Correlative Imaging
Correlative Imaging - Related Videos
02:20
Correlations
0 Views •
2020
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
07:34
Surgery and Sample Processing for Correlative Imaging of the Murine Pulmonary Valve
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2021
Here, we describe a correlative workflow for the excision, pressurization, fixation, and imaging of the murine pulmonary valve to determine the gross conformation and local extracellular matrix structures.
14:21
Optical Frequency Domain Imaging of Ex vivo Pulmonary Resection Specimens: Obtaining One to One Image to Histopathology Correlation
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Cited by 21 •
2013
A method to image ex vivo pulmonary resection specimens with optical frequency domain imaging (OFDI) and obtain precise correlation to histology is described, which is essential to developing specific OFDI interpretation criteria for pulmonary pathology. This method is applicable to other tissue types and imaging techniques to obtain precise imaging to histology correlation for accurate image interpretation and assessment. Imaging criteria established with this technique would then be...
08:45
Shrinkage of Dental Composite in Simulated Cavity Measured with Digital Image Correlation
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Cited by 12 •
2014
In order to understand the spatial development of polymerization shrinkage stress in dental resin-composite restorations, Digital Image Correlation was used to provide full-field displacement/strain measurement of restored model glass cavities by correlating images of the restoration taken before and after polymerization.
01:27
Correlation and Causation
0 Views •
2026
Correlation and CausationStatistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. A relationship between variables shows correlation, but it does not show cause-and-effect. A direct cause-and-effect relationship requires additional controlled experiments. If no consistent relationship exists between the variables, then there is no correlation.Correlation versus CausationIf the dependent variable increases or decreases when the...
FAQs
Preserving a specimen’s location and identity makes observations from different modalities comparable rather than isolated descriptions. The first dataset establishes where a feature appears, while the later dataset can be aligned to that same spatial context. This linkage lets researchers relate structural, molecular, or functional signals to one another within the biological sample.
The value of combining modalities comes from connecting different kinds of evidence about the same region. Structural information describes organization, molecular information identifies associated molecular features, and functional information indicates behavior or activity. Considering these signals together can produce a more comprehensive interpretation than relying on one contrast mechanism alone.
Spatial registration provides the shared framework needed to compare corresponding regions across datasets. When observations are aligned, a feature seen in one modality can be examined alongside information from another modality at the same location. This reduces ambiguity in interpreting complex samples and supports relationships between organization, molecular characteristics, and function.
A correlative imaging workflow begins by imaging the specimen with one method, then preserving the specimen’s location and identity before acquiring information with a second method. The resulting datasets are registered or aligned so corresponding regions can be compared. Maintaining this continuity is essential because the value lies in linking observations from the same sample.
In bioengineering, this approach can connect cell behavior with the architecture of a biomaterial, the organization of a tissue, or features of an engineered microenvironment. Such comparisons help researchers interpret how biological responses relate to the designed or surrounding structure, making correlative imaging relevant to characterization of engineered biological systems.
The integrated view supports characterization and evaluation in engineered biological systems. By examining structural, molecular, and functional information together, researchers can assess complex samples more comprehensively and use those observations to guide the design or evaluation of engineered systems. Its contribution is therefore not only descriptive; it links measurements to bioengineering goals.