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Endomicroscopy is a technique for obtaining histology-like images from inside the human body in real-time, [1] [2] [3] a process known as ‘optical biopsy’. [ 4 ] [ 5 ] It generally refers to fluorescence confocal microscopy , although multi-photon microscopy and optical coherence tomography have also been adapted for endoscopic use.
This is a list of unsolved problems in chemistry. Problems in chemistry are considered unsolved when an expert in the field considers it unsolved or when several experts in the field disagree about a solution to a problem.
Confocal endoscopy, or confocal laser endomicroscopy (CLE), is a modern imaging technique that allows the examination of real-time microscopic and histological features inside the body. In the word "endomicroscopy", endo- means "within" and -skopein means "to view or observe".
These artifacts may be caused by a variety of phenomena such as the underlying physics of the energy-tissue interaction as between ultrasound and air, susceptibility artifacts, data acquisition errors (such as patient motion), or a reconstruction algorithm's inability to represent the anatomy.
Two-photon absorption (TPA) is a third-order process in which two photons are nearly simultaneously absorbed by the same molecule. If a second photon is absorbed by the same electron within the same quantum event, the electron enters an excited state.
Some errors are not clearly random or systematic such as the uncertainty in the calibration of an instrument. [4] Random errors or statistical errors in measurement lead to measurable values being inconsistent between repeated measurements of a constant attribute or quantity are taken. Random errors create measurement uncertainty.
Some errors are introduced when the experimenter's desire for a certain result unconsciously influences selection of data (a problem which is possible to avoid in some cases with double-blind protocols). [4] There have also been cases of deliberate scientific misconduct. [5]
Random experimental errors in the data contribute to even for a perfect model, and these have more leverage when the data are weak or few, such as for a low-resolution data set. Model inadequacies such as incorrect or missing parts and unmodeled disorder are the other main contributors to R {\displaystyle R} , making it useful to assess the ...