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.
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.
Laser Speckle Contrast Imaging (LSCI) has emerged as a promising imaging modality that offers full field, real time, continuous and agent free monitoring of cerebral blood flow during neurosurgery. Since LSCI does not require the injection of a contrast agent, it has the potential to complement fluorescence-based modalities by providing continuous and dynamic changes in blood flow during critical moments of neurosurgery. We performed a clinical study with LSCI to investigate the clinical utility of the technique intraoperatively. A commercially available Zeiss Pentero 900® microscope was equipped with a λ=785nm laser diode attached to a customized mount. The backscattered laser light was collected by the microscope, producing a laser speckle image on the external camera which was mounted on the microscope side-port. Custom software collected laser speckle images, computed, and then displayed the speckle contrast images in real time throughout the surgery onto the operating room monitors. The images were displayed with custom color maps and thresholding. The robust integration in the surgical workflow of the technology enabled the investigation of the need for dynamic vessel-flow characterization in 20-patients at the Inselspital in Bern, Switzerland. We assessed vessel flow during key time points in the surgery and provided real time and continuous measurements to the surgeon.
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.
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 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.
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.
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