TY - JOUR
T1 - Error propagation analysis of seven partial volume correction algorithms for [18F]THK-5351 brain PET imaging
AU - Oyama, Senri
AU - Hosoi, Ayumu
AU - Ibaraki, Masanobu
AU - McGinnity, Colm J.
AU - Matsubara, Keisuke
AU - Watanuki, Shoichi
AU - Watabe, Hiroshi
AU - Tashiro, Manabu
AU - Shidahara, Miho
N1 - Funding Information:
This study was supported in part by Grants-in-Aid for Scientific Research (C) (No. 15 K08687) and (B) (No 17H04118) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japanese Government. CJM was supported by the Wellcome/EPSRC Centre for Medical Engineering (WT 203148/Z/16/Z) and the EPSRC Centre for Doctoral Training in Medical Imaging (EP/L015226/1; both based in the UK).
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
KW - Brain PET
KW - Error-propagation analysis
KW - Partial volume correction
KW - [F]THK-5351
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U2 - 10.1186/s40658-020-00324-9
DO - 10.1186/s40658-020-00324-9
M3 - Article
AN - SCOPUS:85090914581
VL - 7
JO - EJNMMI Physics
JF - EJNMMI Physics
SN - 2197-7364
IS - 1
M1 - 57
ER -