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.

Ultrasonic Aspirator for Tissue Contact Detection: An Online Classification on Time-Series.

The goal of neurosurgical tumor surgery is to remove the tumor completely without damaging healthy brain structures and thereby impairing the patient's neurological functions. This requires careful planning and execution of the operation by experienced neurosurgeons using the latest intraoperative technologies to achieve safe and rapid tumor reduction without harming the patient. To achieve this goal, a standard ultrasonic aspirator designed for tissue removal is equipped with additional intraoperative tissue detection using machine learning methods.Since decision-making in a clinical context must be fast, online contact detection is critical. Data are generated on three types of artificial tissue models in a CNC machine-controlled environment with four different ultrasonic aspirator settings. Contact classification on artificial tissue models is evaluated on four classification algorithms: change point detection (CPD), random forest (RF), recurrent neural network (RNN) and temporal convolutional network (TCN). Data preprocessing steps are applied, and their impacts are investigated. All methods are evaluated on five-fold cross-validation and provide generally good results with a performance of up to 0.977±0.007 in mean F1-score. Preprocessing the data has a positive effect on the classification processes for all methods and consistently improves the metrics. Thus, this work indicates in a first step that contact classification is feasible in an online context for an ultrasonic aspirator. Further research is necessary on different tissue types, as well as hand-held use to more closely resemble the intraoperative clinical conditions.

Toward intraoperative tissue classification: exploiting signal feedback from an ultrasonic aspirator for brain tissue differentiation.

PURPOSE: During brain tumor surgery, care must be taken to accurately differentiate between tumorous and healthy tissue, as inadvertent resection of functional brain areas can cause severe consequences. Since visual assessment can be difficult during tissue resection, neurosurgeons have to rely on the mechanical perception of tissue, which in itself is inherently challenging. A commonly used instrument for tumor resection is the ultrasonic aspirator, whose system behavior is already dependent on tissue properties. Using data recorded during tissue fragmentation, machine learning-based tissue differentiation is investigated for the first time utilizing ultrasonic aspirators. METHODS: Artificial tissue model with two different mechanical properties is synthesized to represent healthy and tumorous tissue. 40,000 temporal measurement points of electrical data are recorded in a laboratory environment using a CNC machine. Three different machine learning approaches are applied: a random forest (RF), a fully connected neural network (NN) and a 1D convolutional neural network (CNN). Additionally, different preprocessing steps are investigated. RESULTS: Fivefold cross-validation is conducted over the data and evaluated with the metrics F1, accuracy, positive predictive value, true positive rate and area under the receiver operating characteristic. Results show a generally good performance with a mean F1 of up to 0.900 ± 0.096 using a NN approach. Temporal information indicates low impact on classification performance, while a low-pass filter preprocessing step leads to superior results. CONCLUSION: This work demonstrates the first steps to successfully differentiate healthy brain and tumor tissue using an ultrasonic aspirator during tissue fragmentation. Evaluation shows that both neural network-based classifiers outperform the RF. In addition, the effects of temporal dependencies are found to be reduced when adequate data preprocessing is performed. To ensure subsequent implementation in the clinic, handheld ultrasonic aspirator use needs to be investigated in the future as well as the addition of data to reflect tissue diversity during neurosurgical operations.

Experimental and Numerical Determination of the Local Temperature Distribution during Phacoemulsification and Comparison of Different Surgery Situations within Enucleated Porcine Eyes.

Phacoemulsification, a common treatment for cataract, is accompanied by cell damage at the corneal endothelium. Thermal exposure during treatment has been considered a reason for this damage, but a thorough experimental and theoretical assessment of the local temperature distribution inside the eye had not yet been conducted. Measurements in porcine eyes and a finite-element simulation enabled such an assessment and localized the highest temperature rise very close to the probe. The results described in this study indicate that a distance of 1 mm between the probe and the endothelium should be maintained during treatment as a safety margin, especially when fluid flow is blocked. The highest measured temperature rise with surgically reasonable system settings and unblocked fluid flow was 1.11°C. The finite-element simulation described here is able to calculate the temperature rise at the endothelium and could serve as a tool for comparing arbitrary surgical situations with respect to thermal exposure of the endothelium.

Computation of the temperature rise at the corneal endothelium during cataract surgery by modeling of heat generation inside the anterior chamber.

The corneal endothelium sustaining the transparency of the cornea is a vulnerable cell layer. Thermal exposure during phacoemulsification is considered to be a potential cause of post-operative cell loss. Knowledge of the temperature rise and particularly of its dependence on region and system settings could deliver useful information about a potential correlation with cell damage. However, there exists a lack of understanding of the process and location of heat generation. Analytical calculations and experiments enabled the quantification of different mechanisms acting as heat sources during phacoemulsification. Heat generation caused by viscous friction was estimated using both an analytical approach and a numerical simulation. In contrast to absorption of sound and self-heating of the probe, this effect was ascertained to be the main heat source. Calorimetric measurement of the power input verified this modeling. On the basis of these results, the local temperature distribution inside a porcine eye was computed time dependently using the finite-element method. Two different amplitude settings were compared with respect to the temperature increase at the corneal endothelium. Various conclusions on the mitigation of thermal exposure during treatment can be drawn from this finite-element simulation.