Using statistical deformation models for the registration of multimodal breast images
Using statistical deformation models for the registration of multimodal breast images
复制标题
使用统计变形模型来配准多模态乳房图像
DOI：
10.1117/12.811631
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Tanner C
中科院分区：
文献类型：
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作者：
Tanner C
本文介绍了一种新的方法，用于配准多模态乳腺图像。该方法是基于引导初始对准的3D统计变形模型（SDM），然后由一个标准的非刚性配准方法进行精细对准。该方法被应用于补偿大的乳房压缩的问题，即配准磁共振（MR）图像到断层合成图像和X射线乳房X线照片。SDM基于通过从分割的3D MR乳房图像创建的有限元模型模拟20名受试者的合理乳房按压。对模拟数据的留一法测试表明，使用SDM引导配准而不是仿射配准进行初始对准平均可降低平均配准误差，即MR到断层合成图像的3.2 mm与4.2 mm（初始17.1 mm）以及MR到X射线乳房X线照片的5.0 mm与6.2 mm（初始15.0 mm）。
This paper describes a novel method for registering multimodal breast images. The method is based on guiding initial alignment by a 3D statistical deformation model (SDM) followed by a standard non-rigid registration method for fine alignment. The method was applied to the problem of compensating for large breast compressions, namely registering magnetic resonance (MR) images to tomosynthesis images and X-ray mammograms. The SDM was based on simulating plausible breast compressions for a population of 20 subjects via finite element models created from segmented 3D MR breast images. Leave-one-out tests on simulated data showed that using SDM guided registration rather than affine registration for the initial alignment led on average to lower mean registration errors, namely 3.2 mm versus 4.2 mm for MR to tomosynthesis images (17.1 mm initially) and 5.0 mm versus 6.2 mm for MR to X-ray mammograms (15.0 mm initially).