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The proposed polarization-driven DLSA is used to distinguish glioblastoma, the most aggressive subtype of brain neoplasms, from other space-occupying tumoral lesions of the brain, including meningioma, medulloblastoma, subependymomas, and low-grade gliomas. Glioblastoma is characterized by increased microvascular density, necrosis, and high cellular proliferation. In contrast-enhanced T1-weighted magnetic resonance imaging (MRI) data, areas of heterogeneous contrast enhancement with cysts or necrotic niches are present, and in T2/fluid-attenuated inversion recovery (FLAIR) images, extensive vasogenic edema is observed. Meningiomas are tumors originating from the meninges (e.g., dura or arachnoid) surrounding the brain, located extra-axially, with homogeneous contrast enhancement in T1-weighted MRI and may also be surrounded by vasogenic edema. Meningiomas are mostly located at the cerebellopontine angle, making tissue diagnosis difficult. Medulloblastomas are tumors frequently seen in the posterior fossa with prominent contrast enhancement on T1-weighted MRI. Subependymomas are glial tumors originating from ependymal cells of the cerebral ventricles and usually enhance after intravenous (IV) contrast administration in MRI evaluations. Low-grade gliomas are slow-growing tumors originating from glial cells with low malignant potential and a non-contrast enhancing nature in MRI investigations.
We conduct a proof-of-concept experiment to differentiate five different brain neoplasms tested in Hematoxylin and Eosin (H&E)-stained slides: glioblastoma multiforme (GBM), ependymoma, meningioma, medulloblastoma, and low-grade glioma. GBM is a highly aggressive type of brain tumor characterized by rapid growth and a tendency to spread quickly. Ependymomas arise from the ependymal cells lining the ventricles of the brain and the center of the spinal cord. Meningiomas are common brain tumors arising from the meninges, the membranous layers surrounding the brain and spinal cord. Medulloblastomas are highly malignant primary brain tumors originating in the cerebellum, common in children. Low-grade gliomas are slower-growing brain tumors with lower malignant potential. Representative histopathological images of the brain tumors under this study are shown in Fig. 1. These images are standard in the pathology field, providing a clear view of the tissue structures and their pathological characteristics. However, they do not capture the polarization characteristics.
Fig. 1 Representative histopathological images with corresponding diagnostic immunohistochemical staining of the tumor types studied: a GBM, b ependymoma, c meningioma, d medulloblastoma, e low-grade glioma.
The polarization characteristics of different tissue samples are influenced by their unique microstructural and compositional properties. Each type of brain tumor may have distinct optical properties, such as varying degrees of birefringence, scattering coefficients, and absorption rates, which affect how polarized light interacts with the tissue. For instance, GBM exhibits higher heterogeneity in cellular and extracellular structures compared to low-grade gliomas, leading to more complex polarization patterns. Meningiomas, being more fibrous, show distinct birefringent properties, while medulloblastomas, with their denser cellular packing, result in different scattering patterns18, 30. Our method leverages these inherent differences by combining polarimetric imaging, which captures the polarization state changes, with adapted DLSA. This synergy allows us to differentiate tissues based on their unique responses to polarized light. The proposed non-invasive, label-free technique may be used to distinguish the aforementioned intracranial pathologies and can be further extended to preclinical and clinical assessments.
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This study was reviewed and approved by the Ethics Committee of the Iran University of Medical Sciences, Tehran, Iran (IR_IUMS.REC.1397.1237). All procedures were performed in strict adherence to the Declaration of Helsinki – WMA – The World Medical Association 2018.
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Patients were enrolled in a cohort investigation of referred cases to Firoozgar Hospital, Tehran, Iran, in 2022. These patients presented with symptoms and signs of space-occupying tumoral brain lesions, which were newly diagnosed following MRI investigations, surgical intervention, and definitive pathological evaluation of tissues obtained from surgical procedures.
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The surgical procedures included open or endoscopic, total or subtotal resection of the tumor following MRI investigations. Tissue samples were obtained for precise pathological analyses. No corticosteroids or anticonvulsants were prescribed prior to surgery.
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Pathological investigations involved the immediate fixation of samples in formalin post-surgery, followed by paraffin embedding and sectioning. In cases where a definitive diagnosis was not achieved through direct analysis of the H&E-stained slides, IHC results from the pathology department of Firoozgar Hospital, Tehran, Iran, were utilized to reach conclusive final diagnoses, serving as the gold standard technique.
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As previously discussed, while incoherent light sources typically yield noise- and speckle-free images, they often lack the high contrast and quality required for detailed tissue imaging. On the other hand, most tissue samples diffuse coherent light, hindering clear image formation. This challenge prompted us to adopt a novel approach, committed to leveraging polarimetric images while diverting attention to laser speckle patterns, rather than relying solely on the imaging aspect. Our proposed optical setup for polarization-driven DLSA, depicted in Fig. 2, comprises a coherent light source, a spatial filter (SF), a polarization state generator (PSG), a polarization state analyzer (PSA), and an image sensor. Unwanted spatial frequencies of a diode laser (532 nm, 100 mW) are eliminated by the SF unit, with its pinhole positioned at the focal plane of a collimating lens (L, focal length: 5 cm) to ensure uniform intensity across the illumination. The expanded and collimated beam passes through the PSG, consisting of a linear polarizer (LP) and a quarter-wave plate (QWP), to generate an arbitrary polarization state for irradiation. Post-interaction with the sample, diffused light is analyzed through the PSA, which mirrors the PSG’s optical configuration. A digital camera (DCC1545M, Thorlabs) with an 8-bit dynamic range and a 5.2 µm pixel pitch is placed 10 cm from the sample plane. It captures polarimetric images with a resolution of 1280 pixels by 1024 pixels, where each pixel represents an area of 230 nm by 230 nm.
Fig. 2 Schematic of the optical setup for polarization-driven DLSA: SF - spatial filter, MO - microscope objective, LP - linear polarizer, QWP - quarter-wave plate. Displayed below the setup, as an example, are 36 parametric images representing speckle data recorded from one of the samples (low-grade glioma) under investigation. These sequence intensity images are produced by generating six polarization states for the incident light: horizontal linear (H), vertical linear (V), 45$ ^\circ $ linear (P), -45$ ^\circ $ linear (M), left circular (L), and right circular (R). Corresponding polarization states are used for the scattered light, adjusted using the PSG and PSA units as shown in Table 1. The first and second indices indicate the input and output polarization states, respectively. For instance, HV denotes horizontal input and vertical output polarization.
We produce six polarization states for the incident light: horizontal linear (H), vertical linear (V), 45° linear (P), −45° linear (M), left circular (L), and right circular (R). Each time, the same six polarization states are applied to the scattered light in the PSA unit. Hence, 36 parametric images ($ I_{HH}, I_{HV}, I_{HP}, ..., I_{LL} $) of a sample are obtained sequentially by properly adjusting the polarizers and retarders in the PSG and PSA units. The procedure is summarized in Table 1. The first and second indices denote the input and output states of polarization, respectively. For example, the term HV indicates a horizontal state for the input and a vertical state for the output polarizations. The alterations in the polarization-related properties of a specimen cause variations in the speckle pattern observed in the recorded images. The entire imaging process for each sample is completed in 10 minutes. We consider these intensity images as a sequence of speckle pattern data. Collecting such data from a low-grade glioma sample is shown in Fig. 2 as an example. The collected raw images are then subjected to DLSA. The reconstructed 16 Mueller matrix images in the conventional approach may also be used for this purpose towards a robust interpretation57. It is important to emphasize that in this context, “dynamic” refers to changes in the speckle pattern across the sequence of polarimetric images resulting from variations in the sample’s polarimetric responses during polarimetry. This differs from the conventional association with temporal dynamics, as in this context, we focus on sequence-based variations instead of time-varying data. We employ MATLAB (MathWorks, Inc., Natick, MA) to adapt and apply a set of numerical and graphical statistical methods to the polarimetric images, which primarily are customized for DLSA. This allows us to differentiate tissue types based on their unique polarization-influenced speckle traits.
PSA/PSG H V P M R L H $ I_{HH} $ $ I_{HV} $ $ I_{HP} $ $ I_{HM} $ $ I_{HR} $ $ I_{HL} $ V $ I_{VH} $ $ I_{VV} $ $ I_{VP} $ $ I_{VM} $ $ I_{VR} $ $ I_{VL} $ P $ I_{PH} $ $ I_{PV} $ $ I_{PP} $ $ I_{PM} $ $ I_{PR} $ $ I_{PL} $ M $ I_{MH} $ $ I_{MV} $ $ I_{MP} $ $ I_{MM} $ $ I_{MR} $ $ I_{ML} $ R $ I_{RH} $ $ I_{RV} $ $ I_{RP} $ $ I_{RM} $ $ I_{RR} $ $ I_{RL} $ L $ I_{LH} $ $ I_{LV} $ $ I_{LP} $ $ I_{LM} $ $ I_{LR} $ $ I_{LL} $ Table 1. Procedure for collecting 36 polarimetric intensity images as successive speckle data in polarization-driven DLSA. Rows and columns represent polarization states for incident and scattered light, respectively: H: Horizontal, V: Vertical, P: +45° linear, M: −45°, R: Right circular, and L: Left circular polarizations.
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When a laser beam illuminates a rough surface, it forms a random interference pattern called laser speckle. DLSA is an optical technique that captures and analyzes temporal changes in the speckle pattern. Changes within the sample, such as cellular activity or tissue growth, alter the speckle pattern. These changes are recorded as a series of frames and tracked over time. A set of numerical and graphical statistical methods is then applied to these frames for analysis48, 53. DLSA serves as an effective tool for investigating the characteristics of active materials, providing valuable information about the sample’s activity, especially in the case of biomaterials. The analysis of sample activity can reveal various phenomena related to the sample35−37, 58−60.
We synergize polarimetric imaging with dynamic laser speckle analysis to enhance the differentiation of tissue samples. Unlike traditional temporal dynamics, this approach focuses on sequence-based variations, analyzing changes in speckle patterns throughout a collection of polarimetric images. These intensity images capture spatial fluctuations associated with the sample's polarimetric responses during the polarimetric experiment. Coherent light with different polarization states interacts with the tissue samples through absorption, reflection, and scattering, influencing the formed speckle pattern. The intensity and wavelength of the laser remain stable throughout the process, ensuring that any detected changes in the speckle patterns is attributed to internal features28, 29, 61, 62. We consider the images as a series of speckle frames and apply various post-processing techniques to the consecutive images, primarily customized for DLSA.
The Time History Speckle Pattern (THSP) creates a 2D matrix from the collection of speckle images. A set of $ M $ points or pixels is randomly selected in each pattern of the consecutive images. These points are then arranged side by side to form a new $ M \times N $ pixel matrix. In this study, the rows ($ M $) represent the pixels, while the columns ($ N $) show their intensity changes across the polarimetric images, with $ N $ denoting the number of images. The THSP serves as an indicator for any changes in the polarimetric responses within a sample during polarimetry, which manifest as intensity variations in the horizontal direction. Higher intensity variations in the THSP line correspond to samples with higher activity levels38, 63−65. The THSP concept is commonly employed to derive numerical results such as the gray-level co-occurrence matrix (GLCM), inertia moment (IM), and auto-correlation (AC). Additionally, certain statistical parameters, such as contrast, homogeneity, and roughness, can be determined independently of THSP.
The GLCM is an intermediary matrix used to evaluate the dispersion of consecutive pixels in a THSP of $ M$ points through $ N$ speckle patterns. It is defined as:
$$ {\rm{GLCM}}(i,j) = \sum\limits_{m = 1}^M {\sum\limits_{n = 1}^{N - 1} {\left\{ {\begin{array}{*{20}{l}} {1,}&{{\rm{if\ THSP}}(m,n) = i\ {\rm{ and}}}\\& {\rm{THSP}}(m,n + 1) = j\\ {0,}&{{\rm{otherwise}}} \end{array}} \right.} } $$ (1) This matrix represents a transition histogram of intensities. Thus, $ \mathrm{GLCM}(i, j)$ indicates the number of times a transition from intensity level $ i$ to $ j$ occurs50,53,66−70. GLCM values may be normalized to create what is often referred to as the modified GLCM. This normalization enables the depiction of transition probability matrices between intensity values within the (THSP)34,71,72. The modified GLCM is used to find the probability mass function of the regular difference of intensities between two neighboring pixels in a THSP. While high raw GLCM values indicate frequent transitions between specific intensity levels, it is the normalized GLCM values that provide a meaningful depiction of these probabilities, enriching the understanding of the sample's characteristics. In this context, $ i $ and $ j $ represent the intensities of neighboring pixels. This normalization helps calculate the probability of an intensity jump from level $ i $ to $ j $, such that $ P_r((j - i) = w) $, where $ w $ represents the intensity jump value from $ j = i + w $, with $ -255 \leqslant w \leqslant 255 $53 (for an 8-bit image).
The AC function calculates the average correlation between the intensity of pixels in two consecutive speckle patterns across the sequence of polarimetric images. For this, the THSP matrix is used to calculate the AC curve65,73:
$$ \mathrm{AC}(i,j) = \langle \mathrm{THSP}(:,i),\mathrm{THSP}(:,i+j)\rangle $$ (2) There is a relationship between the speckle intensity AC and the mean square displacement ($ \langle{\Delta r^2(i,j)}\rangle $), which is crucial for determining the dissemination of statistical data in biological samples. The AC function of THSP is directly related to the ($ \langle{\Delta r^2(i,j)}\rangle $) of the scatterers between the $ i $ and the $ i + j $ polarimetric images. THSP(:,$ i $) and THSP(:,$ i+j $) refer to the pixels of THSP in the $ i $ and the $ i + j $ images, respectively73−76:
$$ \mathrm{AC}(i,j) = e^{-2k\gamma\sqrt{\langle{\Delta r^2(i,j)}\rangle}} $$ (3) The wave vector $ k $ and parameter $ \gamma $, which depends on the polarization state of light, are used to calculate the mean square displacements (MSD) of contributing scatterers of samples that produce speckle patterns. To obtain MSD, curves are fitted to experimental data.
The randomness of the speckle pattern can be determined by examining its entropy, which has been studied and generalized through the Shannon entropy (SE) concept. This method has proven useful in the image correlation method for quality valuation of the speckle pattern. SE is calculated by estimating the probability density function of the intensity of the speckle pattern, which changes with the intensity distribution randomness. The intensity probability density function, $ p(I) $, of a random intensity distribution can be written as77:
$$ p(I) = \frac{4I}{\langle I \rangle}\; \exp{\frac{-2I}{\langle I \rangle}} $$ (4) $ \langle \rangle $ indicates the ensemble average of the variable. The intensity SE distribution can be derived from the function of the intensity normalized probability density, described as $ Np(I) = \langle I \rangle p(I) $, and using77:
$$ \mathrm{SE} = -\sum\limits_{I} Np(I) \; \log Np(I) $$ (5) where SE represents the intensity distribution with the summation taken over the intensity. It is important to note that $ I $ unequivocally represents the intensities of the images in the datapack as a 3D matrix.
The Average Difference (AD), also known as the Fujii method, may be used to show graphical analysis outcomes48,53. This method produces a result with a relative value, as shown in Eq. 6:
$$ \mathrm{AD} = \sum\limits_{k} \frac{|I_{k} - I_{k-1}|}{I_{k} + I_{k-1}} $$ (6) where $ I_k $ represents an image (intensity matrix) taken at instant $ k $. This method enhances the differences in areas of the image that are dark, making the final image clearer than other graphical methods.
The motion history image (MHI) is a valuable tool for analyzing the activity level of samples by identifying movements over time within a series of images. MHI distinguishes between static and dynamic patterns in pixel intensity, providing precise information about recent motion. By examining the timestamps of pixels across an image sequence, MHI accurately tracks and analyzes object movements, offering a comprehensive understanding of motion sequences. To achieve this, intensity matrices are grouped into a 3D matrix known as a datapack, and the dynamics of the object are revealed by subtracting sequential speckle patterns. This method effectively determines an object’s movement48,53,78. To generate the MHI, we first calculate the difference between consecutive frames to detect motion. This is done by deriving the $ S_l(i,j)$ matrix for each of the $ N$ speckle patterns (images) by subtracting it from the previous pattern:
$$ S_{ l}(i,j) = I_l(i,j) - I_{l-1}(i,j) $$ (7) where $ I_l(i,j)$ and $ I_{l-1}(i,j)$ represent the intensity of pixel $ (i,j)$ at moments $ l$ and $ l-1$, respectively. Subsequently, these matrices are converted into binary patterns by applying a suitable threshold to distinguish significant motion from noise:
$$ {T_l}(i,j) = \left\{ {\begin{array}{*{20}{l}} {1,}&{{\rm{if }}\ |{S_l}(i,j)| > {S_T}}\\ {0,}&{{\rm{if }}\ |{S_l}(i,j)| \leqslant {S_T}} \end{array}} \right. $$ (8) where $ T_l(i,j)$ is the threshold image of $ S_l$ at each moment $ l$, and $ S_{T}$ is the activity threshold parameter ($ 0 \leqslant S_{T} \leqslant 255$). This threshold helps to filter out small changes and noise, only highlighting significant changes as motion. Therefore, MHI specifically considers pixel activity with intensity levels above $ S_{T}$. Finally, the MHI matrix is generated from $ T_l(i,j)$ by:
$$ \mathrm{MHI} = 255 \sum\limits_{k = 0}^{N-1} T_{l-k}\; h_k $$ (9) where $ h_k$ is given by:
$$ h_k = \frac{N-k}{N(N+1)/2} $$ (10) It is important to note that the $ h_k$ value is a crucial weighting parameter that is solely based on the age of the image. Therefore, it is essential to consider this value while determining the relevance and importance of the image in any given context. Polarization-driven DLSA shifts the focus from temporal changes to sequence-based variations and applies the adapted methods to the collection of polarimetric images.
Polarization-driven dynamic laser speckle analysis for brain neoplasms differentiation
- Light: Advanced Manufacturing , Article number: (2024)
- Received: 01 March 2023
- Revised: 05 August 2024
- Accepted: 06 August 2024 Published online: 12 November 2024
doi: https://doi.org/10.37188/lam.2024.043
Abstract: Early diagnosis of brain tumors is often hindered by non-specific symptoms, particularly in eloquent brain regions where open surgery for tissue sampling is unfeasible. This limitation increases the risk of misdiagnosis due to tumor heterogeneity in stereotactic biopsies. Label-free diagnostic methods, including intraoperative probes and cellular origin analysis techniques, hold promise for improving diagnostic accuracy. Polarimetry offers valuable information on the polarization properties of biomedical samples, yet it may not fully reveal specific structural characteristics. The interpretative scope of polarimetric data is sometimes constrained by the limitations of existing decomposition methods. On the other hand, dynamic laser speckle analysis (DLSA), a burgeoning technique, not only does not account for the polarimetric attributes but also is known for tracking only the temporal activity of the dynamic samples. This study bridges these gaps by synergizing conventional polarimetric imaging with DLSA for an in-depth examination of sample polarization properties. The effectiveness of our system is shown by analyzing the collection of polarimetric images of various tissue samples, utilizing a variety of adapted numerical and graphical statistical post-processing methods.
Research Summary
A simple yet efficient approach to tissue analysis synergizing optical techniques
This study synergizes polarimetry with dynamic laser speckle analysis, presenting a powerful non-invasive approach to tissue analysis. The concept is proved by differentiating different types of brain tumors. Variations in polarimetric responses within a tissue sample during polarimetric measurement cause changes in the speckle patterns observed in the recorded sequence of polarimetric images. This collection of intensity patterns is taken as a series of speckle frames, which are then analyzed using a variety of numerical and graphical statistical post-processing methods. The encouraging outcomes of our research mark a significant step forward in the field of medical diagnostics, opening new possibilities for label-free cancer tissue analysis.
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