Author Correction: Multispectral imaging detects gastritis consistently in mouse model and in humans.
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ABSTRACT:
SUBMITTER: Bazin T
PROVIDER: S-EPMC8370981 | biostudies-literature | 2021 Aug
REPOSITORIES: biostudies-literature
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Author Correction: Multispectral imaging detects gastritis consistently in mouse model and in humans.
Bazin Thomas T Martinez-Herrera Sergio Ernesto SE Jobart-Malfait Aude A Benezeth Yannick Y Boffety Matthieu M Julié Catherine C Emile Jean-François JF Michel Valérie V Goudail François F Touati Eliette E Marzani Franck F Lamarque Dominique D
Scientific reports 20210817 1
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Project description:Gastritis constitutes the initial step of the gastric carcinogenesis process. Gastritis diagnosis is based on histological examination of biopsies. Non-invasive real-time methods to detect mucosal inflammation are needed. Tissue optical properties modify reemitted light, i.e. the proportion of light that is emitted by a tissue after stimulation by a light flux. Analysis of light reemitted by gastric tissue could predict the inflammatory state. The aim of our study was to investigate a potential association between reemitted light and gastric tissue inflammation. We used two models and three multispectral analysis methods available on the marketplace. We used a mouse model of Helicobacter pylori infection and included patients undergoing gastric endoscopy. In mice, the reemitted light was measured using a spectrometer and a multispectral camera. We also exposed patient's gastric mucosa to specific wavelengths and analyzed reemitted light. In both mouse model and humans, modifications of reemitted light were observed around 560 nm, 600 nm and 640 nm, associated with the presence of gastritis lesions. These results pave the way for the development of improved endoscopes in order to detect real-time gastritis without the need of biopsies. This would allow a better prevention of gastric cancer alongside with cost efficient endoscopies.
| S-EPMC7674504 | biostudies-literature
Project description:BackgroundGastric inflammation is a major risk factor for gastric cancer. Current endoscopic methods are not able to efficiently detect and characterize gastric inflammation, leading to a sub-optimal patients' care. New non-invasive methods are needed. Reflectance mucosal light analysis is of particular interest in this context. The aim of our study was to analyze reflectance light and specific autofluorescence signals, both in humans and in a mouse model of gastritis.MethodsWe recruited patients undergoing gastroendoscopic procedure during which reflectance was analysed with a multispectral camera. In parallel, the gastritis mouse model of Helicobacter pylori infection was used to investigate reflectance from ex vivo gastric samples using a spectrometer. In both cases, autofluorescence signals were measured using a confocal microscope.FindingsIn gastritis patients, reflectance modifications were significant in near-infrared spectrum, with a decrease between 610 and 725 nm and an increase between 750 and 840 nm. Autofluorescence was also modified, showing variations around 550 nm of emission. In H. pylori infected mice developing gastric inflammatory lesions, we observed significant reflectance modifications 18 months after infection, with increased intensity between 617 and 672 nm. Autofluorescence was significantly modified after 1, 3 and 6 months around 550 and 630 nm. Both in human and in mouse, these reflectance data can be considered as biomarkers and accurately predicted inflammatory state.InterpretationIn this pilot study, using a practical measuring device, we identified in humans, modification of reflectance spectra in the visible spectrum and for the first time in near-infrared, associated with inflammatory gastric states. Furthermore, both in the mouse model and humans, we also observed modifications of autofluorescence associated with gastric inflammation. These innovative data pave the way to deeper validation studies on larger cohorts, for further development of an optical biopsy system to detect gastritis and finally to better surveil this important gastric cancer risk factor.FundingThe project was funded by the ANR EMMIE (ANR-15-CE17-0015) and the French Gastroenterology Society (SNFGE).
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Project description: Not available
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Project description:PurposeTo develop a method for improved B0 field-map estimation, deblurring, and image combination for multispectral imaging near metal.MethodsA goodness-of-fit field-map estimation technique is proposed that uses only the multispectral imaging (MSI) data to estimate the field map. Using the improved field map, a novel deblurring technique is proposed that also employs a new image combination scheme to reduce the effects of noise and other residual MSI artifacts. The proposed field-map estimation and deblurring techniques are compared to the current methods in phantoms and/or in vivo from subjects with knee, hip, and spinal metallic implants.ResultsPhantom experiments validate that the goodness-of-fit field-map estimation is less sensitive to noise and bias than the conventional center-of-mass technique, which reduces distortion in the deblurring methods. The new deblurring approach also is substantially less sensitive to noise and distortion than the current deblurring method, as demonstrated in phantoms and in vivo, and is able to find a good tradeoff between deblurring and distortion.ConclusionThe proposed methods not only enable field-mapping with reduced noise sensitivity but are able to create deblurred images with less distortion and better signal-to-noise ratio with no additional scan time, thereby enabling improved visualization of underlying anatomy near metallic implants. Magn Reson Med 78:2022-2034, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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Project description:Multispectral photoacoustic imaging (MPAI) is a promising emerging diagnostic technology, but fluence artifacts can degrade device performance. Our goal was to develop well-validated phantom-based test methods for evaluating and comparing MPAI fluence correction algorithms, including a heuristic diffusion approximation, Monte Carlo simulations, and an algorithm we developed based on novel application of the diffusion dipole model (DDM). Phantoms simulated a range of breast-mimicking optical properties and contained channels filled with chromophore solutions (ink, hemoglobin, or copper sulfate) or connected to a previously developed blood flow circuit providing tunable oxygen saturation (SO2). The DDM algorithm achieved similar spectral recovery and SO2 measurement accuracy to Monte Carlo-based corrections with lower computational cost, potentially providing an accurate, real-time correction approach. Algorithms were sensitive to optical property uncertainty, but error was minimized by matching phantom albedo. The developed test methods may provide a foundation for standardized assessment of MPAI fluence correction algorithm performance.
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Project description:Noncontact optical imaging of curved objects can result in strong artifacts due to the object's shape, leading to curvature biased intensity distributions. This artifact can mask variations due to the object's optical properties, and makes reconstruction of optical/physiological properties difficult. In this work we demonstrate a curvature correction method that removes this artifact and recovers the underlying data, without the necessity of measuring the object's shape. This method is applicable to many optical imaging modalities that suffer from shape-based intensity biases. By separating the spatially varying data (e.g., physiological changes) from the background signal (dc component), we show that the curvature can be extracted by either averaging or fitting the rows and columns of the images. Numerical simulations show that our method is equivalent to directly removing the curvature, when the object's shape is known, and accurately recovers the underlying data. Experiments on phantoms validate the numerical results and show that for a given image with 16.5% error due to curvature, the method reduces that error to 1.2%. Finally, diffuse multispectral images are acquired on forearms in vivo. We demonstrate the enhancement in image quality on intensity images, and consequently on reconstruction results of blood volume and oxygenation distributions.
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Project description:ObjectivesTo develop a mouse model for multispectral fluorescence imaging of the pancreas and pancreatic microenvironment.MethodsCre/loxP technology was used to develop this model. We crossed mT/mG indicator mice, engineered to constitutively express a conditional tdTomato transgene that converts to green fluorescent protein (GFP) expression after exposure to Cre recombinase, with Pdx1-Cre transgenic mice. To characterize this model for studies of pancreas biology, we performed bright light and fluorescence imaging of body cavities and intact organs and confocal microscopy of pancreata from offspring of Pdx1-Cre and mT/mG crosses.ResultsPdx1-Cre-mT/mG mice demonstrated bright GFP expression within the pancreas and duodenum and intense tdTomato expression in all other organs. Green fluorescent protein expression was mosaic in Pdx1-Cre-mT/mG pancreata, with most showing extensive conversion from tdTomato to GFP expression within the epithelial-derived elements of the pancreatic parenchyma. Because both GFP and tdTomato are membrane targeted, individual cell borders were clearly outlined in confocal images of mT/mG pancreata.ConclusionsThis mouse model enables multispectral fluorescence imaging of individual cells and cell processes at the microscopic level of the pancreatic microenvironment; it should prove valuable for a variety of fluorescence imaging studies, ranging from pancreatic development to pancreatic cancer biology.
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Project description:Removing the comb artifact introduced by imaging fibre bundles, or 'fibrescopes', for example in medical endoscopy, is essential to provide high quality images to the observer. Multispectral imaging (MSI) is an emerging method that combines morphological (spatial) and chemical (spectral) information in a single data 'cube'. When a fibrescope is coupled to a spectrally resolved detector array (SRDA) to perform MSI, comb removal is complicated by the demosaicking step required to reconstruct the multispectral data cube. To understand the potential for using SRDAs as multispectral imaging sensors in medical endoscopy, we assessed five comb correction methods with respect to five performance metrics relevant to biomedical imaging applications: processing time, resolution, smoothness, signal and the accuracy of spectral reconstruction. By assigning weights to each metric, which are determined by the particular imaging application, our results can be used to select the correction method to achieve best overall performance. In most cases, interpolation gave the best compromise between the different performance metrics when imaging using an SRDA.
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Project description:An amendment to this paper has been published and can be accessed via a link at the top of the paper.
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Project description:IntroductionA severe side effect of cancer chemotherapy is the development of gastrointestinal mucositis, characterised by mucosal inflammation. We investigated if 2-deoxy-2-[18F] fluoro-D-glucose positron emission tomography combined with computed tomography (2-[18F]FDG-PET/CT) could visualise gastrointestinal mucositis in mice treated with the chemotherapeutic agent doxorubicin.MethodsIn this study, gastrointestinal inflammation was longitudinally evaluated by 2-[18F]FDG-PET/CT scans before and 1, 3, 6, and 10 days after treatment with doxorubicin. Doxorubicin-treated mice were compared to saline-treated littermates using the abdominal standard uptake value of 2-[18F]FDG corrected for body weight (SUVBW).ResultsAbdominal SUVBW was significantly increased on day 1 (p < 0.0001), day 3 (p < 0.0001), and day 6 (p < 0.05) in the doxorubicin-treated group compared to controls. Abdominal SUVBW returned to baseline levels on day 10. In the doxorubicin group, the largest weight loss was observed on day 3 (control vs doxorubicin, mean percent of baseline weight: (98.5 ± 3.2% vs 87.9 ± 4.6%, p < 0.0001). Moreover, in the doxorubicin-treated group, villus lengths were decreased by 23-28% on days 1 and 3 in the small intestine (p < 0.05), and jejunal levels of tumour necrosis factor and interleukin-1β were significantly increased on day 3 (p < 0.05).DiscussionTogether, these findings indicate that sequential 2-[18F]FDG-PET/CT scans can objectively quantify and evaluate the development and resolution of intestinal inflammation over time in a mouse model of doxorubicin-induced mucositis.
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