Phase analysis strategies for MHz OCE in the large displacement regime

Abstract

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.

Advanced FFT-based contrast approach for MHz optical coherence elastography

“…In the center of the red rectangle, the line scans were performed, with the respective 20 times averaged B-scan presented in Figure 1D. The sample displacement after 0.16 ms is shown in Figure 1E using the method described in [5]. Here, an adaptive histogram equalization is applied to normalized values to emphasize various structures within the map.…”

“…To verify the functionality of this approach, the results are compared with those obtained with conventional PhS-OCE, where only the phase signal of the acquired signal was analyzed. The unwrapping algorithm for retrieving the true phase has already been presented and its robustness and functionality have been verified by histological results [5][6][7].…”