PET). Estimating receptor density, in terms of the non-displaceable binding potential (BP_ND), from the LGA using the ordinary least-squares (OLS) method has been found to be negatively biased owing to noise in PET data. This is because OLS does not consider errors in the X-variable (predictor variable). Existing bias reduction methods can either only reduce the bias slightly or reduce the bias accompanied by increased variation in the estimates. In this study, we addressed the bias reduction problem by applying a different regression method.

Methods: We employed least-squares cubic (LSC) linear regression, which accounts for errors in both variables as well as the correlation of these errors. Noise-free PET data were simulated, for ¹¹C-carfentanil kinetics, with known BP_ND values. Statistical noise was added to these data and the BP_NDs were re-estimated from the noisy data by three methods, conventional LGA, multilinear reference tissue model 2 (MRTM2), and LSC-based LGA; the results were compared. The three methods were also compared in terms of beta amyloid (A β) quantification of ¹¹C-Pittsburgh compound B brain PET data for two patients with Alzheimer's disease and differing A β depositions.

Results: Amongst the three methods, for both synthetic and actual data, LSC was the least biased, followed by MRTM2, and then the conventional LGA, which was the most biased. Variations in the LSC estimates were smaller than those in the MRTM2 estimates. LSC also required a shorter computational time than MRTM2.

Conclusions: The results suggest that LSC provides a better trade-off between the bias and variability than the other two methods. In particular, LSC performed better than MRTM2 in all aspects; bias, variability, and computational time. This makes LSC a promising method for BP_ND parametric imaging in PET studies.

Keywords: Binding potential; LSC; Logan graphical analysis; Positron emission tomography; Receptor parametric imaging.

MeSH terms

Algorithms
Alzheimer Disease / diagnostic imaging*
Alzheimer Disease / metabolism
Amyloid beta-Peptides / chemistry*
Amyloid beta-Peptides / metabolism
Bias
Brain / diagnostic imaging
Brain / metabolism
Carbon Isotopes / chemistry
Carbon Isotopes / pharmacokinetics*
Fentanyl / analogs & derivatives
Fentanyl / chemistry
Humans
Least-Squares Analysis
Positron-Emission Tomography
Radiographic Image Interpretation, Computer-Assisted / methods*
Signal-To-Noise Ratio

Substances

Amyloid beta-Peptides
Carbon Isotopes
carfentanil
Fentanyl