利用多模态成像技术向不同受众传播医学图像

IF 3.56 Q1 Medicine
Laura M. Cole, Arul N. Selvan, Rebecca Partridge, Heath Reed, Chris Wright, Malcolm R. Clench
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引用次数: 2

摘要

已经完成了一项研究,审查了有关向不同受众解释和传播多模式医学成像数据集的设计问题。为了创建模型数据集,通过磁共振成像(MRI)、基质辅助激光解吸/电离-质谱(MALDI-MSI)和组织学对小鼠纤维肉瘤组织进行可视化。采用0.25T Esaote GScan获取MRI图像;MALDI图像是使用Q-Star脉冲星I质谱计获得的。然后对用于MALDI-MSI的相同组织切片进行组织学染色。由于出血导致的含铁血黄素沉积区域可以通过MRI可见。在获得的MALDI-MSI数据中,鞘磷脂种类的分布可用于识别活肿瘤区域。使用基于分层聚类的分割的数学“上采样”为MRI和MALDI-MS提供了一个复杂的图像增强工具,并有助于图像的相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Communication of medical images to diverse audiences using multimodal imaging

Communication of medical images to diverse audiences using multimodal imaging

A study has been completed examining design issues concerning the interpretation of and dissemination of multimodal medical imaging data sets to diverse audiences. To create a model data set mouse fibrosarcoma tissue was visualised via magnetic resonance imaging (MRI), Matrix-Assisted Laser Desorption/Ionisation-Mass Spectrometry (MALDI-MSI) and histology. MRI images were acquired using the 0.25T Esaote GScan; MALDI images were acquired using a Q-Star Pulsar I mass spectrometer. Histological staining of the same tissue sections used for MALDI-MSI was then carried out. Areas assigned to hemosiderin deposits due to haemorrhaging could be visualised via MRI. In the MALDI-MSI data obtained the distribution sphingomyelin species could be used to identify regions of viable tumour. Mathematical ‘up sampling’ using hierarchical clustering-based segmentation provided a sophisticated image enhancement tool for both MRI and MALDI-MS and assisted in the correlation of images.

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来源期刊
Advanced Structural and Chemical Imaging
Advanced Structural and Chemical Imaging Medicine-Radiology, Nuclear Medicine and Imaging
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