Biomedical Engineering Reference
In-Depth Information
motions of the animal cause especially high noise in dynamic SD-OCM imaging. These
motions make long-time laps measurements difficult. Therefore, motion correction
processing is required for such types of long-term dynamic imaging. Lee et al.
[17]
proposed a motion correction method that is especially suitable for phase-resolved dynamic
OCM imaging for recording temporal responses of the brain functionality.
Respiratory and cardiac motions contribute differently to image noise. Respiratory motions
cause bulk image shifts (BISs) and cardiac motions cause global phase fluctuations (GPFs).
A cross-correlation maximization-based shift correction algorithm was effective in
suppressing BISs, while GPFs were significantly reduced by removing axial and lateral
global phase variations. GPFs were suppressed by removing phase variations that are global
in either the axial or the lateral direction. By using a combination of BIS and GPF
correction algorithms, the motion artifacts were significantly reduced. To demonstrate the
use of the algorithm, dynamic imaging data from the rodent cerebral cortex were acquired
using an SD-OCM system. In addition, a nonorigin-centered GPF correction algorithm was
examined. Several combinations of these algorithms were tested to find an optimized
approach. It was demonstrated that the image stability was improved from 0.5 to 0.8 units
in terms of the cross-correlation over 4 s of dynamic imaging, with a reduction of phase
noise by two orders of magnitude in
B
8% voxels.
The light source consisted of two superluminescent diodes which yielded a combined
170-nm bandwidth centered at 1310 nm, giving an axial resolution of 3.5
m in tissue. The
spectrum of interfered light was measured with a 1024 pixel InGaAs line-scan camera at
47,000 spectra/s. A 5
3
objective was used, giving a transverse resolution of 7
μ
m in tissue.
The surface of the cortex was illuminated by another light source, with a wavelength of
570
6
5 nm. This light was used for imaging of the cortical surface by a CCD camera.
μ
13.4.4 Visualizing the Microvasculature within the Retina
Phase-resolved Doppler OCM measures the phase difference between adjacent A-scans,
which have to be recorded at overlapping positions within the sample. This phase difference
is directly proportional to the velocity of the moving particle. An extension of this idea was
proposed in Ref.
[18]
. The sample beams of two identical SD-OCM setups are scanned over
the object at different lateral positions. This generates two tomograms which are slightly
separated in time. During the post processing, both data sets are merged and an extended
algorithm is applied to extract a 3D capillary network tomogram of the retina. The
separation between both sample beams can be adjusted arbitrarily, and hence the velocity
measurement range can be freely chosen. By using this method, it was possible to
contrast the major vessels in the human retina in vivo as well as display the
microvasculature.
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