Multiple energy X-ray imaging of metal oxide particles inside gingival tissues.

BACKGROUND: Periodontal disease affects over 50% of the global population and is characterized by gingivitis as the initial sign. One dental health issue that may contribute to the development of periodontal disease is foreign body gingivitis (FBG), which can result from exposure to some kinds of foreign metal particles from dental products or food. OBJECTIVE: We design a novel, portable, affordable, multispectral X-ray and fluorescence optical microscopic imaging system dedicated to detecting and differentiating metal oxide particles in dental pathological tissues. A novel denoising algorithm is applied. We verify the feasibility and optimize the performance of the imaging system with numerical simulations. METHODS: The designed imaging system has a focused X-ray tube with tunable energy spectra and thin scintillator coupled with an optical microscope as detector. A simulated soft tissue phantom is embedded with 2-micron thick metal oxide discs as the imaged object. GATE software is used to optimize the systematic parameters such as energy bandwidth and X-ray photon number. We have also applied a novel denoising method, Noise2Sim with a two-layer UNet structure, to improve the simulated image quality. RESULTS: The use of an X-ray source operating with an energy bandwidth of 5 keV, X-ray photon number of 108, and an X-ray detector with a 0.5 micrometer pixel size in a 100 by 100-pixel array allowed for the detection of particles as small as 0.5 micrometer. With the Noise2Sim algorithm, the CNR has improved substantially. A typical example is that the Aluminum (Al) target's CNR is improved from 6.78 to 9.72 for the case of 108 X-ray photons with the Chromium (Cr) source of 5 keV bandwidth. CONCLUSIONS: Different metal oxide particles were differentiated using Contrast-to-Noise ratio (CNR) by utilizing four different X-ray spectra.

Bacterial Lesions of the Oral Mucosa.

Nonodontogenic bacterial infections of the oral cavity are not a common finding in the United States. Nevertheless, there has been an increase in prevalence of certain bacterial sexually transmitted diseases, such as syphilis and gonorrhea, and conditions such as tuberculosis still pose a serious threat to certain segments of the population. Finally, given the uncommon nature and pathophysiology of these diseases, diagnosis is often delayed, resulting in more clinically significant disease and potential contamination of individuals. Thus, it is prudent that clinicians be familiar with these uncommon but potentially serious infectious diseases, so treatment can be instituted promptly.

Interactions between silica and titanium nanoparticles and oral and gastrointestinal epithelia: Consequences for inflammatory diseases and cancer.

Engineered nanoparticles (NPs) composed of elements such as silica and titanium, smaller than 100 nm in diameter and their aggregates, are found in consumer products such as cosmetics, food, antimicrobials and drug delivery systems, and oral health products such as toothpaste and dental materials. They may also interact accidently with epithelial tissues in the intestines and oral cavity, where they can aggregate into larger particles and induce inflammation through pathways such as inflammasome activation. Persistent inflammation can lead to precancerous lesions. Both the particles and lesions are difficult to detect in biopsies, especially in clinical settings that screen large numbers of patients. As diagnosis of early stages of disease can be lifesaving, there is growing interest in better understanding interactions between NPs and epithelium and developing rapid imaging techniques that could detect foreign particles and markers of inflammation in epithelial tissues. NPs can be labelled with fluorescence or radioactive isotopes, but it is challenging to detect unlabeled NPs with conventional imaging techniques. Different current imaging techniques such as synchrotron radiation X-ray fluorescence spectroscopy are discussed here. Improvements in imaging techniques, coupled with the use of machine learning tools, are needed before diagnosis of particles in biopsies by automated imaging could move usefully into the clinic.

X-ray projection imaging of metal oxide particles inside gingival tissues.

There is increasing recognition that oral health affects overall health and systemic diseases. Nonetheless it remains challenging to rapidly screen patient biopsies for signs of inflammation or the pathogens or foreign materials that elicit the immune response. This is especially true in conditions such as foreign body gingivitis (FBG), where the foreign particles are often difficult to detect. Our long term goal is to establish a method to determine if the inflammation of the gingival tissue is due to the presence of a metal oxide, with emphasis on elements that were previously reported in FBG biopsies, such as silicon dioxide, silica, and titanium dioxide whose persistent presence can be carcinogenic. In this paper, we proposed to use multiple energy X-ray projection imaging to detect and to differentiate different metal oxide particles embedded inside gingival tissues. To simulate the performance of the imaging system, we have used GATE simulation software to mimic the proposed system and to obtain images with different systematic parameters. The simulated parameters include the X-ray tube anode metal, the X-ray spectra bandwidth, the X-ray focal spot size, the X-ray photon number, and the X-ray dector pixel. We have also applied the de-noising algorithm to obtain better Contrast-to-noise ratio (CNR). Our results indicate that it is feasible to detect metal particles as small as 0.5 micrometer in diameter when we use a Chromium anode target with an energy bandwidth of 5 keV, an X-ray photon number of 10^8, and an X-ray detector with a pixel size of 0.5 micrometer and 100 by 100 pixels. We have also found that different metal particles could be differentiated from the CNR at four different X-ray anodes and spectra. These encouraging initial results will guide our future imaging system design.