Diagnosis of Pituitary Adenoma Biopsies by Ultrahigh Resolution Optical Coherence Tomography Using Neuronal Networks.

Objective: Despite advancements of intraoperative visualization, the difficulty to visually distinguish adenoma from adjacent pituitary gland due to textural similarities may lead to incomplete adenoma resection or impairment of pituitary function. The aim of this study was to investigate optical coherence tomography (OCT) imaging in combination with a convolutional neural network (CNN) for objectively identify pituitary adenoma tissue in an ex vivo setting. Methods: A prospective study was conducted to train and test a CNN algorithm to identify pituitary adenoma tissue in OCT images of adenoma and adjacent pituitary gland samples. From each sample, 500 slices of adjacent cross-sectional OCT images were used for CNN classification. Results: OCT data acquisition was feasible in 19/20 (95%) patients. The 16.000 OCT slices of 16/19 of cases were employed for creating a trained CNN algorithm (70% for training, 15% for validating the classifier). Thereafter, the classifier was tested on the paired samples of three patients (3.000 slices). The CNN correctly predicted adenoma in the 3 adenoma samples (98%, 100% and 84% respectively), and correctly predicted gland and transition zone in the 3 samples from the adjacent pituitary gland. Conclusion: Trained convolutional neural network computing has the potential for fast and objective identification of pituitary adenoma tissue in OCT images with high sensitivity ex vivo. However, further investigation with larger number of samples is required.

Toward optical coherence tomography on a chip: in vivo three-dimensional human retinal imaging using photonic integrated circuit-based arrayed waveguide gratings.

In this work, we present a significant step toward in vivo ophthalmic optical coherence tomography and angiography on a photonic integrated chip. The diffraction gratings used in spectral-domain optical coherence tomography can be replaced by photonic integrated circuits comprising an arrayed waveguide grating. Two arrayed waveguide grating designs with 256 channels were tested, which enabled the first chip-based optical coherence tomography and angiography in vivo three-dimensional human retinal measurements. Design 1 supports a bandwidth of 22 nm, with which a sensitivity of up to 91 dB (830 µW) and an axial resolution of 10.7 µm was measured. Design 2 supports a bandwidth of 48 nm, with which a sensitivity of 90 dB (480 µW) and an axial resolution of 6.5 µm was measured. The silicon nitride-based integrated optical waveguides were fabricated with a fully CMOS-compatible process, which allows their monolithic co-integration on top of an optoelectronic silicon chip. As a benchmark for chip-based optical coherence tomography, tomograms generated by a commercially available clinical spectral-domain optical coherence tomography system were compared to those acquired with on-chip gratings. The similarities in the tomograms demonstrate the significant clinical potential for further integration of optical coherence tomography on a chip system.

Towards ultrahigh resolution OCT based endoscopical pituitary gland and adenoma screening: a performance parameter evaluation.

Ultrahigh resolution optical coherence tomography (UHR-OCT) for differentiating pituitary gland versus adenoma tissue has been investigated for the first time, indicating more than 80% accuracy. For biomarker identification, OCT images of paraffin embedded tissue are correlated to histopathological slices. The identified biomarkers are verified on fresh biopsies. Additionally, an approach, based on resolution modified UHR-OCT ex vivo data, investigating optical performance parameters for the realization in an in vivo endoscope is presented and evaluated. The identified morphological features-cell groups with reticulin framework-detectable with UHR-OCT showcase a promising differentiation ability, encouraging endoscopic OCT probe development for in vivo application.

Depth resolved label-free multimodal optical imaging platform to study morpho-molecular composition of tissue.

Multimodal imaging platforms offer a vast array of tissue information in a single image acquisition by combining complementary imaging techniques. By merging different systems, better tissue characterization can be achieved than is possible by the constituent imaging modalities alone. The combination of optical coherence tomography (OCT) with non-linear optical imaging (NLOI) techniques such as two-photon excited fluorescence (TPEF), second harmonic generation (SHG) and coherent anti-Stokes Raman scattering (CARS) provides access to detailed information of tissue structure and molecular composition in a fast, label-free and non-invasive manner. We introduce a multimodal label-free approach for morpho-molecular imaging and spectroscopy and validate the system in mouse skin demonstrating the potential of the system for colocalized acquisition of OCT and NLOI signals.