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X-ray Mammography - MRI Registration Using a Volume-Preserving Affine Transformation and an EM-MRF for Breast Tissue Classification

机译:X射线乳腺摄影-使用体积保留的仿射变换和EM-MRF进行MRI配准的乳腺癌组织分类

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摘要

Registration of MR volumes to X-ray mammograms is a clinically valuable task, as each modality provides complementary information on normal and abnormal breast tissue structure and function. We propose an intensity-based technique with a 3D volume-preserving affine transformation. An important part of our framework is the use of an Expectation-Maximization (EM) algorithm, with a Markov Random Field (MRF) regularization, that is used for breast tissue classification and subsequently the mapping of the MR intensities to X-ray attenuation. Initially, the proposed framework was tested on simulated X-ray data, where the goal was to register the original undeformed MRI to a simulated X-ray that was produced using a real compression image, acquired from volunteers in the MR scanner (8 cases). Since the ground truth in this case can be estimated from individually defined landmarks, we have evaluated the mean reprojection error, which was 3.83mm. The algorithm was then applied and evaluated visually on 5 cases that had both X-ray mammograms and MRIs.
机译:将MR体积配准到X射线乳房X线照片是一项临床上有价值的任务,因为每种方式都可以提供有关正常和异常乳房组织结构和功能的补充信息。我们提出了一种基于强度的技术,并保留了3D体积的仿射变换。我们框架的重要组成部分是使用期望最大化(EM)算法和马尔可夫随机场(MRF)正则化算法,该算法用于乳腺组织分类以及随后将MR强度映射到X射线衰减。最初,对拟议的框架进行了模拟X射线数据测试,目的是将原始未变形的MRI记录到使用真实压缩图像产生的模拟X射线,该图像是从MR扫描仪的志愿者那里获得的(8例) 。由于这种情况下的地面真实性可以通过单独定义的地标进行估算,因此我们评估了平均重投影误差,该误差为3.83mm。然后对5例同时具有X线乳房X线照片和MRI的病例应用该算法并进行视觉评估。

著录项

  • 来源
    《Digital mammography》|2010年|p.23-30|共8页
  • 会议地点 Girona(ES);Girona(ES)
  • 作者单位

    Centre for Medical Image Computing, University College London, Gower Street, WC1E 6BT, London, UK;

    Centre for Medical Image Computing, University College London, Gower Street, WC1E 6BT, London, UK;

    Centre for Medical Image Computing, University College London, Gower Street, WC1E 6BT, London, UK;

    Centre for Medical Image Computing, University College London, Gower Street, WC1E 6BT, London, UK ,Computer Vision Laboratory, ETH Zurich, Sternwartstrasse 7, 8092 Zurich, Switzerland;

    Centre for Medical Image Computing, University College London, Gower Street, WC1E 6BT, London, UK;

    Centre for Medical Image Computing, University College London, Gower Street, WC1E 6BT, London, UK;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物医学工程;
  • 关键词

    multimodal registration; 2D - 3D registration; breast tissue classification;

    机译:多式联运注册; 2D-3D注册;乳房组织分类;

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