3 Label-Free, Ultrahigh-Speed, Direct Imaging and Tracking …
75
optical element along the beam path scatters light and contributes to the background.
The general goal of background estimation and correction is to achieve faithful,
artifact-free imaging of the signal without the presence of an undesired background.
Background correction should not be mistaken for noise reduction or image restoration [48, 49]. Here, the background, in the most general definition, refers to light
arriving at the detector that does not carry a signal of interest. The presence of a
background complicates or even prevents signal detection. Depending on the application, the signal of interest varies, and so does the background. For example, in
some applications, the cell structure and morphology are the signal targets, whereas
in other applications, the signal targets are sub-cellular organelles. These different
definitions of a signal lead to distinct strategies for estimating and removing the background. In this section, a few methods for background estimation and correction are
overviewed, and their performance in live cell imaging is illustrated. In addition, a
sophisticated background estimation method that can resolve the nanoscopic motion
of a nanoparticle throughout the observation time is discussed [50]. These methods
rely on the distinct spatial and temporal characteristics of a signal and background.
COBRI and iSCAT share background correction strategies because they both employ
scattering-based imaging interferometry.
The simplest measurement in microscopy is perhaps imaging spatially separated
particles attached on a clean coverglass. At first glance, such a measurement appears
to have no source of an undesired background. However, when the particles are
extremely small and have a weak visibility of 0.01 or less, a spatially heterogeneous
background due to imperfect, non-uniform illumination begins to complicate any
measurement. The illumination background can be measured using several methods. One method is to capture the background before the particle appears on the
coverglass—this method is especially useful in the application of “landing assays”
[51–53]. If the particle is already attached to the coverglass before the measurement,
a common method is to modulate the lateral position of the sample, during which
a video is recorded. The static illumination background can be extracted from the
moving signal in the video. This can be accomplished by calculating the temporal
median background of the video. Alternatively, the signal modulated at a specific
frequency can be reconstructed through Fourier analysis. This method of spatial
modulation works well for estimating and removing the illumination background,
but it does not correct the background caused by the sample (e.g., by roughness
of the coverglass) because it moves together with the signal. In the most sensitive
COBRI and iSCAT imaging, the scattering background caused by the roughness of
the coverglass prevents direct visualization of a weak signal [52].
In many applications, a particle is in constant motion, and the task is to measure
this motion. This provides an opportunity for convenient background correction.
By recording a video containing the dynamic particle of interest, a static background can be extracted that represents the combined effect of illumination, coverglass roughness, and any other background contributions that are relatively static
within the observation time. Correcting that background from the raw video gives a
background-free video showing only the signal. This method is particularly powerful
in applications of SPT where a particle of interest moves over the observation area. In
75
optical element along the beam path scatters light and contributes to the background.
The general goal of background estimation and correction is to achieve faithful,
artifact-free imaging of the signal without the presence of an undesired background.
Background correction should not be mistaken for noise reduction or image restoration [48, 49]. Here, the background, in the most general definition, refers to light
arriving at the detector that does not carry a signal of interest. The presence of a
background complicates or even prevents signal detection. Depending on the application, the signal of interest varies, and so does the background. For example, in
some applications, the cell structure and morphology are the signal targets, whereas
in other applications, the signal targets are sub-cellular organelles. These different
definitions of a signal lead to distinct strategies for estimating and removing the background. In this section, a few methods for background estimation and correction are
overviewed, and their performance in live cell imaging is illustrated. In addition, a
sophisticated background estimation method that can resolve the nanoscopic motion
of a nanoparticle throughout the observation time is discussed [50]. These methods
rely on the distinct spatial and temporal characteristics of a signal and background.
COBRI and iSCAT share background correction strategies because they both employ
scattering-based imaging interferometry.
The simplest measurement in microscopy is perhaps imaging spatially separated
particles attached on a clean coverglass. At first glance, such a measurement appears
to have no source of an undesired background. However, when the particles are
extremely small and have a weak visibility of 0.01 or less, a spatially heterogeneous
background due to imperfect, non-uniform illumination begins to complicate any
measurement. The illumination background can be measured using several methods. One method is to capture the background before the particle appears on the
coverglass—this method is especially useful in the application of “landing assays”
[51–53]. If the particle is already attached to the coverglass before the measurement,
a common method is to modulate the lateral position of the sample, during which
a video is recorded. The static illumination background can be extracted from the
moving signal in the video. This can be accomplished by calculating the temporal
median background of the video. Alternatively, the signal modulated at a specific
frequency can be reconstructed through Fourier analysis. This method of spatial
modulation works well for estimating and removing the illumination background,
but it does not correct the background caused by the sample (e.g., by roughness
of the coverglass) because it moves together with the signal. In the most sensitive
COBRI and iSCAT imaging, the scattering background caused by the roughness of
the coverglass prevents direct visualization of a weak signal [52].
In many applications, a particle is in constant motion, and the task is to measure
this motion. This provides an opportunity for convenient background correction.
By recording a video containing the dynamic particle of interest, a static background can be extracted that represents the combined effect of illumination, coverglass roughness, and any other background contributions that are relatively static
within the observation time. Correcting that background from the raw video gives a
background-free video showing only the signal. This method is particularly powerful
in applications of SPT where a particle of interest moves over the observation area. In