Spatial Auto-Regressive Analysis of Correlation in 3-D PET With Application to Model-Based Simulation of Data

Huang, Jian; Mou, Tian; O’Regan, Kevin; O’Sullivan, Finbarr

doi:10.1109/tmi.2019.2938411

Cited by 8 publications

(13 citation statements)

References 24 publications

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“…We refer to such list-mode or sinogram sampling techniques as projection-domain methods. The work here is stimulated by ( Huang et al, 2020 ), who used a combination of physical phantom studies and numerical simulations to develop an image-domain bootstrapping strategy for PET data. The approach is based on a sub-ordinate Gaussian structure, a particular type of Gaussian copula form ( Joe, 2014 ), with the ability to capture the Poisson-like nature of voxel-level measurements as well as relevant spatial and temporal covariances.…”

Section: Introductionmentioning

confidence: 99%

“…The approach is based on a sub-ordinate Gaussian structure, a particular type of Gaussian copula form ( Joe, 2014 ), with the ability to capture the Poisson-like nature of voxel-level measurements as well as relevant spatial and temporal covariances. In the context of standard clinical PET-FDG studies, involving imaging over a relatively short duration time frame between 45 and 60 minutes after tracer injection, ( Huang et al, 2020 ) proposed sub-dividing frame data in order to obtain the near-replicate information needed to estimate unknown parameters in a proposed image-domain simulation model. This technique was illustrated using data from a clinical PET-FDG lung cancer study.…”

Section: Introductionmentioning

confidence: 99%

“…The work here develops a more flexible procedure for image-domain bootstrapping. This new approach is applicable in situations where there may be a complex temporal structure in the measured PET data – a near-constant temporal structure is intrinsic to the method used in ( Huang et al, 2020 ). In addition the latter work relied on a parametric Gamma-model form to represent the marginal distributions of voxel-level data and a parametric spatial auto-regressive (SAR) form to represent covariance patterns.…”

Section: Introductionmentioning

confidence: 99%

“…The purpose of this report is to describe the modeling techniques involved in a novel image-domain bootstrapping method and to numerically demonstrate its performance relative to the standard projection-domain bootstrapping technique. Similar to ( Huang et al, 2020 ) the proposed approach is only applicable to situations where the PET data have a temporal extent – e.g. dynamic PET studies.…”

Section: Introductionmentioning

confidence: 99%

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A Generalized Linear modeling approach to bootstrapping multi-frame PET image data

O’Sullivan

et al. 2021

Medical Image Analysis

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PET imaging is an important diagnostic tool for management of patients with cancer and other diseases. Medical decisions based on quantitative PET information could potentially benefit from the availability of tools for evaluation of associated uncertainties. Raw PET data can be viewed as a sample from an inhomogeneous Poisson process so there is the possibility to directly apply bootstrapping to raw projection-domain list-mode data. Unfortunately this is computationally impractical, particularly if data reconstruction is iterative or the acquisition protocol is dynamic. We develop a flexible statistical linear model analysis to be used with multi-frame PET image data to create valid bootstrap samples. The technique is illustrated using data from dynamic PET studies with fluoro-deoxyglucose (FDG) and fluoro-thymidine (FLT) in brain and breast cancer patients. As is often the case with dynamic PET studies, data have been archived without raw list-mode information. Using the bootstrapping technique maps of kinetic parameters and associated uncertainties are obtained. The quantitative performance of the approach is assessed by simulation. The proposed image-domain bootstrap is found to substantially match the projection-domain alternative. Analysis of results points to a close relation between relative uncertainty in voxel-level kinetic parameters and local reconstruction error. This is consistent with statistical theory.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Generalized Linear modeling approach to bootstrapping multi-frame PET image data

O’Sullivan

et al. 2021

Medical Image Analysis

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“…The inter-frame decorrelation of A(z, x, y, t) was computed based on complex OCT signals, which enables higher motion sensitivity because of the comprehensive utilization of amplitude and phase information. In addition, to suppress noise-induced decorrelation artifacts, the inverse signal-to-noise ratio (iSNR)-decorrelation OCTA (ID-OCTA) algorithm was applied to remove static and noise regions at all SNRs in intensity-decorrelation feature space [23].…”

Section: Octamentioning

confidence: 99%

Correlation of optical attenuation coefficient estimated using optical coherence tomography with changes in astrocytes and neurons in a chronic photothrombosis stroke model

Yang

Liu

Yao

et al. 2019

Biomed. Opt. Express

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The optical attenuation coefficient (OAC) estimated using optical coherence tomography (OAC-OCT) offers a label-free 3D mapping of tissue infarction, but the physiological origin of the OAC contrast remains unclear. For effectively suppressing OAC fluctuations, we propose a hybrid (wavelength/angle) division multiplexing (HDM) method, which improved the OAC contrast by 70.7% in tissue phantoms. To test the feasibility of OAC-based infarction detection, triphenyltetrazolium chloride (TTC) staining was performed on fresh ex vivo brain slices, and the TTC-defined infarction was used as the ground truth. Sharp OAC contrast was observed between the TTC-defined infarction (1.09 mm −1 ) and normal tissue (0.79 mm −1 ). The OAC infarction spatially matched well with the TTC-defined infarction. To further explore the physiological origin of OAC contrast in ischemic stroke at the cellular level, the dynamic changes in OAC were measured in the rat cortex in vivo over 3 weeks after photothrombosis (PT) occlusion and found significantly correlated with the changes in astrocytes and neurons acquired with ex vivo hematoxylin and eosin (HE), glial fibrillary acidic protein (GFAP), and NeuN staining. These results suggest that OAC imaging enables non-invasive infarction detection and its contrast might originate from the changes in astrocytes and neurons in the chronic PT stroke model. The cellular responses revealed by in vivo OAC imaging would be essential for evaluating treatments and even developing novel therapies.

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The spatial and temporal trends of severe periodontitis burden in Asia, 1990–2019: A population‐based epidemiological study

Luo

Luan

Jiang

et al. 2022

Journal of Periodontology

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Background To investigate the long‐term and spatial patterns of incidence, prevalence, and disability‐adjusted life year (DALY) rates of severe periodontitis in Asia from 1990 to 2019, and to estimate the associations between disease burden and socioeconomic development using the Socio‐Demographic Index (SDI). Methods Data were obtained from the global burden of disease study 2019. The average annual percent change (AAPC) was calculated to reflect temporal trends, spatial autocorrelation analysis was conducted to estimate the spatial characteristics, and spatial panel models were used to investigate the association between SDI and severe periodontitis burden. Results For Asia as a whole, the crude rates increased by 1.10% per year for incidence, 1.42% per year for prevalence, and 1.41% per year for DALY from 1990 to 2019. The age‐standardized incidence, prevalence and DALY rates increased by 0.18%, 0.22%, and 0.23% per year, respectively. Spatially, the hot spots of age‐standardized incidence, prevalence and DALY rates were located in Southern Asia, besides, these rates all showed increasing trends in most countries, and the increases were clustered in Southeastern Asia. Further, SDI showed a negative association with incidence (coef = −14.44; 95% CI: −24.63, −4.25) and prevalence (coef = −40.09; −51.81, −28.36), and a positive association with DALY rates (coef = 0.31; 0.23; 0.38). Conclusions Severe periodontitis poses a serious public health challenge in Asian countries with increasing temporal trends and substantial spatial inequalities. Effective geographically targeted public health interventions and strategies are needed to address the growing burden associated with severe periodontitis.

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Spatial Auto-Regressive Analysis of Correlation in 3-D PET With Application to Model-Based Simulation of Data

Cited by 8 publications

References 24 publications

A Generalized Linear modeling approach to bootstrapping multi-frame PET image data

A Generalized Linear modeling approach to bootstrapping multi-frame PET image data

Correlation of optical attenuation coefficient estimated using optical coherence tomography with changes in astrocytes and neurons in a chronic photothrombosis stroke model

The spatial and temporal trends of severe periodontitis burden in Asia, 1990–2019: A population‐based epidemiological study

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