RESUMEN
BACKGROUND: Novel partial volume correction (PVC) algorithms have been validated by assuming ideal conditions of image processing; however, in real clinical PET studies, the input datasets include error sources which cause error propagation to the corrected outcome. METHODS: We aimed to evaluate error propagations of seven PVCs algorithms for brain PET imaging with [18F]THK-5351 and to discuss the reliability of those algorithms for clinical applications. In order to mimic brain PET imaging of [18F]THK-5351, pseudo-observed SUVR images for one healthy adult and one adult with Alzheimer's disease were simulated from individual PET and MR images. The partial volume effect of pseudo-observed PET images were corrected by using Müller-Gärtner (MG), the geometric transfer matrix (GTM), Labbé (LABBE), regional voxel-based (RBV), iterative Yang (IY), structural functional synergy for resolution recovery (SFS-RR), and modified SFS-RR algorithms with incorporation of error sources in the datasets for PVC processing. Assumed error sources were mismatched FWHM, inaccurate image-registration, and incorrectly segmented anatomical volume. The degree of error propagations in ROI values was evaluated by percent differences (%diff) of PV-corrected SUVR against true SUVR. RESULTS: Uncorrected SUVRs were underestimated against true SUVRs (- 15.7 and - 53.7% in hippocampus for HC and AD conditions), and application of each PVC algorithm reduced the %diff. Larger FWHM mismatch led to larger %diff of PVC-SUVRs against true SUVRs for all algorithms. Inaccurate image registration showed systematic propagation for most algorithms except for SFS-RR and modified SFS-RR. Incorrect segmentation of the anatomical volume only resulted in error propagations in limited local regions. CONCLUSIONS: We demonstrated error propagation by numerical simulation of THK-PET imaging. Error propagations of 7 PVC algorithms for brain PET imaging with [18F]THK-5351 were significant. Robust algorithms for clinical applications must be carefully selected according to the study design of clinical PET data.
RESUMEN
PURPOSE: To suppress partial volume effect (PVE) in brain PET, there have been many algorithms proposed. However, each methodology has different property due to its assumption and algorithms. Our aim of this study was to investigate the difference among partial volume correction (PVC) method for tau and amyloid PET study. METHODS: We investigated two of the most commonly used PVC methods, Müller-Gärtner (MG) and geometric transfer matrix (GTM) and also other three methods for clinical tau and amyloid PET imaging. One healthy control (HC) and one Alzheimer's disease (AD) PET studies of both [18F]THK5351 and [11C]PIB were performed using a Eminence STARGATE scanner (Shimadzu Inc., Kyoto, Japan). All PET images were corrected for PVE by MG, GTM, Labbé (LABBE), Regional voxel-based (RBV), and Iterative Yang (IY) methods, with segmented or parcellated anatomical information processed by FreeSurfer, derived from individual MR images. PVC results of 5 algorithms were compared with the uncorrected data. RESULTS: In regions of high uptake of [18F]THK5351 and [11C]PIB, different PVCs demonstrated different SUVRs. The degree of difference between PVE uncorrected and corrected depends on not only PVC algorithm but also type of tracer and subject condition. CONCLUSION: Presented PVC methods are straight-forward to implement but the corrected images require careful interpretation as different methods result in different levels of recovery.