Optical Coherence Tomography (OCT) is a technique that allows imaging tissue in three spatial dimensions. Such a
technique makes it possible to examine the subsurface of the tissue. The depth of penetration into the tissue can be
tailored by tuning the wavelength of the light source. While in some cases it is desirable to obtain deep penetration of
the sample, when scanning for cancerous changes, it may only be necessary to penetrate the first few hundred
micrometres. The use of a shorter wavelength, while decreasing the penetration depth, will improve the resolution of the
instrument. While images from OCT systems contain speckle and other artefacts, there are methods of evaluating the
information by using image processing techniques. Of particular interest is the scattering coefficient that can be derived
from the OCT data. Using discriminant techniques on the scattering data (such as principal components analysis), gives
a sensitive way of differentiating between changes in structure in the tissue. An extensive data collection was performed
on cervical tissue using samples that ranged from normal to invasive cancer. The histopathology of each sample was
gathered and was classified from normal to cancer. The scattering profiles of the data were averaged and gradient
analysis was performed, showing that for small distances into the sample there is a significant difference between
scattering profiles between cancerous and normal tissue. PCA was also performed on the data showing grouping into
various stages of cancer.
Optical coherence tomography (OCT) allows non-invasive imaging of sub-surface structures in vivo, ideally without a
need for target preparation. In conventional OCT, the contrast for blood vessels depends on a variety of factors and can
be challenging. Speckle variance has been recognized as a method to enhance contrast for blood flow without the
application of contrast agents in OCT images.
Here, we demonstrate the possibility of extracting blood flow information from a volumetric OCT datasets that was
obtained routinely from a human participant. We used a commercially available OCT system with a clinical CE-mark.
The light source has a central wavelength of 1310 nm. Using Multi-Beam technology, the system achieves an isometric
resolution of better than 7.5 μm in tissue over the entire imaging depth of around 1 mm. At 1 mm image width, 21
frames (B-scans) per second can be imaged.
We used the speckle variance in order to enhance the contrast for blood vessels in vivo. This method allowed us
determining the presence and depth of blood flow within the 1 mm penetration depth, without dependence on direction
or orientation of the blood flow with respect to the scanning beam.
An optical coherence tomography (OCT) prediction algorithm is designed and tested on a data set of sample images (taken from vegetables and porcine tissues) to demonstrate proof of concept. Preprocessing and classification of data are fully automated, at a rate of 60,000 A-scans/min on a standard computer and can be considered to deliver in near real-time. A data set consisting of nine groups was classified correctly in 82% of cases after cross-validation. Sets of fewer groups reach higher rates. The algorithm is able to distinguish groups with strong visual similarity among several groups of varying resemblance. Surface recognition and normalizing to the surface are essential for this approach. The mean divided by the standard deviation is a suitable descriptor for reducing a set of surface normalized A-scans. The method enables grouping of separate A-scans and is therefore straightforward to apply on 3-D data. OCT data can reliably be classified using principal component analysis combined with linear discriminant analysis. It remains to be shown whether this algorithm fails in the clinical setting, where interpatient variation can be greater than the deviations that are investigated as a disease marker.
The lateral resolution of Fourier domain optical coherence tomography (FD-OCT)
systems is limited by the depth of focus that can be achieved over the desired imaging
depth at the chosen wavelength. Various solutions have been proposed such as Bessel
beams and computational methods; however these suffer from various practical
drawbacks. We present a novel optical set-up involving multiple optical channels that
does not suffer from these drawbacks and delivers at least double the resolution of a
single beam system. The theory of this approach is discussed, also the realisation in a
practical laboratory system, measurement results and initial application in assessing
oesophageal cancers and pre-cancers.
The keyword for management of cervical cancer is prevention. The present program within the UK, the 'National Health
Service (NHS) cervical screening programme' (NHSCSP), is based on cytology. Although the program has reduced the
incidence of cervical cancer, this program requires patient follow ups and relies on diagnostic biopsying. There is
potential for reducing costs and workload within the NHS, and relieving anxiety of patients. In this study, Optical
Coherence Tomography (OCT) was investigated for its capability to improve this situation. Our time domain bench top
system used a superluminescent diode (Superlum), centre wave length ~1.3 &mgr;m, resolution (air) ~15 &mgr;m. Tissue samples
were obtained according to the ethics approval by Gloucestershire LREC, Nr. 05/Q2005/123. 1387 images of 199
participants have been compared with histopathology results and categorized accordingly. Our OCT images do not reach
the clarity and resolution of histopathology. Further, establishing and recognizing features of diagnostic significance
seems difficult. Automated classification would allow one to take decision-making to move from the subjective appraisal
of a physician to an objective assessment. Hence we investigated a classification algorithm for its ability in recognizing
pre-cancerous stages from OCT images. The initial results show promise.
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