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.
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.
A clear identification of the border between a brain tumor and surrounding healthy tissue during neurosurgery is essential in order to maximize tumor resection while preserving neurological function. However, tumor tissue is often difficult to differentiate from infiltrated brain during surgery. Most existing techniques have drawbacks in terms of cost, measurement time and accuracy. The fibre tracts of healthy brain white matter are composed of densely packed bundles of myelinated axons that form uniaxial linear birefringent medium with the optical axis oriented along the direction of the fibre bundle. Brain tumors, whose cells grow in a largely chaotic way, lack this anisotropy of refractive index. Therefore tumor tissue can be distinguished from of healthy white matter using polarized light. A wide-field visible wavelength imaging Mueller polarimetric system was used for the study of formalin-fixed human brain sections measured in reflection geometry. The non-linear decomposition of the Mueller matrices provided the maps of depolarization, scalar retardance and azimuth of the optical axis. A compelling correlation between the azimuth of the optical axis and the orientation of the brain fibre tracts was proven with the gold standard histology analysis. We present the results of post-processing of Mueller polarimetric images of fixed human brain sections using a combination of classical computer vision and machine learning algorithms, for the automated brain fibre tracking in the white matter tracts. Manually labelled polarimetric data was used to train a convolutional neural network to identify white matter. Within the identified white matter, surface fibre tracts could be visualized. We expect that Mueller polarimetric imaging modality combined with our ML algorithms for fibre tracking will visualize the directions of fibre tracts in imaging plane during tumor surgery, thus, allowing a neurosurgeon to orient himself, to spare essential fibre tracts and to make surgery more complete and safe.
Imaging Mueller polarimetry (IMP) was used in reflection geometry and large field of view configuration for the in-plane visualization of brain fiber tracts by exploring the anisotropy of the refractive index of healthy brain white matter. Our initial studies demonstrated that IMP successfully detects in-plane orientation of fiber tracts on a flat surface of the excised brain specimens. This work, performed ex-vivo on complete fresh calf brains proves the potential of IMP as a technique suitable to detect both presence and orientation of brain fiber tracts in the adverse conditions of complex surface topography and presence of blood.
We use a wide field imaging Mueller polarimeter to visualize the fiber tracts of healthy brain in the retardance maps for the detection of tumor borders. The results of ex-vivo polarimetric studies of thick sections of brain tissue are presented.
The accurate detection of brain tumor border during neurosurgery is crucial for the safe and complete tumor resection, but it is often difficult to differentiate solid tumor tissue from infiltrated white matter. To address this problem we suggest detecting optical anisotropy of brain white matter which consists of bundles of axons (or fiber tracts). Tumor growth erases this optical anisotropy of healthy brain. We used a wide-field imaging Mueller polarimeter to measure thick fixed human and fresh animal brain sections in reflection. The maps of azimuth of fast optical axis of linear birefringent medium obtained from Lu-Chipman decomposition of the experimental Mueller matrices showed a compelling correlation with the fiber tracts directions on histology image of thin whole mount silver-stained brain tissue section.
The crucial problem of brain tumor surgery is the accurate detection the tumor border for safe and complete tumor resection. Whereas it is quite easy to identify brain tumor in preoperative magnetic resonance imaging, it is often difficult to differentiate solid tumor tissue from infiltrated white matter during surgery with conventional surgical intra-operative microscope. To address this problem we suggest exploring the optical anisotropy of healthy brain white matter which represents a highly ordered structure consisting of axons that are joined together in fiber tracts. Tumor cells grow chaotically and erase the optical anisotropy of healthy brain. Instead of detecting the tumor itself, we suggest to visualize healthy white matter by means of its fiber tracts by detecting the optical anisotropy of brain tissue. For this purpose we used a wide-field imaging Mueller polarimetric system operating in the visible wavelength range in backscattering configuration. The Mueller matrix images of the thick (~1cm) fixed human brain specimen and thick (~1cm) fresh veal brain specimen were measured at 633 nm in reflection. Lu Chipman decomposition was applied pixel-wise to the experimental Mueller matrices. The maps of azimuth of fast optical axis of linear birefringent medium showed a compelling correlation with the fiber tracts directions on histology image of thin whole mount silver-stained brain tissue section, that is gold standard for ex-vivo brain fiber tract visualization. Thus, label-free non-contact imaging Mueller polarimetry shows potential for the intra-operative visualization of brain white matter fiber tracts. Further studies are ongoing.
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