Deformable Registration for Longitudinal Breast MRI Screening
被引:0
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作者:
Hatef Mehrabian
论文数: 0引用数: 0
h-index: 0
机构:Sunnybrook Research Institute,Physical Sciences
Hatef Mehrabian
Lara Richmond
论文数: 0引用数: 0
h-index: 0
机构:Sunnybrook Research Institute,Physical Sciences
Lara Richmond
Yingli Lu
论文数: 0引用数: 0
h-index: 0
机构:Sunnybrook Research Institute,Physical Sciences
Yingli Lu
Anne L. Martel
论文数: 0引用数: 0
h-index: 0
机构:Sunnybrook Research Institute,Physical Sciences
Anne L. Martel
机构:
[1] Sunnybrook Research Institute,Physical Sciences
[2] Sunnybrook Health Sciences Centre,Department of Medical Imaging
[3] University of Toronto,Department of Medical Biophysics
来源:
Journal of Digital Imaging
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2018年
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31卷
关键词:
Breast MRI;
Non-rigid registration;
Finite element analysis;
Elastix;
D O I:
暂无
中图分类号:
学科分类号:
摘要:
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 T1-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.
机构:
Northwestern Polytech Univ, Sch Automat, Xian 710072, Shaanxi, Peoples R China
Univ N Carolina, Dept Radiol, Chapel Hill, NC 27599 USA
Univ N Carolina, BRIC, Chapel Hill, NC 27599 USANorthwestern Polytech Univ, Sch Automat, Xian 710072, Shaanxi, Peoples R China
Cao, Xiaohuan
Yang, Jianhua
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机构:
Northwestern Polytech Univ, Sch Automat, Xian 710072, Shaanxi, Peoples R ChinaNorthwestern Polytech Univ, Sch Automat, Xian 710072, Shaanxi, Peoples R China
Yang, Jianhua
Gao, Yaozong
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机构:
Shanghai United Imaging Intelligence Co Ltd, Shanghai 201807, Peoples R ChinaNorthwestern Polytech Univ, Sch Automat, Xian 710072, Shaanxi, Peoples R China
Gao, Yaozong
Wang, Qian
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h-index: 0
机构:
Shanghai Jiao Tong Univ, Sch Biomed Engn, Inst Med Imaging Technol, Shanghai 200030, Peoples R ChinaNorthwestern Polytech Univ, Sch Automat, Xian 710072, Shaanxi, Peoples R China
Wang, Qian
Shen, Dinggang
论文数: 0引用数: 0
h-index: 0
机构:
Univ N Carolina, Dept Radiol, Chapel Hill, NC 27599 USA
Univ N Carolina, BRIC, Chapel Hill, NC 27599 USA
Korea Univ, Dept Brain & Cognit Engn, Seoul 02841, South KoreaNorthwestern Polytech Univ, Sch Automat, Xian 710072, Shaanxi, Peoples R China