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
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 (B P ND ), from the LGA using the ordinary least-...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-02-01
|
| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-020-0421-6 |
| _version_ | 1818610157446234112 |
|---|---|
| author | Paulus K. Shigwedha Takahiro Yamada Kohei Hanaoka Kazunari Ishii Yuichi Kimura Yutaka Fukuoka |
| author_facet | Paulus K. Shigwedha Takahiro Yamada Kohei Hanaoka Kazunari Ishii Yuichi Kimura Yutaka Fukuoka |
| author_sort | Paulus K. Shigwedha |
| collection | DOAJ |
| description | 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 (B P 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 B P ND values. Statistical noise was added to these data and the B P ND s 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 B P ND parametric imaging in PET studies. |
| first_indexed | 2024-12-16T15:09:58Z |
| format | Article |
| id | doaj.art-7ce6d331773e439998bfed5c341391a8 |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-12-16T15:09:58Z |
| publishDate | 2020-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-7ce6d331773e439998bfed5c341391a82022-12-21T22:26:59ZengBMCBMC Medical Imaging1471-23422020-02-012011810.1186/s12880-020-0421-6A strategy to account for noise in the X-variable to reduce underestimation in Logan graphical analysis for quantifying receptor density in positron emission tomographyPaulus K. Shigwedha0Takahiro Yamada1Kohei Hanaoka2Kazunari Ishii3Yuichi Kimura4Yutaka Fukuoka5Department of Electrical Engineering and Electronics, Graduate School of Engineering, Kogakuin UniversityDepartment of Computational Systems Biology, Faculty of Biology-Oriented Science and Technology, Kindai UniversityInstitute of Advanced Clinical Medicine, Kindai UniversityDepartment of Radiology, Faculty of Medicine, Kindai UniversityDepartment of Computational Systems Biology, Faculty of Biology-Oriented Science and Technology, Kindai UniversityDepartment of Electrical Engineering and Electronics, Graduate School of Engineering, Kogakuin UniversityAbstract 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 (B P 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 B P ND values. Statistical noise was added to these data and the B P ND s 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 B P ND parametric imaging in PET studies.https://doi.org/10.1186/s12880-020-0421-6Positron emission tomographyLogan graphical analysisReceptor parametric imagingBinding potentialLSC |
| spellingShingle | Paulus K. Shigwedha Takahiro Yamada Kohei Hanaoka Kazunari Ishii Yuichi Kimura Yutaka Fukuoka 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 Medical Imaging Positron emission tomography Logan graphical analysis Receptor parametric imaging Binding potential LSC |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | strategy to account for noise in the x variable to reduce underestimation in logan graphical analysis for quantifying receptor density in positron emission tomography |
| topic | Positron emission tomography Logan graphical analysis Receptor parametric imaging Binding potential LSC |
| url | https://doi.org/10.1186/s12880-020-0421-6 |
| work_keys_str_mv | AT pauluskshigwedha astrategytoaccountfornoiseinthexvariabletoreduceunderestimationinlogangraphicalanalysisforquantifyingreceptordensityinpositronemissiontomography AT takahiroyamada astrategytoaccountfornoiseinthexvariabletoreduceunderestimationinlogangraphicalanalysisforquantifyingreceptordensityinpositronemissiontomography AT koheihanaoka astrategytoaccountfornoiseinthexvariabletoreduceunderestimationinlogangraphicalanalysisforquantifyingreceptordensityinpositronemissiontomography AT kazunariishii astrategytoaccountfornoiseinthexvariabletoreduceunderestimationinlogangraphicalanalysisforquantifyingreceptordensityinpositronemissiontomography AT yuichikimura astrategytoaccountfornoiseinthexvariabletoreduceunderestimationinlogangraphicalanalysisforquantifyingreceptordensityinpositronemissiontomography AT yutakafukuoka astrategytoaccountfornoiseinthexvariabletoreduceunderestimationinlogangraphicalanalysisforquantifyingreceptordensityinpositronemissiontomography AT pauluskshigwedha strategytoaccountfornoiseinthexvariabletoreduceunderestimationinlogangraphicalanalysisforquantifyingreceptordensityinpositronemissiontomography AT takahiroyamada strategytoaccountfornoiseinthexvariabletoreduceunderestimationinlogangraphicalanalysisforquantifyingreceptordensityinpositronemissiontomography AT koheihanaoka strategytoaccountfornoiseinthexvariabletoreduceunderestimationinlogangraphicalanalysisforquantifyingreceptordensityinpositronemissiontomography AT kazunariishii strategytoaccountfornoiseinthexvariabletoreduceunderestimationinlogangraphicalanalysisforquantifyingreceptordensityinpositronemissiontomography AT yuichikimura strategytoaccountfornoiseinthexvariabletoreduceunderestimationinlogangraphicalanalysisforquantifyingreceptordensityinpositronemissiontomography AT yutakafukuoka strategytoaccountfornoiseinthexvariabletoreduceunderestimationinlogangraphicalanalysisforquantifyingreceptordensityinpositronemissiontomography |