Partial volume correction for PET quantification and its impact on brain network in Alzheimer’s disease

Yang, Jiarui; Hu, Chenhui; Guo, Ning; Dutta, Joyita; Vaina, Lucia M.; Johnson, Keith A.; Sepulcre, Jorge; Fakhri, Georges El; Li, Quanzheng

doi:10.1038/s41598-017-13339-7

Cited by 43 publications

(42 citation statements)

References 63 publications

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“…However, in many cases, the applied PVC methods were limited to the classical and popular Müller-Gärtner (MG) [ 19 ] and geometric transfer matrix (GTM) methods [ 20 , 21 ] and similar algorithms. Recently, cross-sectional or longitudinal, brain network big data analysis of a PET database combined with PVC pipeline processing has been introduced into the PET field [ 18 , 22 ]; however, the automatic processing pipeline has a potential to include and propagate errors accidentally resulting in incorrect conclusions. It is obvious that careful processing can prevent error propagations.…”

Section: Introductionmentioning

confidence: 99%

Error propagation analysis of seven partial volume correction algorithms for [18F]THK-5351 brain PET imaging

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

Error propagation analysis of seven partial volume correction algorithms for [18F]THK-5351 brain PET imaging

et al. 2020

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“…In Figure 10, MTE has more variation of information between all the channels compared to GCA. The colors correspond to a variation of information between the regions or channels (Van Den Heuvel and Fornito, 2014;Yang et al, 2017). GCA has the following results for its out degrees for HCs and SCZ patients, respectively: Channels (Fp1 of HC and Fp1 of SCZ, Fp2 and Fp2, F3 and F3, F3 and F3, F4 and F4), have no difference in their channels.…”

Section: Statistical Comparison For the Topographical Difference Betwmentioning

confidence: 98%

Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy

Harmah

et al. 2020

Front. Comput. Neurosci.

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People living with schizophrenia (SCZ) experience severe brain network deterioration. The brain is constantly fizzling with non-linear causal activities measured by electroencephalogram (EEG) and despite the variety of effective connectivity methods, only few approaches can quantify the direct non-linear causal interactions. To circumvent this problem, we are motivated to quantitatively measure the effective connectivity by multivariate transfer entropy (MTE) which has been demonstrated to be able to capture both linear and non-linear causal relationships effectively. In this work, we propose to construct the EEG effective network by MTE and further compare its performance with the Granger causal analysis (GCA) and Bivariate transfer entropy (BVTE). The simulation results quantitatively show that MTE outperformed GCA and BVTE under varied signal-to-noise conditions, edges recovered, sensitivity, and specificity. Moreover, its applications to the P300 task EEG of healthy controls (HC) and SCZ patients further clearly show the deteriorated network interactions of SCZ, compared to that of the HC. The MTE provides a novel tool to potentially deepen our knowledge of the brain network deterioration of the SCZ.

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“…Based on recent studies using PET data to compare HC and AD individuals (Chung et al 2016;Duan et al 2017;Pereira et al 2018;Sanabria-Diaz et al 2013;Yang et al 2017), the following metrics were computed to characterize the network topological architecture:…”

Section: Region-based Uptake Covariance and Network Metricsmentioning

confidence: 99%

“…In this sense, most of these studies have attributed changes in network topology to the disease, without considering possible effects introduced by methodological constraints of the PET images, leading to inconclusive results and some discrepancies in the reported direction of changes in local and global metrics between control and AD groups. Moreover, only little attention has been paid to the possible benefits of PVE correction to quantitatively study PET-based covariance networks (Yang et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Metabolic and amyloid PET network reorganization in Alzheimer’s disease: differential patterns and partial volume effects

González‐Escamilla

Miederer²,

Grothe

et al. 2020

Brain Imaging and Behavior

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Alzheimer's disease (AD) is a neurodegenerative disorder, considered a disconnection syndrome with regional molecular pattern abnormalities quantifiable by the aid of PET imaging. Solutions for accurate quantification of network dysfunction are scarce. We evaluate the extent to which PET molecular markers reflect quantifiable network metrics derived through the graph theory framework and how partial volume effects (PVE)-correction (PVEc) affects these PET-derived metrics 75 AD patients and 126 cognitively normal older subjects (CN). Therefore our goal is twofold: 1) to evaluate the differential patterns of [ 18 F]FDG-and [ 18 F]AV45-PET data to depict AD pathology; and ii) to analyse the effects of PVEc on global uptake measures of [ 18 F]FDG-and [ 18 F]AV45-PET data and their derived covariance network reconstructions for differentiating between patients and normal older subjects. Network organization patterns were assessed using graph theory in terms of "degree", "modularity", and "efficiency". PVEc evidenced effects on global uptake measures that are specific to either [ 18 F]FDG-or [ 18 F]AV45-PET, leading to increased statistical differences between the groups. PVEc was further shown to influence the topological characterization of PET-derived covariance brain networks, leading to an optimised characterization of network efficiency and modularisation. Partial-volume effects correction improves the interpretability of PET data in AD and leads to optimised characterization of network properties for organisation or disconnection.

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Partial volume correction for PET quantification and its impact on brain network in Alzheimer’s disease

Cited by 43 publications

References 63 publications

Error propagation analysis of seven partial volume correction algorithms for [18F]THK-5351 brain PET imaging

Error propagation analysis of seven partial volume correction algorithms for [18F]THK-5351 brain PET imaging

Measuring the Non-linear Directed Information Flow in Schizophrenia by Multivariate Transfer Entropy

Metabolic and amyloid PET network reorganization in Alzheimer’s disease: differential patterns and partial volume effects

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