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

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

BACKGROUND

To investigate the long-term and spatial patterns of incidence, prevalence and disability-adjusted life year (DALY) 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. Furthermore, 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. This article is protected by copyright. All rights reserved.

Quantitation of multiple injection dynamic PET scans: an investigation of the benefits of pooling data from separate scans when mapping kinetics

Multiple injection dynamic positron emission tomography (PET) scanning is used in the clinical management of certain groups of patients and in medical research. The analysis of these studies can be approached in two ways: (i) separate analysis of data from individual tracer injections, or (ii), concatenate/pool data from separate injections and carry out a combined analysis. The simplicity of separate analysis has some practical appeal but may not be statistically efficient. We use a linear model framework associated with a kinetic mapping scheme to develop a simplified theoretical understanding of separate and combined analysis. The theoretical framework is explored numerically using both 1D and 2D simulation models. These studies are motivated by the breast cancer flow-metabolism mismatch studies involving15O-water (H2O) and18F-Fluorodeoxyglucose (FDG) and repeat15O-H2O injections used in brain activation investigations. Numerical results are found to be substantially in line with the simple theoretical analysis: mean square error characteristics of alternative methods are well described by factors involving the local voxel-level resolution of the imaging data, the relative activities of the individual scans and the number of separate injections involved. While voxel-level resolution has dependence on scan dose, after adjustment for this effect, the impact of a combined analysis is understood in simple terms associated with the linear model used for kinetic mapping. This is true for both data reconstructed by direct filtered backprojection or iterative maximum likelihood. The proposed analysis has potential to be applied to the emerging long axial field-of-view PET scanners.

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

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.

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

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.