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首页> 外文会议>Conference on Medical Imaging 2008: Imaging Processing; 20080217-19; San Diego,CA(US) >Fusion of Rat Brain Histology and MRI using Weighted Multi-Image Mutual Information
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Fusion of Rat Brain Histology and MRI using Weighted Multi-Image Mutual Information

机译：加权多图像互信息融合大鼠脑组织学与MRI

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Introduction - Fusion of histology and MRI is frequently demanded in biomedical research to study in vitro tissue properties in an in vivo reference space. Distortions and artifacts caused by cutting and staining of histological slices as well as differences in spatial resolution make even the rigid fusion a difficult task. State-of-the-art methods start with a mono-modal restacking yielding a histological pseudo-3D volume. The 3D information of the MRI reference is considered subsequently. However, consistency of the histology volume and consistency due to the corresponding MRI seem to be diametral goals. Therefore, we propose a novel fusion framework optimizing histology/histology and histology/MRI consistency at the same time finding a balance between both goals. Method - Direct slice-to-slice correspondence even in irregularly-spaced cutting sequences is achieved by registration-based interpolation of the MRI. Introducing a weighted multi-image mutual information metric (WI), adjacent histology and corresponding MRI are taken into account at the same time. Therefore, the reconstruction of the histological volume as well as the fusion with the MRI is done in a single step. Results - Based on two data sets with more than 110 single registrations in all, the results are evaluated quantitatively based on Tanimoto overlap measures and qualitatively showing the fused volumes. In comparison to other multi-image metrics, the reconstruction based on WI is significantly improved. We evaluated different parameter settings with emphasis on the weighting term steering the balance between intra- and inter-modality consistency.
机译：简介-生物医学研究中经常需要组织学和MRI融合以研究体内参考空间中的体外组织特性。由于组织切片的切割和染色以及空间分辨率的差异而导致的变形和伪影，甚至刚性融合也是一项艰巨的任务。最先进的方法从单模式重堆开始，产生组织学上的伪3D体积。随后考虑MRI参考的3D信息。然而，组织学体积的一致性和由于相应的MRI而引起的一致性似乎是直径目标。因此，我们提出了一种新颖的融合框架，可同时优化组织学/组织学和组织学/ MRI一致性，从而在两个目标之间找到平衡。方法-通过基于MRI的配准插值，即使在不规则间隔的切割序列中也可以实现直接的切片到切片对应。引入加权的多图像互信息度量（WI），同时要考虑相邻的组织学和相应的MRI。因此，只需一步即可完成组织学体积的重建以及与MRI的融合。结果-基于总共包含110个以上单次注册的两个数据集，基于Tanimoto重叠度量对结果进行定量评估，并定性显示融合量。与其他多图像指标相比，基于WI的重构得到了显着改善。我们评估了不同的参数设置，重点是权重因素指导了模态内和模态间一致性之间的平衡。

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