During neurooncological surgery the intraoperative visual differentiation of healthy and diseased tissue is often challenging. In our prior work we demonstrated that imaging Mueller polarimetry is a promising tool for both ex- and in-vivo brain tissue differentiation and diagnosis. Apart from the superficial 2D-polarimetric maps of brain fiber tracts that can be generated with IMP, the knowledge of the probing tissue volume is crucial for the estimation of residual tumor thickness and the proximity of underlying fiber tracts. Here, we quantified the penetration depth of a probing light beam by evaluating the polarimetric maps of formalin-fixed (FF) human cerebral corpus callosum sections of different thicknesses measured in reflection, and we extended the analysis to FF gray matter brain sections of different thicknesses. Finally, we evaluated the light penetration depth at different wavelengths. Our findings allow us to define different thresholds of light penetration depth for white and gray brain matter.
Neurosurgical treatment is the primary approach for brain cancer, particularly gliomas, posing challenges due to their invasiveness and the imperative to maintain neurological function. Precise delineation of tumor margins becomes crucial to prevent neurological deficits and improve prognosis in neuro-oncological surgery. Intraoperative tumor border visualisation during neurosurgery finds a promising solution in imaging Mueller polarimetry. The development of tumor segmentation algorithms using polarimetric data requires a large and curated database of polarimetric measurement associated with the co-registered ground truth. We developed a neuropathology protocol to gather both histological and polarimetric data. Moreover, we implemented an image processing pipeline to obtain a precise mapping between histological and polarimetric data, allowing histological data to serve as a reliable ground truth for tissue characterisation. However, the histological processing steps, such as the freezing, cryosectioning and thawing of the samples, might alter the tissue microstructure and the polarimetric parameters of brain tissue. In this study, we extend the description of the neuropathology protocol by analysing the effect of the histological processing steps on the polarimetric properties of fresh thick brain specimens. We evaluated and compared polarimetric properties of fresh healthy and neoplastic brain tissue before and after applying the histological processing steps. We found a moderate effect of the latter on the polarimetric properties of both brain tissue types. The contrast in polarimetric parameters observed between different brain tissue types is conserved, as well as the ability to perform fiber tracking. Thus, the protocol facilitates a database of co-registered histological and polarimetric data.
The advent of polarization-sensitive cameras opens the avenue for real-time in-vivo polarimetric diagnostic imaging of biological tissues in clinical settings, but this approach allows measuring only the first three rows of 4×4 Mueller matrix. In order to extract diagnostically relevant images of tissue linear retardance, azimuth of the optical axis and depolarization from the partial Mueller matrix we have formulated a theoretical framework for the decomposition of 3×4 Mueller matrices and tested its validity on both simulated data for optical phantoms and experimental data collected from thick sections of formalin-fixed human brain measured in reflection. The polarimetric maps calculated with our algorithm and Lu-Chipman polar decomposition of the complete Mueller matrices demonstrate compelling correlation and preserve diagnostic image contrast.
Imaging Mueller polarimetry has already proved its potential for biomedical applications. However, tissue characterization utilizing all 16 elements of the Mueller matrix (MM) is not straightforward and requires data postprocessing decomposition algorithms. We developed the theoretical framework and performed the experimental studies on extracting the polarimetric parameters of phantoms and biological tissue while using only part of MM elements and validating them against the results of Lu-Chipman decomposition of corresponding complete MMs. Our findings open an avenue for developing simple and compact polarimetric systems operating at video rates that can be translated to clinics for real-time tissue diagnosis and monitoring.
SignificanceImaging Mueller polarimetry (IMP) appears as a promising technique for real-time delineation of healthy and neoplastic tissue during neurosurgery. The training of machine learning algorithms used for the image post-processing requires large data sets typically derived from the measurements of formalin-fixed brain sections. However, the success of the transfer of such algorithms from fixed to fresh brain tissue depends on the degree of alterations of polarimetric properties induced by formalin fixation (FF).AimComprehensive studies were performed on the FF induced changes in fresh pig brain tissue polarimetric properties.ApproachPolarimetric properties of pig brain were assessed in 30 coronal thick sections before and after FF using a wide-field IMP system. The width of the uncertainty region between gray and white matter was also estimated.ResultsThe depolarization increased by 5% in gray matter and remained constant in white matter following FF, whereas the linear retardance decreased by 27% in gray matter and by 28% in white matter after FF. The visual contrast between gray and white matter and fiber tracking remained preserved after FF. Tissue shrinkage induced by FF did not have a significant effect on the uncertainty region width.ConclusionsSimilar polarimetric properties were observed in both fresh and fixed brain tissues, indicating a high potential for transfer learning.
Neurosurgery is the first line treatment for most malignancies of the brain however intraoperative healthy and diseased tissue differentiation often remains a challenge. We have demonstrated earlier that wide-field Muller Polarimetry Imaging (MPI) is a promising approach for brain tissue differentiation and fiber tracking. To examine the technique’s versatility in a similar to in vivo setting, we used our system to create maps of polarimetric properties for tissue differentiation in cadaveric animal brains under neurosurgery-like conditions. We present the effects of ultrasonic cavitation on optical response and examined the challenges of a complex topography and blood presence in a surgical resection cavity.
The identification of the border between tumor and healthy brain tissue remains a main challenge in glioma surgery. To address this problem we suggest using the Mueller Polarimetric Imaging (MPI) operating in the visible spectral range. In our prior studies, we demonstrated the potential of MPI to assess the anisotropy of healthy brain tissue in fixed and fresh specimens. In this study, we use the MPI system in backscattering geometry in order to evaluate and determine the depth of light penetration through the evaluation of 2D surface polarimetric maps of the formalin-fixed human cerebral corpus callosum sections of different thicknesses.
Surgical resection is the first-line treatment for most malignancies of the brain. However, the intraoperative identification of brain tumor tissue remains a challenge. In previous work, we demonstrated the potential of wide-field Mueller Polarimetric Imaging (MPI) to assess the anisotropy of fresh and fixed specimens of healthy brain, independently. Now, we use the MPI system to acquire polarimetric maps of fresh cadaveric pig cerebral tissue and compare the parameter evolution over time following formaldehyde-fixation. We demonstrated that despite the apparition of tissue morphological changes induced by formaldehyde fixation, this process preserves the polarimetric properties, remaining quantitatively similar to fresh tissue ones.
Mueller matrix coefficients are conventionally derived from averaged measurements of several polarimetric intensity images for each polarisation state.
However, averaging large numbers of measurements is not compatible with real-time surgical applications.
To overcome this limitation, we introduce a novel learning-based denoising framework aiming at recovering accurate, physically consistent and high signal-to-noise ratio (SNR) polarimetric scans from short-time noisy acquisitions.
We formulate a microstructure-aware denoising diffusion network and validate against current state-of-the-art denoising techniques for real images in healthy and diseased brain samples.
Ultimately, the performance is analysed for near-real-time applicability and the advantage of the proposed approach is discussed.
Delineating the boundary of a tumors from healthy brain tissue is a challenging task in neurosurgery.
Mueller polarimetry imaging promises to visualise and segment these borders in real-time, based on optical properties correlated with the directionality of densely packed white-matter fiber-bundles.
In prior work, we demonstrated deep-learning methods leveraging Mueller polarimetry outperformed traditional approaches with similar segmentation tasks.
However, formalin-fixation vs. fresh sample tissue and differences of human vs. animal brain tissue properties may hinder the direct applicability to neurosurgical scenarios.
To overcome this potential limitation, we propose a learning-based strategy by jointly training on augmented multi-domain data together with model fine-tuning to improve tissue segmentation.
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