Abstract
BackgroundNovel 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.MethodsWe 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.ResultsUncorrected 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.ConclusionsWe 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.
Highlights
Novel partial volume correction (PVC) algorithms have been validated by assuming ideal conditions of image processing; in real clinical Positron emission computed tomography (PET) studies, the input datasets include error sources which cause error propagation to the corrected outcome
The PET images suffer from the partial volume effect (PVE) due to the limited spatial resolution of PET scanners, where regional depiction of the uptake of PET radiopharmaceutical is blurred and its quantitative accuracy is reduced
In real situations of clinical study, discrepancies from the ideal conditions can happen for example due to shift-variant spatial resolution on PET images [4], incorrect segmentation of regions due to parameters used for image processing or distortion of magnetic resonance (MR) images itself, and inaccurate image registration between PET and MR images
Summary
Novel partial volume correction (PVC) algorithms have been validated by assuming ideal conditions of image processing; in real clinical PET studies, the input datasets include error sources which cause error propagation to the corrected outcome. The PET images suffer from the partial volume effect (PVE) due to the limited spatial resolution of PET scanners, where regional depiction of the uptake of PET radiopharmaceutical is blurred and its quantitative accuracy is reduced. Correction of PVE, known as partial volume correction (PVC), is usually performed for PET images by using spread function of blurring and an anatomical prior during post-reconstruction processing or during image reconstruction [1]. The accuracy of post-reconstruction PVC algorithms has been validated under the ideal conditions of correctly assigned point spread function, accurately delineated region contours, and excellent registration of anatomical images with PET images. Even a small amount of discrepancy is likely to influence the output PVE-corrected image, as a result of error propagation [5]
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