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Imaging depth variations in hyperspectral imaging: Development of a method to detect tumor up to the required tumor-free margin width

机译:高光谱成像的成像深度变化:培养一种检测肿瘤的方法,直至无需自由肿瘤边缘宽度

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摘要

Hyperspectral imaging is a promising technique for resection margin assessment during cancer surgery. Thereby, only a specific amount of the tissue below the resection surface, the clinically defined margin width, should be assessed. Since the imaging depth of hyperspectral imaging varies with wavelength and tissue composition, this can have consequences for the clinical use of hyperspectral imaging as margin assessment technique. In this study, a method was developed that allows for hyperspectral analysis of resection margins in breast cancer. This method uses the spectral slope of the diffuse reflectance spectrum at wavelength regions where the imaging depth in tumor and healthy tissue is equal. Thereby, tumor can be discriminated from healthy breast tissue while imaging up to a similar depth as the required tumor-free margin width of 2 mm. Applying this method to hyperspectral images acquired during surgery would allow for robust margin assessment of resected specimens. In this paper, we focused on breast cancer, but the same approach can be applied to develop a method for other types of cancer.
机译:Hyperspectral成像是癌症手术期间切除保证金评估的有希望的技术。因此,应仅评估切除表面下方的特定量,临床限定的边缘宽度。由于高光谱成像的成像深度随波长和组织组成而变化,因此这可能对高光谱成像作为保证金评估技术的临床应用产生后果。在该研究中,开发了一种方法,其允许在乳腺癌中的切除边缘进行高光谱分析。该方法使用漫反射谱的光谱斜率在波长区域处,其中肿瘤和健康组织中的成像深度相等。由此,可以从健康的乳腺组织中的肿瘤区分,同时成像到类似的深度,作为所需的无肿瘤距宽度为2mm。将该方法应用于手术期间获得的高光谱图像将允许切除的标本的稳健保证金评估。在本文中,我们专注于乳腺癌,但可以应用相同的方法来开发一种用于其他类型癌症的方法。

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