K-distribution three-dimensional mapping of biological tissues in optical coherence tomography

Volumetric tumor delineation and assessment of its early response to radiotherapy with optical coherence tomography

Texture analyses of optical coherence tomography (OCT) images have shown initial promise for differentiation of normal and tumor tissues. This work develops a fully automatic volumetric tumor delineation technique employing quantitative OCT image speckle analysis based on Gamma distribution fits. We test its performance in-vivo using immunodeficient mice with dorsal skin window chambers and subcutaneously grown tumor models. Tumor boundaries detection is confirmed using epi-fluorescence microscopy, combined photoacoustic-ultrasound imaging, and histology. Pilot animal study of tumor response to radiotherapy demonstrates high accuracy, objective nature, novelty of the proposed method in the volumetric separation of tumor and normal tissues, and the sensitivity of the fitting parameters to radiation-induced tissue changes. Overall, the developed methodology enables hitherto impossible longitudinal studies for detecting subtle tissue alterations stemming from therapeutic insult.

Optical Interferometric Fringe Pattern-Incorporated Spectrum Calibration Technique for Enhanced Sensitivity of Spectral Domain Optical Coherence Tomography

Depth-visualizing sensitivity can be degraded due to imperfect optical alignment and non-equidistant distribution of optical signals in the pixel array, which requires a measurement of the re-sampling process. To enhance this depth-visualizing sensitivity, reference and sample arm-channeled spectra corresponding to different depths using mirrors were obtained to calibrate the spectrum sampling prior to Fourier transformation. During the process, eight interferogram patterns corresponding to point spread function (PSF) signals at eight optical path length differences were acquired. To calibrate the spectrum, generated intensity points of the original interferogram were re-indexed towards a maximum intensity range, and these interferogram re-indexing points were employed to generate a new lookup table. The entire software-based process consists of eight consecutive steps. Experimental results revealed that the proposed method can achieve images with a high depth-visualizing sensitivity. Furthermore, the results validate the proposed method as a rapidly performable spectral calibration technique, and the real-time images acquired using our technique confirm the simplicity and applicability of the method to existing optical coherence tomography (OCT) systems. The sensitivity roll-off prior to the spectral calibration was measured as 28 dB and it was halved after the calibration process.

Optical coherence tomography velocimetry based on decorrelation estimation of phasor pair ratios (DEPPAIR)

Quantitative velocity estimations in optical coherence tomography requires the estimation of the axial and lateral flow components. Optical coherence tomography measures the depth resolved complex field reflected from a sample. While the axial velocity component can be determined from the Doppler shift or phase shift between a pair of consecutive measurements at the same location, the estimation of the lateral component for in vivo applications is still challenging. One approach to determine lateral velocity is multiple simultaneous measurements at different angles. In another approach the lateral component can be retrieved through repeated measurements at (nearly) the same location by an analysis of the decorrelation over time. In this paper we follow the latter approach. We describe a model for the complex field changes between consecutive measurements and use it to predict the uncertainties for amplitude-based, phase-based and complex algorithms. The uncertainty of the flow estimations follows from a statistical analysis and is determined by the number of available measurements and the applied analysis method. The model is verified in phantom measurements and the dynamic range of velocity estimations is investigated. We demonstrate that phase-based and complex (phasor) based lateral flow estimation methods are superior to amplitude-based algorithms.

Signal averaging improves signal-to-noise in OCT images: But which approach works best, and when?

The high acquisition speed of state-of-the-art optical coherence tomography (OCT) enables massive signal-to-noise ratio (SNR) improvements by signal averaging. Here, we investigate the performance of two commonly used approaches for OCT signal averaging. We present the theoretical SNR performance of (a) computing the average of OCT magnitude data and (b) averaging the complex phasors, and substantiate our findings with simulations and experimentally acquired OCT data. We show that the achieved SNR performance strongly depends on both the SNR of the input signals and the number of averaged signals when the signal bias caused by the noise floor is not accounted for. Therefore we also explore the SNR for the two averaging approaches after correcting for the noise bias and, provided that the phases of the phasors are accurately aligned prior to averaging, then find that complex phasor averaging always leads to higher SNR than magnitude averaging.

Pilot feasibility study of in vivo intraoperative quantitative optical coherence tomography of human brain tissue during glioma resection

This study investigates the feasibility of in vivo quantitative optical coherence tomography (OCT) of human brain tissue during glioma resection surgery in six patients. High-resolution detection of glioma tissue may allow precise and thorough tumor resection while preserving functional brain areas, and improving overall survival. In this study, in vivo 3D OCT datasets were collected during standard surgical procedure, before and after partial resection of the tumor, both from glioma tissue and normal parenchyma. Subsequently, the attenuation coefficient was extracted from the OCT datasets using an automated and validated algorithm. The cortical measurements yield a mean attenuation coefficient of 3.8 ± 1.2 mm-1 for normal brain tissue and 3.6 ± 1.1 mm-1 for glioma tissue. The subcortical measurements yield a mean attenuation coefficient of 5.7 ± 2.1 and 4.5 ± 1.6 mm-1 for, respectively, normal brain tissue and glioma. Although the results are inconclusive with respect to trends in attenuation coefficient between normal and glioma tissue due to the small sample size, the results are in the range of previously reported values. Therefore, we conclude that the proposed method for quantitative in vivo OCT of human brain tissue is feasible during glioma resection surgery.

One-to-one registration of en-face optical coherence tomography attenuation coefficients with histology of a prostatectomy specimen

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.