4.4 Article

A nonrigid registration algorithm for longitudinal breast MR images and the analysis of breast tumor response

期刊

MAGNETIC RESONANCE IMAGING
卷 27, 期 9, 页码 1258-1270

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.mri.2009.05.007

关键词

Breast Cancer; Image registration; DCE-MRI; Neoadjuvant chemotherapy; Treatment monitoring

资金

  1. National Institutes of Health for funding through NCI [1R01CA129961]
  2. NIBIB [EB005936]
  3. NCI [1P50 098131]
  4. Vanderbilt-Ingrain Cancer Center Institutional Grant [NTH P30 CA68485]

向作者/读者索取更多资源

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can estimate parameters relating to blood flow and tissue volume fractions and therefore may be used to characterize the response of breast tumors to treatment. To assess treatment response, values of these DCE-MRI parameters are observed at different time points during the course of treatment. We propose a method whereby DCE-MRI data sets obtained in separate imaging sessions can be co-registered to a common image space, thereby retaining spatial information so that serial DCE-MRI parameter maps can be compared on a voxel-by-voxel basis. In performing inter-session breast registration, one must account for patient repositioning and breast deformation, as well as changes in tumor shape and volume relative to other imaging sessions. One challenge is to optimally register the normal tissues while simultaneously preventing tumor distortion. We accomplish this by extending the adaptive bases algorithm through adding a tumor-volume preserving constraint in the cost function. We also propose a novel method to generate the simulated breast magnetic resonance (MR) images, which can be used to evaluate the proposed registration algorithm quantitatively. The proposed nonrigid registration algorithm is applied to both simulated and real longitudinal 3D high resolution MR images and the obtained transformations are then applied to lower resolution physiological parameter maps obtained via DCE-MRI. The registration results demonstrate the proposed algorithm can successfully register breast MR images acquired at different time points and allow for analysis of the registered parameter maps. (C) 2009 Elsevier Inc. All rights reserved.

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