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纵向乳腺 MRI 筛查的可变形配准。

Deformable Registration for Longitudinal Breast MRI Screening.

机构信息

Physical Sciences, Sunnybrook Research Institute, 2075 Bayview Ave, Toronto, ON, M4N 3M5, Canada.

Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.

出版信息

J Digit Imaging. 2018 Oct;31(5):718-726. doi: 10.1007/s10278-018-0063-1.

Abstract

MRI screening of high-risk patients for breast cancer provides very high sensitivity, but with a high recall rate and negative biopsies. Comparing the current exam to prior exams reduces the number of follow-up procedures requested by radiologists. Such comparison, however, can be challenging due to the highly deformable nature of breast tissues. Automated co-registration of multiple scans has the potential to aid diagnosis by providing 3D images for side-by-side comparison and also for use in CAD systems. Although many deformable registration techniques exist, they generally have a large number of parameters that need to be optimized and validated for each new application. Here, we propose a framework for such optimization and also identify the optimal input parameter set for registration of 3D T-weighted MRI of breast using Elastix, a widely used and freely available registration tool. A numerical simulation study was first conducted to model the breast tissue and its deformation through finite element (FE) modeling. This model generated the ground truth for evaluating the registration accuracy by providing the deformation of each voxel in the breast volume. An exhaustive search was performed over various values of 7 registration parameters (4050 different combinations of parameters were assessed) and the optimum parameter set was determined. This study showed that there was a large variation in the registration accuracy of different parameter sets ranging from 0.29 mm to 2.50 mm in median registration error and 3.71 mm to 8.90 mm in 95 percentile of the registration error. Mean registration errors of 0.32 mm, 0.29 mm, and 0.30 mm and 95 percentile errors of 3.71 mm, 5.02 mm, and 4.70 mm were obtained by the three best parameter sets. The optimal parameter set was applied to consecutive breast MRI scans of 13 patients. A radiologist identified 113 landmark pairs (~ 11 per patient) which were used to assess registration accuracy. The results demonstrated that using the optimal registration parameter set, a registration accuracy (in mm) of 3.4 [1.8 6.8] was achieved.

摘要

MRI 筛查高危乳腺癌患者具有很高的敏感性,但召回率和阴性活检率也很高。与之前的检查相比,这种比较可以减少放射科医生要求的后续检查程序。然而,由于乳房组织的高度可变形性,这种比较具有挑战性。自动配准多个扫描具有通过提供并排比较的 3D 图像以及在 CAD 系统中的使用来辅助诊断的潜力。尽管存在许多可变形配准技术,但它们通常具有大量需要针对每个新应用程序进行优化和验证的参数。在这里,我们提出了一种用于这种优化的框架,并确定了使用广泛使用且免费提供的配准工具 Elastix 对 3D T 加权乳房 MRI 进行配准的最佳输入参数集。首先进行了数值模拟研究,通过有限元 (FE) 建模对乳房组织及其变形进行建模。该模型通过提供乳房体积中每个体素的变形为评估配准精度提供了地面实况。对 7 个配准参数的各种值进行了详尽的搜索(评估了 4050 种不同的参数组合),并确定了最佳参数集。这项研究表明,不同参数集的配准精度差异很大,中位数配准误差从 0.29 毫米到 2.50 毫米不等,95%的配准误差从 3.71 毫米到 8.90 毫米不等。三个最佳参数集的平均配准误差分别为 0.32 毫米、0.29 毫米和 0.30 毫米,95%的配准误差分别为 3.71 毫米、5.02 毫米和 4.70 毫米。最优参数集应用于 13 例连续的乳房 MRI 扫描。放射科医生确定了 113 个标记点对(每个患者约 11 个),用于评估配准精度。结果表明,使用最佳配准参数集,可实现 3.4 [1.8 6.8] 的配准精度。

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