首页> 外文会议>Visualization, Image-Guided Procedures, and Display pt.1; Progress in Biomedical Optics and Imaging; vol.6,no.21 >Image Guided Multi-Modality Registration and Visualization for Breast Cancer Detection
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Image Guided Multi-Modality Registration and Visualization for Breast Cancer Detection

机译:图像引导的乳腺癌多模态配准和可视化

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It is crucial that breast cancer be detected in its earlier and more curable stages of development. New imaging modalities are emerging, such as electrical impedance spectroscopy (EIS), microwave imaging and spectroscopy (MIS), magnetic resonance elastography (MRE), and near-infrared (NIR) imaging. These alternative imaging modalities strive to alleviate limitations of traditional screening and diagnostic tools on dense breast tissue and detection of small abnormalities. The purpose of this study is to combine the results from alternative imaging modalities with T1 and T2-weighted MR Imaging. Two categories of data are presented, pixel data (MRIs) and geometry model with scalar values (MRE and MIS). Three dimensional mesh models (surface/volume meshes) are generated using the automatic mesh generator for biological models developed in the laboratory. A graphic user interface (GUI) for medical image processing powered by Visualization Toolkit (VTK) was developed which supports interactive and automatic image registration, image volume manipulation and geometry rendering. Registration of image/image and image/geometry is a fundamental requirement for multi-spectral data visualization within the same workspace. Various physical properties can be visualized to reveal the correlations between alternative imaging modalities and subsequently for breast tissue classification. A registration strategy was implemented using T1 and T2-weighted MR data as the standard subject. It combined automated image registration (AIR) with interactive registration routines. The final synthetic datasets are rendered in 3D views. This framework was created for multi-modality breast imaging data registration and visualization. The aligned image/geometry data facilitate breast tissue classification.
机译:至关重要的是,必须在乳腺癌的早期和可治愈的发展阶段对其进行检测。新的成像方式正在出现,例如电阻抗光谱(EIS),微波成像和光谱(MIS),磁共振弹性成像(MRE)和近红外(NIR)成像。这些替代的成像方式致力于减轻传统的筛查和诊断工具在乳腺密集组织和微小异常检测方面的局限性。这项研究的目的是将来自替代成像模式的结果与T1和T2加权MR成像相结合。呈现两类数据,像素数据(MRI)和标量值的几何模型(MRE和MIS)。使用自动网格生成器为实验室中开发的生物模型生成三维网格模型(表面/体积网格)。开发了由Visualization Toolkit(VTK)支持的用于医学图像处理的图形用户界面(GUI),该界面支持交互式和自动图像配准,图像体积操纵和几何图形渲染。图像/图像和图像/几何图形的配准是同一工作空间内多光谱数据可视化的基本要求。各种物理特性可以可视化,以揭示替代成像方式之间的相关性,以及随后用于乳腺组织分类的相关性。使用T1和T2加权MR数据作为标准主题实施注册策略。它结合了自动图像注册(AIR)和交互式注册例程。最终的合成数据集以3D视图呈现。该框架是为多模式乳房成像数据注册和可视化创建的。对齐的图像/几何数据有助于乳房组织分类。

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