Quantitative Image Analysis Method Research Articles

Quantitative cellular pathology of the amygdala in temporal lobe epilepsy and correlation with magnetic resonance imaging volumetry, tissue microstructure, and sudden unexpected death in epilepsy risk factors.

Amygdala enlargement can occur in temporal lobe epilepsy, and increased amygdala volume is also reported in sudden unexpected death in epilepsy (SUDEP). Apnea can be induced by amygdala stimulation, and postconvulsive central apnea (PCCA) and generalized seizures are both known SUDEP risk factors. Neurite orientation dispersion and density imaging (NODDI) has recently provided additional information on altered amygdala microstructure in SUDEP. In a series of 24 surgical temporal lobe epilepsy cases, our aim was to quantify amygdala cellular pathology parameters that could predict enlargement, NODDI changes, and ictal respiratory dysfunction. Using whole slide scanning automated quantitative image analysis methods, parallel evaluation of myelin, axons, dendrites, oligodendroglia, microglia, astroglia, neurons, serotonergic networks, mTOR-pathway activation (pS6) and phosphorylated tau (pTau; AT8, AT100, PHF) in amygdala, periamygdala cortex, and white matter regions of interest were compared with preoperative magnetic resonance imaging data on amygdala size, and in 13 cases with NODDI and evidence of ictal-associated apnea. We observed significantly higher glial labeling (Iba1, glial fibrillary acidic protein, Olig2) in amygdala regions compared to cortex and a strong positive correlation between Olig2 and Iba1 in the amygdala. Larger amygdala volumes correlated with lower microtubule-associated protein (MAP2), whereas higher NODDI orientation dispersion index correlated with lower Olig2 cell densities. In the three cases with recorded PCCA, higher MAP2 and pS6-235 expression was noted than in those without. pTau did not correlate with SUDEP risk factors, including seizure frequency. Histological quantitation of amygdala microstructure can shed light on enlargement and diffusion imaging alterations in epilepsy to explore possible mechanisms of amygdala dysfunction, including mTOR pathway activation, that in turn may increase the risk for SUDEP.

Radiomics-based machine learning model to phenotype hip involvement in ankylosing spondylitis: a pilot study.

Hip involvement is an important reason of disability in patients with ankylosing spondylitis (AS). Unveiling the potential phenotype of hip involvement in AS remains an unmet need to understand its biological mechanisms and improve clinical decision-making. Radiomics, a promising quantitative image analysis method that had been successfully used to describe the phenotype of a wide variety of diseases, while it was less reported in AS. The objective of this study was to investigate the feasibility of radiomics-based approach to profile hip involvement in AS. A total of 167 patients with AS was included. Radiomic features were extracted from pelvis MRI after image preprocessing and feature engineering. Then, we performed unsupervised machine learning method to derive radiomics-based phenotypes. The validation and interpretation of derived phenotypes were conducted from the perspectives of clinical backgrounds and MRI characteristics. The association between derived phenotypes and radiographic outcomes was evaluated by multivariable analysis. 1321 robust radiomic features were extracted and four biologically distinct phenotypes were derived. According to patient clinical backgrounds, phenotype I (38, 22.8%) and II (34, 20.4%) were labelled as high-risk while phenotype III (24, 14.4%) and IV (71, 42.5%) were at low risk for hip involvement. Consistently, the high-risk phenotypes were associated with higher prevalence of MRI-detected lesion than the low-risk. Moreover, phenotype I had significant acute inflammation signs than phenotype II, while phenotype IV was enthesitis-predominant. Importantly, the derived phenotypes were highly predictive of radiographic outcomes of patients, as the high-risk phenotypes were 3 times more likely to have radiological hip lesion than the low-risk [27 (58.7%) vs 16 (28.6%); adjusted odds ratio (OR) 2.95 (95% CI 1.10, 7.92)]. We confirmed for the first time, the clinical actionability of profiling hip involvement in AS by radiomics method. Four distinct phenotypes of hip involvement in AS were identified and importantly, the high-risk phenotypes could predict structural damage of hip involvement in AS.

Amyloid‐beta (Aβ) load in the post‐mortem brain correlates with APOE genotype and ante‐mortem cognitive performance in centenarians

AbstractBackgroundThe prevalence of amyloid‐beta (Aβ) pathology increases at higher ages in non‐demented individuals1. With anti‐amyloid‐beta therapies on the horizon and the growing number of elderly in our population, a clear perception of the association between accumulated Aβ pathology, APOE genotype and cognitive performance in the oldest‐old is warranted.MethodsIn 93 post‐mortem brains donated by participants of the Dutch 100‐plus Study, who self‐reported to be cognitively healthy at inclusion, we evaluated: 1) the spatiotemporal Aβ spread using Thal phases2; and 2) Aβ load in frontal, parietal, temporal and occipital cortices using a quantitative image analysis method. Spread and load of Aβ pathology were compared between different APOE groups (ɛ2/2 and ɛ2/3 (n = 20); ɛ3/3 (n = 65); ɛ2/4 and ɛ3/4 (n = 8)). Post‐mortem Aβ measurements were correlated to ante‐mortem test scores on a comprehensive neuropsychological test battery3 and a composite z‐score for global cognitive performance. Median age at death was 103.3 (interquartile range (IQR): 102.3‐104.9) and median time between neuropsychological assessment and brain donation amounted 0.61 years (IQR: 0.28‐0.93).ResultsNotably, only 10% of the centenarians had not accumulated Aβ pathology. APOE ɛ2/2 and ɛ2/3 carriers had lower Aβ load (median frontal AB load = 0.30%, IQR: 0.02‐6.15), but not spread, compared to APOE ɛ2/4 and ɛ3/4 carriers (median frontal AB load = 5.08%, IQR: 2.13‐30.71) (p = 0.041), but not compared to ɛ3/3 carriers (median frontal AB load = 2.19%, IQR:0.23‐7.54) (p = 0.245). Higher Aβ load in all four analyzed regions associated with lower performance on the Digit Span Backward test (rfrontal = ‐0.29, rparietal = ‐0.29, rtemporal = ‐0.28, roccipital = ‐0.32, FDR corrected p<0.05) and the Clock Drawing test (rfrontal = ‐0.40, rparietal = ‐0.29, rtemporal = ‐0.29, roccipital = ‐0.33, FDR corrected p<0.05), which assess executive‐ and visuospatial‐functioning. Moreover, higher Aβ load in the frontal cortex correlated with lower performance on the Mini Mental State Examination (MMSE) (r = ‐0.24, p = 0.049) and a seperate composite score of global cognitive performance (r = ‐0.28, p = 0.019).ConclusionAlthough Aβ pathology seems nearly inevitable at extreme ages, our results suggest that Aβ load, rather than Aβ spread, affects cognitive performance in the oldest‐old1 https://doi.org/10.1002/alz.12899 2 https://doi.org/10.1212/wnl.58.12.1791 3 https://doi.org/10.1007/s10654-018-0451-3

In vitro modeling of polyclonal infection dynamics within the human airways by Haemophilus influenzae differential fluorescent labeling.

Standardized clinical procedures for antibiotic administration rely on pathogen identification and antibiotic susceptibility testing, often performed on single-colony bacterial isolates. For respiratory pathogens, this could be questionable, as chronic patients may be persistently colonized by multiple clones or lineages from the same bacterial pathogen species. Indeed, multiple strains of nontypeable Haemophilus influenzae, with different antibiotic susceptibility profiles, can be co-isolated from cystic fibrosis and chronic obstructive pulmonary disease sputum specimens. Despite this clinical evidence, we lack information about the dynamics of H. influenzae polyclonal infections, which limits the optimization of therapeutics. Here, we present the engineering and validation of a plasmid toolkit (pTBH, toolbox for Haemophilus), with standardized modules consisting of six reporter genes for fluorescent or bioluminescent labeling of H. influenzae. This plasmid set was independently introduced in a panel of genomically and phenotypically different H. influenzae strains, and two of them were used as a proof of principle to analyze mixed biofilm growth architecture and antibiotic efficacy, and to visualize the dynamics of alveolar epithelial co-infection. The mixed biofilms showed a bilayer architecture, and antibiotic efficacy correlated with the antibiotic susceptibility of the respective single-species strains. Furthermore, differential kinetics of bacterial intracellular location within subcellular acidic compartments were quantified upon co-infection of cultured airway epithelial cells. Overall, we present a panel of novel plasmid tools and quantitative image analysis methods with the potential to be used in a whole range of bacterial host species, assay types, and/or conditions and generate meaningful information for clinically relevant settings. IMPORTANCE Genomic diversity of nontypeable H. influenzae strains confers phenotypic heterogeneity. Multiple strains of H. influenzae can be simultaneously isolated from clinical specimens, but we lack detailed information about polyclonal infection dynamics by this pathogen. A long-term barrier to our understanding of this host-pathogen interplay is the lack of genetic tools for strain engineering and differential labeling. Here, we present a novel plasmid toolkit named pTBH (toolbox for Haemophilus), with standardized modules for fluorescent or bioluminescent labeling, adapted to H. influenzae requirements but designed to be versatile so it can be utilized in other bacterial species. We present detailed experimental and quantitative image analysis methods, together with proof-of-principle examples, and show the ample possibilities of 3D microscopy, combined with quantitative image analysis, to model H. influenzae polyclonal infection lifestyles and unravel the co-habitation and co-infection dynamics of this respiratory pathogen.

Quantitative image analysis of influence of polysaccharides on protein network formation in GDL-acidified milk gels

Exopolysaccharides (EPS) are commonly used to improve the texture of yogurt. These polysaccharides interact with casein micelles, the major protein in milk, via electrostatic and depletion mechanisms during fermentation by lactic acid bacteria (LAB). However, the relationship between the physicochemical properties and monosaccharide composition of EPS and their impact on yogurt texture is not yet fully understood. To address this knowledge gap, we studied the effects of polysaccharides commonly used as food additives on acid-induced milk protein networks. Confocal laser scanning microscopy (CLSM) was used to image the network microstructures. Image analysis, including Fourier transform, autocorrelation, and binarization-based techniques, was applied to quantify key structural features of the mixed milk protein/polysaccharide gels. These parameters were then related to the macroscopic properties of the model food matrices, such as elastic and viscous moduli and yield point. We found that the addition of neutral polysaccharides resulted in a concentration-dependent increase in structure factor, protein domain size, and pore fraction. In contrast, the presence of charged polysaccharides led to an increase in protein domain size, a decrease in pore fraction, and a decrease in elastic and viscous moduli. These results demonstrate the use of a quantitative image analysis method for selecting LAB with favorable EPS properties to improve yogurt texture.

Maternal age, obesity and hyperglycaemia are associated with a delay in preimplantation embryo development in mouse.

Fertility has decreased due to advanced maternal age and the rising prevalence of the metabolic syndrome. Using quantitative image analysis methods, we show that these factors are associated with delayed preimplantation embryo development in a mouse model. Delayed maternal age, obesity and diabetes are associated with reduced fertility. We investigated how age and obesity/metabolic syndrome impact fertility and hypothesized that its decrease is due to defects in preimplantation embryo development. Three groups of female C57Bl6 mice (12 weeks, 9 months and 1 year old) were fed either a high-fat diet for 8 weeks, to induce obesity and the metabolic syndrome, or a control chow diet. Body weight and composition, glucose tolerance and insulin resistance were assessed. Fecundity was evaluated by mating and pregnancy rates, as well as by the number of embryos. Embryo quality was assessed morphologically, and cell fate composition was analysed in preimplantation embryos by state-of-the-art single-cell quantitative confocal image analysis. The high-fat diet was associated with increased adiposity, glucose intolerance and insulin resistance, especially in the older mice. Fecundity was affected by age more than by the diet. Both age and high-fat diet were associated with reduced cell fate allocation, indicating a delay in the preimplantation embryo development, and with increased expression of GATA3, an inhibitor of placentation. These results support that age and the metabolic syndrome reduce fertility through mechanisms which are present at conception or very early in pregnancy.

Quantitative image analysis and cell imaging of peroxynitrite based on an upconversion luminescence total internal reflection single particle imaging platform

With the rapid development of science and technology, single particle imaging technology has been greatly improved, and many more sensitive optical microscopes have been rapidly developed. Herein, we propose a single particle imaging method for quantitative image analysis and cell imaging of peroxynitrite based on upconversion luminescence total internal reflection imaging platform. Cyanine-dye-assembled composite upconversion nanoparticles (UCNPs) were prepared and used as luminescent probe. For the quantitative image analysis method, the numbers of photons are obtained by calculating the average brightness of 550 frames of sequence images based on the Brownian motion of the luminescent nanoparticles. Compared to the fluorescence spectrometer method, the quantitative image analysis method for sensing peroxynitrite has many advantages such as less sample consumption (≤10 μL), fast detection time (~0.1 s) and low detection limit (0.067 μM). Due to the unique advantages of UCNPs luminescent materials, such as low biotoxicity, near-infrared emission and high penetration depth, the luminescent materials are also applied to image and semiquantitatively detect of exogenous peroxynitrite in living cells based on the single particle imaging platform. This work not only greatly improves the efficiency in vitro study, but also provides a simple, sensitive and convenient semiquantitative detection method for single particle imaging of living cells.

FRI029 - Novel digital pathology quantitative image analysis and AI method detects the treatment effects of NASH drug candidates with a performance that benchmarks imaging-based measurements

FRI029 - Novel digital pathology quantitative image analysis and AI method detects the treatment effects of NASH drug candidates with a performance that benchmarks imaging-based measurements

FRI592 - Establishing quantitative image analysis methods for tumor microenviroment evaluation

FRI592 - Establishing quantitative image analysis methods for tumor microenviroment evaluation

Abstract 453: Establishing quantitative image analysis methods for tumor microenvironment evaluation

Abstract Introduction: There is growing evidence that supports the role of the tumor microenvironment (TME) in the development and progression of hepatocellular carcinoma (HCC). However, the correlation between its composition and prognosis remain unclear. TME evaluation requires a combination of cell type and spatial information. These information can be obtained with the use of immunohistochemistry on patient derived slides. However, the IHC quantification remains a challenge. Computational methods such as artificial intelligence-based tool, may expedite the detection and classification thousands of different cells, expanding our understanding of the TME. Here, we aim to develop an AI based image analysis pipeline to define morphological and immunological characteristics of HCC-TME, as well as their relationship with clinicopathological features. Materials and Methods: We collected 98 HCC samples from liver resections available in the . TME composition of the tumors was evaluated and classified as inflamed, immune excluded and immune desert. Tumor slides were stained with a panel of TME markers (CD3, CD8, FOXP3, TIGIT, RORgt, ICOS, GranzymeB CD163, iNOS, PD-1, PD-L1) by IHC. The slides were digitalised and whole slide images were used for the quantification. The samples were split into training (80%) and test (20%) datasets and used to train convolutional neural network (CNN) models. For the quantification of immune cells, we trained two separate CNNs: cell detection and tumor-stroma segmentation. Cell nucleus instance segmentation was achieved using StarDist package (Schmidt et al 2018). We trained a model with our slides and tested the pretrained model (2D_versatile_he) which is for H&E stained images. Immune cells were classified using random forest classifier in QuPath. Finally, we trained a CNN in UNET architecture with ResNet34 backbone for semantic segmentation of tumor tissue into parenchyma, stroma and debris classes, by fastai deep learning library. Results: The accuracy of pretrained StarDist model was limited to 72% on IHC slide images. Thus, we trained a new cell nucleus instance segmentation StarDist model with our dataset and it reached 84% accuracy, 91.3% F1-Score, 92% true positive, 90.6% true negative rates on IHC slide images. Random forest classifier annotated immune cells at 98% accuracy. The tissue segmentation model classified tumor regions into parenchyma, stroma and debris at 95,8% accuracy, 92.5% dice, 86.3% IuO. Conclusions: In this study we developed a pipeline implementing open-source solutions to quantify IHC slides. The use of this semi-automatized computational pathology workflow can provide robust information in regard of the TME composition augmenting the discovering tumour specific TME features and pave way for the discovery of novel prognostic and therapeutic targets. Citation Format: Caner Ercan, Mairene Coto-Llerena, Salvatore Piscuoglio, Luigi M. Terracciano. Establishing quantitative image analysis methods for tumor microenvironment evaluation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 453.

Accurate Quantitative Histomorphometric-Mathematical Image Analysis Methodology of Rodent Testicular Tissue and Its Possible Future Research Perspectives in Andrology and Reproductive Medicine.

Infertility is increasing worldwide; male factors can be identified in nearly half of all infertile couples. Histopathologic evaluation of testicular tissue can provide valuable information about infertility; however, several different evaluation methods and semi-quantitative score systems exist. Our goal was to describe a new, accurate and easy-to-use quantitative computer-based histomorphometric-mathematical image analysis methodology for the analysis of testicular tissue. On digitized, original hematoxylin-eosin (HE)-stained slides (scanned by slide-scanner), quantitatively describable characteristics such as area, perimeter and diameter of testis cross-sections and of individual tubules were measured with the help of continuous magnification. Immunohistochemically (IHC)-stained slides were digitized with a microscope-coupled camera, and IHC-staining intensity measurements on digitized images were also taken. Suggested methods are presented with mathematical equations, step-by-step detailed characterization and representative images are given. Our novel quantitative histomorphometric-mathematical image analysis method can improve the reproducibility, objectivity, quality and comparability of andrological-reproductive medicine research by recognizing even the mild impairments of the testicular structure expressed numerically, which might not be detected with the present semi-quantitative score systems. The technique is apt to be subjected to further automation with machine learning and artificial intelligence and can be named ‘Computer-Assisted or -Aided Testis Histology’ (CATHI).

Observation of Liquid-Liquid-Phase Separation and Vesicle Spreading during Supported Bilayer Formation via Liquid-Phase Transmission Electron Microscopy.

Liquid-phase transmission electron microscopy (LP-TEM) enables the real-time visualization of nanoscale dynamics in solution. This technique has been used to study the formation and transformation mechanisms of organic and inorganic nanomaterials. Here, we study the formation of block-copolymer-supported bilayers using LP-TEM. We observe two formation pathways that involve either liquid droplets or vesicles as intermediates toward supported bilayers. Quantitative image analysis methods are used to characterize vesicle spread rates and show the origin of defect formation in supported bilayers. Our results suggest that bilayer assembly methods that proceed via liquid droplet intermediates should be beneficial for forming pristine supported bilayers. Furthermore, supported bilayers inside the liquid cells may be used to image membrane interactions with proteins and nanoparticles in the future.

Accurate detection of spherical objects in a complex background.

The automated detection of particles in microscopy images has become a routinely used method for quantitative image analysis in biology, physics, and other research fields. While the majority of particle detection algorithms have been developed for bulk materials, the detection of particles in a heterogenous environment due to surfaces or other objects in the studied material is of great interest. However, particle detection is hindered by a complex background due to the diffraction of light resulting in a decreased contrast and image noise. We present a new heuristic method for the reliable detection of spherical particles that suppresses false detections due to a heterogenous background without additional background measurements. Further, we discuss methods to obtain particle coordinates with improved accuracy and compare with other methods, in particular with that of Crocker and Grier.

Quantitative image analysis method for detection of nitrite with cyanine dye-NaYF4:Yb,Tm@NaYF4 upconversion nanoparticles composite luminescent probe

In this work, a quantitative image analysis method based on cyanine dye-upconversion nanoparticles composite luminescent nanoprobe for the detection of nitrite was developed. The nanoprobe was constructed by combining the NaYF4:Yb,Tm@NaYF4 upconversion nanoparticles (UCNPs) and the new cyanine dye IR-790. The upconversion nanoparticles transferred energy to IR-790, resulting in the luminescence quenching, while the luminescence of UCNPs was recovered after adding NO2−. The increase in photons was related to the concentration of NO2−. Under the optimal experimental conditions, the detection range was 0.20–140 μM and the limit of detection was 0.030 μM. The measurement for NO2− can be completed in 29 min. The method has the characteristics of fast response (~0.1 s), low sample consumption (10 μL) and powerful data support (550 frame time series images). Furthermore, the quantitative image analysis method was successfully applied for the analysis of nitrite in environmental water and food samples.

Clinical Validation Is the Key to Adopting AI in Clinical Practice.

Clinical Validation Is the Key to Adopting AI in Clinical Practice.

PLAIN AND PVA FIBRE-REINFORCED GEOPOLYMER COMPACT TENSION SPECIMEN CRITICAL AREA SURFACE COMPOSITION ASSESSMENT

For more than 40 years, low calcium alkali-activated cement composite, or in other words, geopolymer, has been around. In recent years there has been increased interest in this material and its properties. It is mainly due to the claim that geopolymer is the cement of the future. This claim is based on environmental factors. For instance, the CO2 emissions for geopolymer binder can be up to 6 less than for Portland cement binder. Most of the researches regarding geopolymer composite properties examine only mechanical and long-term properties in compression. There has been a lack of long-term tests in tension due to difficulties in performing them. As the tensile stresses are an essential part of structure assessment, it is necessary to evaluate new material properties as thoroughly as possible. Due to the nature of geopolymer specimen hardening (polymerisation), there is a difference in modulus of elasticity development and shrinkage caused by binding that could have factors that regular Portland cement specimens do not.This article aims to evaluate the surface composition of plain and 1% PVA reinforced geopolymer compact tension specimens that have been subjected to creep and shrinkage tests. Specimen cross-section images were acquired using the scanning electron microscope (SEM). Using the quantitative image analysis method, amounts of cross-section composition elements are determined. Furthermore, the amount of cracks is determined and compared between plain and PVA fiber-reinforced specimens.It has been determined that even though 1% of PVA fibre-reinforced specimens have lower tensile strength, their creep and shrinkage strains are lower, and the number of microcracks at the notch base of the specimen. Still, it has to be acknowledged that the amount of air voids in all analysed specimens is relatively high.

The Role of Imaging in the Detection and Management of COVID-19: A Review

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnosis and treatment assessment of the disease. Herein, we review the imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography - CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play crucial roles in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.

Sensitive quantitative image analysis of bisulfite based on near-infrared upconversion luminescence total internal reflection platform

Bisulfite (HSO3−), serves as an important additive in food industry, is one of the most widely distributed environmental pollutants. Herein, a fast and efficient quantitative image analysis method for the determination of HSO3− has been developed. The method builds a luminescence energy transfer (LET) system utilized upconversion nanoparticles (UCNPs) as an energy donor and cyanine dye molecules as an energy acceptor. The upconversion luminescence is quenched a lot on the addition of dye molecules and gets recovered well with the addition of HSO3−. All the phenomena can be recorded via traditional luminescence spectrometer and near-infrared upconversion luminescence total internal reflection platform. The quantitative image analysis performed on the near-infrared upconversion luminescence total internal reflection platform can significantly reduce sample consumption (10 μL) as well as make a quick and efficient analysis (0.1 s) with a large amout of data become easy. Meanwhile, it shows a wider linear range (1–120 μM), lower detection limit (0.070 μM) and higher detection speed than that of the classical luminescence spectrometer.

Abstract 17071: Cell Communication Inference in Macrophages

Introduction: Tissue macrophages play diverse roles in the cardiovascular system during health and disease. They have diverse functions within tissues, but our understanding of their dynamics is limited because most macrophage characterization assays are destructive and have low temporal resolution. We asked whether these cells are dynamic and interconnected. Methods: Here, we describe experimental and analytical methods for measuring cell dynamics and inferring communication between cells in vitro and in vivo. We created a mouse (Csf1r-Cre x GCaMP5) expressing the Cre-inducible genetically encoded calcium indicator GCaMP5 under the regulation of the innate immune promoter, Csf1r, to non-destructively quantify high-frequency cell dynamics and differentiated them in culture using m-CSF. We developed custom image analysis routines and parameterization strategies for classifying calcium responses. Results: Our studies revealed that calcium reporter BMDMs display minimal fluctuations at baseline but exhibit a dynamic response to immunogenic DNA sensing. DNA-induced isolated cell injury and death, which precipitated cell communication that spread with a velocity of [9μm/s], consistent with an extracellular diffusion mechanism. We developed quantitative image analysis methods that corrected for random calcium fluctuations and identified statistically significant areas of correlated calcium changes suggestive of communication. An analytical pipeline enabled quantification of calcium spike dynamics and correlations of dynamic calcium profiles of single cell sharing a local microenvironment. This resulted in an “improbable synchrony” metric that allowed localization of communication in time and space. We adapted the pipeline for in vivo studies and tested them in a dorsal window chamber model using intravital microscopy. At 2Hz sampling frequency, we identified 27 potential communication events as they responded to complex microenvironmental cues in vivo. Conclusion: The experimental and analytical methods for inferring cell communication provide a new quantitative toolkit for investigating known as-yet undiscovered cell communication pathways.

Automated phantom analysis for gamma cameras and SPECT: A methodology for use in a clinical setting.

PurposeWe introduce an automated, quantitative image analysis package for gamma camera and single photon emission computed tomography quality control. Our focus was to produce consistent methods that are feasible in clinical settings and use standard phantoms.MethodsFour gamma cameras were used to acquire planar images of four‐quadrant bar phantoms and projection views of an American College of Radiology (ACR) phantom as part of a standard gamma camera quality control program. Images were sent to QC‐Track® (Atirix Medical Systems, Inc., Minneapolis, MN, USA), which automatically placed predetermined regions of interest (ROIs) and performed analysis. For the bar phantom, a standard deviation (SD)‐based modulation transfer function was calculated for a circular ROI in each quadrant. The bar widths at various MTF values were reported using linear interpolation as applicable. For the ACR phantom, the contrast‐to‐noise ratio (CNR) for each sphere, a modulation for each rods section, and a percent deviation for uniformity ROIs was calculated. Spheres corresponding to a CNR of 3, and the rod size at various modulations were also reported using linear interpolation. Visual analysis was performed by three medical physicists to evaluate interobserver variability and correlation to quantitative values.ResultsAnalysis of the bar phantom showed predictable differences with changes in matrix size and bar width and showed consistency over similar acquisitions over the course of the study. Analysis of the ACR Phantom showed increasing CNR and modulation with increasing sphere and rod diameter, as expected. For both phantoms, quantitative values from linear interpolation correlated well with visual analysis.ConclusionOur automated method for quantitative image analysis is consistent and shows increased precision and sensitivity when compared to standard visual methods. Thresholds correspond well with visual analysis and previous guidelines for observer visibility (e.g., Rose criterion), making our framework suitable for routine use in a nuclear medicine department.