Currently, swept-source OCT is more often implemented for endoscopic applications than spectral-domain OCT. Currently, commercially available swept laser sources have higher line-scan rates than that of line-scan cameras used for spectrometers for the wavelength of 1,310 nm, which is commonly used in dense tissue imaging. The sensitivity falloff range in a swept-source system is usually more than 6 mm, compared to less than 2 mm in spectral-domain OCT. The complex conjugate artifact can be more easily resolved, e.g., with frequency shifting [63] or other technologies. Swept-source OCT can also be built with all fiber-optic components, without the need to design and maintain a high-speed spectrometer. While SDOCT may potentially have advantages over SSOCT in terms of axial resolution and phase stability, and high-quality EOCT has been implemented in both SSOCT and SDOCT configurations [31, 64, 65], in the foreseeable future, swept-source OCT will likely remain the preferred configuration for gastroenterologic applications.

The fiber-optic probe guides the light from the EOCT system to the internal organ, focuses the beam onto the mucosa of the luminal wall, and collects the image-bearing backscattered light. A small outer diameter and short rigid parts are preferred for ease of use, especially when the probe in intended to be introduced through the accessory channel of an endoscope. The side-viewing design is most commonly used. When driven in a helical scanning pattern, the probing beam can scan the inner wall of the tubular organs. The probe may be used with or without a centering balloon, depending on the application and the needed optical performance. Balloon-based probes have long working distances, corresponding to the diameter of the balloon. The probe beam passes through the balloon and is maintained in focus on the entire circumference. They are mostly used for large, straight tubular organs, e.g., esophagus. For a typical (Gaussian) beam, the consequence of a long working distance is a larger beam waist, in other words, lower lateral resolution. Probes without balloons typically have a working distance barely longer than the outer protecting catheter sheath and may therefore be focused to a sharper spot. They are placed directly adjacent to the tissue when imaging, and if the organ has a large diameter, only a portion of the circumference is scanned. Short working distance probes are the better choice for imaging pancreatic/bile duct, which have small lumens, and colon if it is to be imaged in a targeted, rather than comprehensive way.

Figure 67.2a shows an example of a probe design for a balloon catheter [52]. A single-mode fiber was fixed in a ferrule followed by a glass spacer to expand the beam before entering a GRIN lens. A separate cylinder rod lens was attached after the GRIN lens to correct the astigmatism induced by the protecting sheath. All optical components have polished surfaces angled at 8° to prevent back reflection. The length of the spacer may be selected to result in the desired working distance of the lens group. The lens group and ferrule were assembled in a metal housing. A gold mirror was glued at the tip of the metal housing to deflect the beam by 80° relative the optical axis. The overall numerical aperture of the probe is 0.024. The total outer diameter of the fiber probe, including the metal housing, is 1.7 mm. The rigid tip (medal housing) is about 2 cm long. Figure 67.2b shows the size of an assembled probe without the metal housing. Figure 67.2c shows the lateral beam profile at a 9 mm working distance, as measured by a beam analyzer. The spot had a minimal ellipticity of 0.98 with a FWHM diameter of 33 μm.

In order to perform the helical pattern scanning, a rotary motor and a translational motor were utilized to drive the proximal end of a flexible shaft. The metal housing of the probe was attached to the distal end of the flexible shaft. The flexible shaft can mostly prevent nonuniform rotary distortion (NURD). To further suppress NURD, The position encoder in the rotary motor was used to trigger the OCT A-scan acquisition at equal angular increments.

BA balloon centers a long working distance probe to maintain focus and to stabilize the probe to suppress motion artifacts. The conventional balloon design uses a single balloon, with its two necks attached to the outer sheath using biocompatible epoxy. The imaging probe is placed within the balloon, and the probe beam passes through the balloon and is focused into the adjacent mucosa. It has been shown that catheter pressure on colonic mucosa significantly alters the tissue appearance [66]. This raises the question of whether balloon pressure or contact compresses the mucosa and affects OCT image features that are diagnostic of dysplasia within BE.

Double balloons were proposed as an alternative design [52]. Figure 67.3a illustrates the double-balloon design that allows two imaging schemes to be employed (with and without balloon-tissue contact) to investigate the influence of pressure. For imaging with no balloon-tissue contact, the images were obtained in the gap between the two balloons (labeled beam position (1)). The fiber probe could also be placed within the distal balloon (labeled beam position (2)) to acquire images through the balloon in the conventional way. The length of the gap was well controlled in order to limit the extent of lumen contraction between the balloons and keep the tissue within the axial imaging range. Holes were made in the outer sheath within the balloons so that they were inflated and deflated from the proximal end of the catheter. The outer sheath had an outer diameter of 2.4 mm. The small diameter allowed the catheter to be inserted through the accessory channel of the endoscope (Fig. 67.3b). The deployment and operation of the catheter probe were observed from the camera of the endoscope, so that images were acquired from the esophageal segments of interest (Fig. 67.3c). Figure 67.3d represents a swine esophageal image in anatomic (cross-sectional) view. The diameters of the imaged esophagi were around 18 mm, but in order to visualize the tissue structure, the images are displayed with a diameter of about 3 mm.

In previous sections, several methods have been introduced for the purpose of motion artifact suppression, i.e., triggering with the encoder in the rotary motor, rotating the probe with a flexible shaft, and stabilizing it with the balloon. However, when imaging living subjects, motion artifact may not be completely suppressed. A study has shown that the motion artifact can be caused by physiological motion, e.g., respiration and heart beating [57]. Figure 67.4a demonstrates an example of motion artifact in the swine esophagus in vivo. Cross-sectional images were acquired for one minute without pulling the probe and stacked in the order of time (time axis). All of the above motion suppression methods were applied. Ideally without motion, these images should be identical. However, the time-radial plane (Fig. 67.4c) illustrates how the reconstruction is substantially distorted radially. Figure 67.4b is an en face plane extracted from the same volume within the layer of the muscularis mucosa (approximately 300 μm from the mucosal surface). In this view, rotational distortion is clearly observed. Motion artifact along the longitudinal direction (perpendicular to the cross-section) is also prominent. However it cannot be clearly visualized without additional imaging mechanism (i.e., Doppler shift detection [67]). Here, motion artifacts refer to the radial and rotational components if not otherwise specified.

Kang et al. proposed an automatic image registration method to suppress motion artifact during esophageal imaging [57]. Registration started with the detection of esophageal lumen, assuming the air-tissue interface had the highest radial gradient. The detection was formulated as a global optimization problem, which sought a contiguous curve with only one pixel from each A-scan. The summation of the radial gradient value on the curve was a global maximum among all the possible curves. To correct for radial motion, the detected lumen surface was aligned to a circle by translating each A-scan in the radial direction. Local box matching (LBM) was then applied to estimate the rotational motion. LBM matched large regions of interest (ROI) in each image to the previous image by cross-correlation and therefore estimated the bulk rotational motion artifact of the ROIs. Each ROI consisted of a few hundred of A-scans. The rotational position of each individual A-scan was then finely adjusted by considering the bulk motion of the adjacent ROIs in the same cross-sectional image. This method can be categorized as a template update method in the field of computer vision [68]. In this case, the previous cross-sectional image was the template. Accumulated registration error occurred because LBM did not distinguish tissue motion from the actual variation in the anatomical structure. To visualize the natural tissue structure, the slowly varying components in the motion trace were preserved during the final image reconstruction.

CAD algorithms have been proposed to analyze colonic crypt morphological patterns [69] and to classify dysplasia in BE [45, 46]. CAD algorithms usually consist of steps including region of interest (ROI) segmentation, feature extraction and selection, and finally classification. It is critical to first identify appropriate and quantifiable target features for computer processing before CAD algorithm development. The straightforward approach is to adopt and quantify the diagnostic criteria established by human expert readers.

EOCT visualizes the 3D structure of the colonic mucosa, which is one of the advantages over other competing colonoscopic imaging technologies, such as high magnification chromoendoscopy. The crypts within aberrant crypt foci (ACF) have larger lumens and are oriented less parallel to each other in three dimension than normal crypts are [70]. Qi et al. proposed to characterize the crypt morphology using 3D image processing to quantify these 3D morphological features [69]. The process was demonstrated using freshly resected segments of colon from patients undergoing surgery to remove cancer. Crypts were first segmented on each en face section of the 3D dataset. Features, such as area of the lumen and density of crypts (counts per square millimeter), were calculated. The centroids of each segmented crypt were then connected to represent the crypt morphological skeleton. Straightness (i.e., a skeletons’ spatial linearity) and parallelism (i.e., the similarity of the skeletons’ spatial orientation) were computed. All the features were quantified for ACF, normal tissue, and normal-appearing tissue adjacent to cancer (NA). Different feature values were observed between ACF and normal/NA tissue, which may potentially be useful for differentiating abnormal crypts from data acquired in vivo.

CAD algorithms have also been developed to potentially assist in diagnosis of dysplasia in BE [45, 46]. The transformation from normal to dysplastic mucosa in BE includes the size, shape, and density of epithelial nuclei, which affects the tissue attention and backscattering properties. Loss of structure associated with normal histological organization (i.e., homogeneity, layering) has been observed in dysplastic tissue [37, 43, 71]. Some of these previous observations were quantified, such as backscattering coefficient and intensity-based texture parameters, for CAD algorithms. New features, such as the stripe pattern associated with surface topology, were also proposed for CAD algorithms [72, 73]. A stripe pattern was often observed in non-dysplastic tissue and seldom observed in dysplastic tissue. This pattern was quantified by the stripe density and orientation. This method was demonstrated using clinical EOCT images from patients undergoing regularly scheduled BE surveillance, where diagnoses were confirmed by biopsy. The receiver operating characteristic (ROC) curve of each feature was calculated to select those with best classification accuracy. Principle component analysis was applied to reduce feature redundancy and increase algorithm robustness. Only a few principle components that accounted for the most variance in the feature space were selected as input for the multivariate classification algorithms. Multiple classification methods were compared. In this case, decision tree combined with bootstrapping yielded best classification accuracy. Bootstrapping consisted of construction of multiple classification training sets, each using a randomly selected subset of the EOCT training images. Each training set generated a decision tree, and then each test image was classified by majority voting of those decision trees [45, 46].

Swine esophagus is a commonly used animal model for in vivo studies to demonstrate feasibility of new technology. Kang et al. reported a spectral-domain EOCT system together with double-balloon catheter probe [52]. The fiber probe was rotated at 10 rev/s and pulled back at 2 cm per minute. One centimeter to three centimeter segments of esophagus were imaged. Each cross-sectional frame consisted of 4,700 A-scans. The image resolution was 9 μm, 33 μm, and 33 μm in radial, rotational, and longitudinal directions, respectively. The image voxel size was 4 μm, 11 μm, and 33 μm in the three directions. Swine were sedated, intubated, ventilated, and maintained under anesthesia for the duration of the procedure. During the imaging study, video endoscopy was used to guide the probe deployment and through the endoscope accessory channel and balloon inflation.

Both double-balloon and single-balloon images were obtained from the same esophageal segment (Fig. 67.5) in order to evaluate relative image quality and the effects of the pressure of the balloon on the OCT appearance of the mucosa. Figure 67.5a is a representative cross-sectional image of the swine esophagus without balloon-tissue contact. The same radially compressed visualization method used in Fig. 67.3d is applied here. Tissue layers and structures can be clearly observed, including the squamous epithelium, lamina propria, muscularis mucosa, submucosa, muscularis propria, and blood vessels in the muscularis mucosa. The imaging depth exceeds 1 mm. Figure 67.5b shows a 3D reconstruction visualizing a 17 mm long segment obtained within 50 s, using the double-balloon scheme. Figure 67.5c, d show a representative cross-sectional image and the corresponding 3D reconstruction obtained with the single-balloon scheme (10 mm segment obtained within 30 s). The typical layers of the esophagus can also be clearly recognized. The imaging depth in the single-balloon images is greater than that of the double balloon, with multiple layers of muscularis propria visible. However, detailed structure in the muscularis mucosa, especially blood vessels, is more clearly observed in the double-balloon images. The glandular structure and the blood vessels are barely recognized in the single-balloon image. The natural mucosal surface topology is apparent in the double-balloon image, whereas the mucosal surface was compressed and smoothed by the balloon in the single-balloon image. The balloon-flattened surface resulted in more stable illumination and therefore more uniform image brightness. Also, the tissue motion artifact was less significant with single-balloon imaging due to the balloon support. These observations suggest the need to further investigate the advantages and disadvantages of balloon-tissue contact and pressure, how tissue features of diagnostic significance in BE are altered, and how the changes affect detection of dysplasia in BE.