Characterization of Polarimetric Properties in Various Brain Tumor Types Using Wide-Field Imaging Mueller Polarimetry.

Romane Gros, Omar Rodriguez-Nunez, Leonard Felger, Stefano Moriconi, Richard McKinley, Angelo Pierangelo, Tatiana Novikova, Erik Vassella, Philippe Schucht, Ekkehard Hewer, Theoni Maragkou
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Abstract

Neuro-oncological surgery is the primary brain cancer treatment, yet it faces challenges with gliomas due to their invasiveness and the need to preserve neurological function. Hence, radical resection is often unfeasible, highlighting the importance of precise tumor margin delineation to prevent neurological deficits and improve prognosis. Imaging Mueller polarimetry, an effective modality in various organ tissues, seems a promising approach for tumor delineation in neurosurgery. To further assess its use, we characterized the polarimetric properties by analysing 45 polarimetric measurements of 27 fresh brain tumor samples, including different tumor types with a strong focus on gliomas. Our study integrates a wide-field imaging Mueller polarimetric system and a novel neuropathology protocol, correlating polarimetric and histological data for accurate tissue identification. An image processing pipeline facilitated the alignment and overlay of polarimetric images and histological masks. Variations in depolarization values were observed for grey and white matter of brain tumor tissue, while differences in linear retardance were seen only within white matter of brain tumor tissue. Notably, we identified pronounced optical axis azimuth randomization within tumor regions. This study lays the foundation for machine learning-based brain tumor segmentation algorithms using polarimetric data, facilitating intraoperative diagnosis and decision making.

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利用宽视场成像穆勒偏振测量法鉴定各种脑肿瘤的偏振特性
神经肿瘤外科手术是治疗脑癌的主要方法,但由于胶质瘤具有侵袭性,且需要保护神经功能,因此面临着挑战。因此,根治性切除往往是不可行的,这凸显了精确划分肿瘤边缘对防止神经功能缺损和改善预后的重要性。成像穆勒偏振测量法是一种在各种器官组织中都很有效的方法,它似乎是神经外科中一种很有前景的肿瘤划定方法。为了进一步评估其应用,我们分析了 27 个新鲜脑肿瘤样本的 45 次偏振测量结果,其中包括不同的肿瘤类型,重点是胶质瘤。我们的研究整合了宽视场成像穆勒极谱分析系统和新颖的神经病理学方案,将极谱分析和组织学数据关联起来,以准确识别组织。图像处理管道有助于偏振图像和组织学掩膜的对齐和叠加。在脑肿瘤组织的灰质和白质中观察到了去极化值的变化,而线性延迟的差异仅出现在脑肿瘤组织的白质中。值得注意的是,我们在肿瘤区域内发现了明显的光轴方位随机性。这项研究为使用偏振数据进行基于机器学习的脑肿瘤分割算法奠定了基础,有助于术中诊断和决策制定。
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