Optical coherence tomography (OCT) discriminates the backscattered light from a tissue sample based on the path length that the light has traveled, being exquisitely sensitive to light that has undergone one or a few scattering events.¹ This extraordinary ability is achieved largely through coherence gating, augmented by confocal gating, and simultaneously rejects out-of-focus light and imposes a selected path length. Depth-resolved images of this backscattering in tissue can be obtained, ex vivo and in vivo, with a resolution commonly in the range 5 to $15\mu \mathrm{m}$, although sub-$1\text{-}\mu \mathrm{m}$ resolution has been demonstrated.² Currently, OCT is primarily used to visualize the morphology of tissue, which can be used to differentiate pathology in some circumstances.³ There is further clinical value, in addition to visualization, to use OCT to differentiate pathology based on the altered structure and organization not readily visible with conventional clinically available imaging techniques. The structure and organization of a tissue are reflected in its optical properties,^4,5 and perhaps the most accessible such property in OCT is the attenuation coefficient, describing the extinction with depth of the detected OCT signal due to absorption and scattering. To measure the OCT attenuation coefficient (${\mu }_{\mathrm{OCT}}$) and obtain diagnostic information from this measure, a model of the OCT signal and a model correlating ${\mu }_{\mathrm{OCT}}$ to the optical properties (absorption and scattering coefficient; Sec. 2), and ultimately to the tissue structure, must be developed (Sec. 3).^{6 – 8} Recently, with the common advent of volumetric OCT imaging, it has become feasible to produce two-dimensional (2-D) en face, and even three-dimensional (3-D) depth-resolved maps of ${\mu }_{\mathrm{OCT}}$, representing an example of the general class of parametric imaging.⁹ Consequently, this topic has generated increased interest in the literature.^{10 – 12}

In this study; an OCT-based intra-operative imaging method for blood flow detection during esophagectomy with gastric tube reconstruction is investigated. Change in perfusion of the gastric tube tissue can lead to ischemia; with a high morbidity and mortality as a result. Anastomotic leakage (incidence 5–20%) is one of the most severe complications after esophagectomy with gastric tube reconstruction. Optical imaging techniques provide for minimal-invasive and real-time visualization tools that can be used in intraoperative settings. By implementing an optical technique for blood flow detection during surgery; perfusion can be imaged and quantified and; if needed; perfusion can be improved by either a surgical intervention or the administration of medication. The feasibility of imaging gastric microcirculation in vivo using optical coherence tomography (OCT) during surgery of patients with esophageal cancer by visualizing blood flow based on the speckle contrast from M-mode OCT images is studied. The percentage of pixels exhibiting a speckle contrast value indicative of flow was quantified to serve as an objective parameter to assess blood flow at 4 locations on the reconstructed gastric tube. Here; it was shown that OCT can be used for direct blood flow imaging during surgery and may therefore aid in improving surgical outcomes for patients.

Optical coherence tomography (OCT), enables high‐resolution 3D imaging of the morphology of light scattering tissues. From the OCT signal, parameters can be extracted and related to tissue structures. One of the quantitative parameters is the attenuation coefficient; the rate at which the intensity of detected light decays in depth. To couple the quantitative parameters with the histology one‐to‐one registration is needed. The primary aim of this study is to validate a registration method of quantitative OCT parameters to histological tissue outcome through one‐to‐one registration of OCT with histology. We matched OCT images of unstained fixated prostate tissue slices with corresponding histology slides, wherein different histologic types were demarcated. Attenuation coefficients were determined by a supervised automated exponential fit (corrected for point spread function and sensitivity roll‐off related signal losses) over a depth of 0.32 mm starting from 0.10 mm below the automatically detected tissue edge. Finally, the attenuation coefficients corresponding to the different tissue types of the prostate were compared. From the attenuation coefficients, we produced the squared relative residue and goodness‐of‐fit metric R². This article explains the method to perform supervised automated quantitative analysis of OCT data, and the one‐to‐one registration of OCT extracted quantitative data with histopathological outcomes.