SignificanceWe assess the feasibility of using diffuse reflectance spectroscopy (DRS) and coherent anti-Stokes Raman scattering spectroscopy (CARS) as optical tools for human brain tissue identification during deep brain stimulation (DBS) lead insertion, thereby providing a promising avenue for additional real-time neurosurgical guidance.AimWe developed a system that can acquire CARS and DRS spectra during the DBS surgery procedure to identify the tissue composition along the lead trajectory.ApproachDRS and CARS spectra were acquired using a custom-built optical probe integrated in a commercial DBS lead. The lead was inserted to target three specific regions in each of the brain hemispheres of a human cadaver. Spectra were acquired during the lead insertion at constant position increments. Spectra were analyzed to classify each spectrum as being from white matter (WM) or gray matter (GM). The results were compared with tissue classification performed on histological brain sections.ResultsDRS and CARS spectra obtained using the optical probe can identify WM and GM during DBS lead insertion. The tissue composition along the trajectory toward a specific target is unique and can be differentiated by the optical probe. Moreover, the results obtained with principal component analysis suggest that DRS might be able to detect the presence of blood due to the strong optical absorption of hemoglobin.ConclusionsIt is possible to use optical measurements from the DBS lead during surgery to identify WM and GM and possibly the presence of blood in human brain tissue. The proposed optical tool could inform the surgeon during the lead placement if the lead has reached the target as planned. Our tool could eventually replace microelectrode recordings, which would streamline the process and reduce surgery time. Further developments are required to fully integrate these tools into standard clinical procedures.
Conventional dual-input state PS-OCT incorrectly assumes that the two probing input states provide equally reliable measurements. In this work, we overcome this assumption by adapting a maximum-likelihood framework which combines all input state and spectral bin measurements to find the most likely sample Jones matrix. This processing method (MLDIPS) shows a significantly reduced retardance noise floor as well as improved qualitative characterization of white matter versus grey matter in porcine brain tissue, displaying better contrast to conventional dual-input processing.
Reconstructed depth-resolved optic axis orientation obtained by catheter based PSOCT in a tissue volume informs on the orientation of the white matter fiber bundles in the brain, owing to the birefringence of myelinated axons. The physical organization of white matter also leads to anisotropic diffusion of water molecules, which is the basis of dMRI for non-invasive imaging of the three-dimensional orientation of white matter fiber bundles. Having access to fiber orientation in both imaging modalities, we are trying to map the depth-resolved birefringence and optic axis orientation to the larger scale dMRI as well as an atlas of brain anatomy.
Deep brain stimulation (DBS) surgery is performed on patients suffering Parkinson’s disease for whom medication is no longer effective in relieving their motor symptoms. In this surgery, a stimulating electrode is implanted in a specific structure deep within the brain, delivering electrical impulses and thus reducing the motor symptoms. The success of the surgery is highly dependent on placing the electrode accurately in the targeted structure, typically the subthalamic nucleus (STN). We developed a DBS electrode that includes optical fibers to perform coherent anti-Stokes Raman scattering (CARS) spectroscopy and diffuse reflectance spectroscopy (DRS) during the electrode insertion in the brain. We were able to identify white and grey matter using principal component analysis (PCA), showing that spectroscopic measurements could be suitable for neuronavigation.
Significance: An advanced understanding of optical design is necessary to create optimal systems but this is rarely taught as part of general curriculum. Compounded by the fact that professional optical design software tools have a prohibitive learning curve, this means that neither knowledge nor tools are easily accessible.
Aim: In this tutorial, we introduce a raytracing module for Python, originally developed for teaching optics with ray matrices, to simplify the design and optimization of optical systems.
Approach: This module is developed for ray matrix calculations in Python. Many important concepts of optical design that are often poorly understood such as apertures, aperture stops, and field stops are illustrated.
Results: The module is explained with examples in real systems with collection efficiency, vignetting, and intensity profiles. Also, the optical invariant, an important benchmark property for optical systems, is used to characterize an optical system.
Conclusions: This raytracing Python module will help improve the reader’s understanding of optics and also help them design optimal systems.
Recently, compression optical clearing (OC) was applied to detect dermal carotenoid using reflection spectroscopy. To enhance the precision and accuracy of reflection spectroscopy to better detect the spectral absorption of beta-carotene inside biological phantom, here, we simultaneously use compression and immersion OC using dimethyl sulfoxide. In addition, we analytically extract the absorption coefficient of beta-carotene using diffuse reflectance spectroscopy (as an analytical OC). Our results show that the presented analytical OC can be applied alone as a noninvasive method to measure cutaneous chromophores at deep tissues. Finally, we also improve the ability of the analytical clearing method mediated with experimental OC. Our result demonstrates that the combination of analytical and experimental clearing methods enhance the ability of diffuse reflection spectroscopy for extracting the absorption coefficient of beta-carotene as one of the chromospheres inside biological phantom.
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