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Ultra-low spatial resolution Raman mapping using a novel fibre optic probe

机译:使用新型光纤探头的超低空间分辨率拉曼映射

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Ultra-low spatial resolution Raman (ULSRR) mapping using fibre probes has been performed on mammalian and human tissues. This will provide an understanding of the potential for in vivo surveillance of the lining of organs using such a technique and for identifying abnormal tissues such as residual tumours within a surgical field.rnThe aim of the study was to create Raman probe map images of excised oesophageal specimens following radical and palliative oesophagectomy procedures. A reproducible mapping grid was placed over the excised tissue surface and Raman mapping at 830nm performed at regular intervals to provide images of 200 pixels over the region of interest.rnPrincipal component analysis was used to create pseudocolour score images of both porcine phantoms and a human resected oesophagus.rnA principal component fed linear discriminant (LD) classification model of 72 biopsy samples from 35 patients was created using a novel single fibre Raman probe. A subset of the training dataset was used to populate a matrix of 200 pixels to simulate a Raman probe map. Spectra from the simulated map were then projected onto the LD model and a pseudocolour LD pathology map created.rnDelineation of clinically significant pathology groups was demonstrated therefore this study has shown the feasibility of in vivo ULSRR for margin assessment using a Raman probe.
机译:已经在哺乳动物和人类组织上进行了使用纤维探针的超低空间分辨率拉曼(ULSRR)绘图。这将提供使用这种技术对器官内膜进行体内监视以及识别异常组织(例如外科手术区域内残留肿瘤)的潜力的研究。这项研究的目的是创建切除的食管的拉曼探针图图像。标本行根治性和姑息性食管切除术。将可复制的映射网格放置在切除的组织表面上,并定期进行830nm的拉曼映射,以在感兴趣区域上提供200像素的图像。主成分分析用于创建猪体模和人类切除的假色评分图像oesophagus.rn使用新型单纤维拉曼探针创建了一个主要成分进料线性判别(LD)分类模型,对来自35位患者的72个活检样品进行了分类。训练数据集的一个子集用于填充200个像素的矩阵,以模拟拉曼探针图。然后将来自模拟图谱的光谱投影到LD模型上,并创建伪彩色LD病理图。rn证明了对临床上重要病理学组的划分,因此,该研究表明了使用拉曼探针进行体内ULSRR进行边缘评估的可行性。

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