Optical coherence tomography (OCT) is a powerful imaging technique to non-invasively differentiate between healthy skin and pathological conditions. Unfortunately, commercially available OCT-systems are typically slow and not capable of scanning large areas at reasonable speed. Since skin lesions may extend over several square centimeters, potential inflammatory infiltrates remain undetected. Here, we present large area robotically assisted OCT (LARA-OCT) for skin imaging. Therefor a collaborative robot is combined with an existing, home-built 3.3 MHz-OCT-system and for surface tracking an online probe-to-surface control is implemented which is solely based on the OCT surface signal. It features a combined surface-distance and surface-orientation closed-loop control algorithm, which enables automatic positioning and alignment of the probe across the target while imaging. This allows to acquire coherent OCT images of skin areas beyond 10 cm².

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In neurosurgical tumor operations on the central nervous system, intraoperative haptic information often assists for discrimination between healthy and diseased tissue. Thus, it can provide the neurosurgeon with additional intraoperative source of information during resection, next to the visual information by the light microscope, fluorescent dyes and neuronavigation. One approach to obtain elastic and viscoelastic tissue characteristics non-subjectively is phase-sensitive optical coherence elastography (OCE), which is based on the principle of optical coherence tomography (OCT). While phase-sensitive OCE offers significantly higher displacement sensitivity inside a sample than commonly used intensity-based correlation methods, it requires a reliable algorithm to recover the phase signal, which is mathematically restricted in the -π to π range. This problem of phase wrapping is especially critical for inter-frame phase analysis since the time intervals between two referenced voxels is long. Here, we demonstrate a one-dimensional unwrapping algorithm capable of removing up to 4π-ambiguities between two frames in the complex phase data obtained from a 3.2 MHz-OCT system. The high sampling rate allows us to resolve large sample displacements induced by a 200 ms air pulse and acquires pixel-precise detail information. The deformation behavior of the tissue can be monitored over the entire acquisition time, offering various subsequent mechanical analysis procedures. The reliability of the algorithm and imaging concept was initially evaluated using different brain tumor mimicking phantoms. Additionally, results from human ex vivo brain tumor samples are presented and correlated with histological findings supporting the robustness of the algorithm.

Optical coherence elastography (OCE) offers the possibility of obtaining the mechanical behavior of a tissue. When also using a non-contact mechanical excitation, it mimics palpation without interobserver variability. One of the most frequently used techniques is phase-sensitive OCE. Depending on the system, depth-resolved changes in the sub-µm to nm range can be detected and visualized volumetrically. Such an approach is used in this work to investigate and detect transitions between healthy and tumorous brain tissue as well as inhomogeneities in the tumor itself to assist the operating surgeon during tumor resection in the future. We present time-resolved, phase-sensitive OCE measurements on various ex vivo brain tumor samples using an ultra-fast 3.2 MHz swept-source optical coherence tomography (SS-OCT) system with a frame rate of 2.45 kHz. 4 mm line scans are acquired which, in combination with the high imaging speed, allow monitoring and investigation of the sample's behavior in response to the mechanical load. Therefore, an air-jet system applies a 200 ms short air pulse to the sample, whose non-contact property facilitates the possibility for future in vivo measurements. Since we can temporally resolve the response of the sample over the entire acquisition time, the mechanical properties are evaluated at different time points with depth resolution. This is done by unwrapping the phase data and performing subsequent assessment. Systematic ex vivo brain tumor measurements were conducted and visualized as distribution maps. The study outcomes are supported by histological analyses and examined in detail.