The effect of 18F-FDG-PET image reconstruction algorithms on the expression of characteristic metabolic brain network in Parkinson’s disease

Abstract

PURPOSE To evaluate the reproducibility of the expression of Parkinson’s Disease Related Pattern (PDRP) across multiple sets of 18F-FDG-PET brain images reconstructed with different reconstruction algorithms. METHODS 18F-FDG-PET brain imaging was performed in two independent cohorts of Parkinson’s disease (PD) patients and normal controls (NC). Slovenian cohort (20 PD patients, 20 NC) was scanned with Siemens Biograph mCT camera and reconstructed using FBP, FBP+TOF, OSEM, OSEM+TOF, OSEM+PSF and OSEM+PSF+TOF. American Cohort (20 PD patients, 7 NC) was scanned with GE Advance camera and reconstructed using 3DRP, FORE-FBP and FORE-Iterative. Expressions of two previously-validated PDRP patterns (PDRP-Slovenia and PDRP-USA) were calculated. We compared the ability of PDRP to discriminate PD patients from NC, differences and correlation between the corresponding subject scores and ROC analysis results across the different reconstruction algorithms. RESULTS The expression of PDRP-Slovenia and PDRP-USA networks was significantly elevated in PD patients compared to NC (p<0.0001), regardless of reconstruction algorithms. PDRP expression strongly correlated between all studied algorithms and the reference algorithm (r≥0.993, p<0.0001). Average differences in the PDRP expression among different algorithms varied within 0.73 and 0.08 of the reference value for PDRP-Slovenia and PDRP-USA, respectively. ROC analysis confirmed high similarity in sensitivity, specificity and AUC among all studied reconstruction algorithms. CONCLUSIONS These results show that the expression of PDRP is reproducible across a variety of reconstruction algorithms of 18F-FDG-PET brain images. PDRP is capable of providing a robust metabolic biomarker of PD for multicenter 18F-FDG-PET images acquired in the context of differential diagnosis or clinical trials.

Abnormal pattern of brain glucose metabolism in Parkinson’s disease: replication in three European cohorts

“…In this study, we compared data from different centers. It is well-known that variations in PET scanners and image reconstruction algorithms influence disease-related pattern scores [53][54][55] (supplementary Fig 1). In support of this, we recently identified clear center-specific features in the current data using machine-learning algorithms [56].…”

Factors affecting the harmonization of disease‐related metabolic brain pattern expression quantification in [18F]FDG‐PET (PETMETPAT)

“…However, the widespread implementation of such SSM/PCA-derived disease-related metabolic brain patterns in multicenter collaborations and clinical practice has been hindered by differences between PET scanners as well as acquisition and reconstruction protocols. Variations in scanners and image reconstruction algorithms have been shown to systematically shift image quality and disease-related pattern subject scores [6] , [24] , [25] , [26] .…”

“…It was found that PDRP topography was highly reproducible across FDG‐PET reconstruction algorithms. In addition, within each type of reconstruction per scanner, discrimination between patients and HCs was not significantly impacted when using disease‐related patterns derived using the same reconstruction method, whereas calibration with HCs was advised when different methods were used [25,26]. However, the impact of additional variables on PDRP expression scores, such as histogram glucose uptake intensity distribution across voxels, as well as image contrast, has not yet been assessed.…”

1st European Congress of Medical Physics September 1–4, 2016; Medical Physics innovation and vision within Europe and beyond

“…Finally, Tomse P. et al [25] evaluated the reproducibility of the expression of Parkinson's Disease Related Pattern (PDRP) across multiple sets of 18F-FDG-PET brain images reconstructed with different reconstruction algorithms. Their results showed that the expression of PDRP was reproducible across a variety of reconstruction algorithms of 18 F-FDG-PET brain images and so PDRP is capable of providing a robust metabolic biomarker of Parkinson's disease for multi centre 18 F-FDG-PET images acquired in the context of differential diagnosis or clinical trials.…”