Phase unwrapping for MHz optical coherence elastography and application to brain tumor tissue.

During neuro-oncologic surgery, phase-sensitive optical coherence elastography (OCE) can be valuable for distinguishing between healthy and diseased tissue. However, the phase unwrapping process required to retrieve the original phase signal is a challenging and critical task. To address this issue, we demonstrate a one-dimensional unwrapping algorithm that recovers the phase signal from a 3.2 MHz OCE system. With a processing time of approximately 0.11 s per frame on the GPU, multiple 2π wraps are detected and corrected. By utilizing this approach, exact and reproducible information on tissue deformation can be obtained with pixel accuracy over the entire acquisition time. Measurements of brain tumor-mimicking phantoms and human ex vivo brain tumor samples verified the algorithm's reliability. The tissue samples were subjected to a 200 ms short air pulse. A correlation with histological findings confirmed the algorithm's dependability.

Mechanical characteristics of glioblastoma and peritumoral tumor-free human brain tissue.

BACKGROUND: The diagnosis of brain tumor is a serious event for the affected patient. Surgical resection is a crucial part in the treatment of brain tumors. However, the distinction between tumor and brain tissue can be difficult, even for experienced neurosurgeons. This is especially true in the case of gliomas. In this project we examined whether the biomechanical parameters elasticity and stress relaxation behavior are suitable as additional differentiation criteria between tumorous (glioblastoma multiforme; glioblastoma, IDH-wildtype; GBM) and non-tumorous, peritumoral tissue. METHODS: Indentation measurements were used to examine non-tumorous human brain tissue and GBM samples for the biomechanical properties of elasticity and stress-relaxation behavior. The results of these measurements were then used in a classification algorithm (Logistic Regression) to distinguish between tumor and non-tumor. RESULTS: Differences could be found in elasticity spread and relaxation behavior between tumorous and non-tumorous tissue. Classification was successful with a sensitivity/recall of 83% (sd = 12%) and a precision of 85% (sd = 9%) for detecting tumorous tissue. CONCLUSION: The findings imply that the data on mechanical characteristics, with particular attention to stress relaxation behavior, can serve as an extra element in differentiating tumorous brain tissue from non-tumorous brain tissue.