PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
January 1, 1999IEEE Transactions on Medical Imaging5,314 citations

Nonrigid registration using free-form deformations: application to breast MR images

View Full Paper
DRDaniel RueckertLSLuke SonodaCHCarmel Hayes

Key Points

  • The aim is to develop a novel nonrigid registration approach for enhancing breast MRI accuracy.
  • Developed a hierarchical transformation model for breast motion including affine and FFD components.
  • Used normalized mutual information for voxel-based similarity, minimizing a cost function for transformation and image similarity.
  • Compared automated nonrigid registration results with rigid and affine techniques on breast MRI data.
  • The nonrigid registration algorithm showed superior motion recovery compared to rigid registration, with significant improvements in image alignment.
  • The method demonstrated higher accuracy in tracking breast deformation than existing registration techniques.

Abstract

In this paper we present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, we have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Rueckert et al. (1999) studied this question.

synapsesocial.com/papers/69c4315db78463c71097f58dhttps://doi.org/10.1109/42.796284
Ask AI
Helpful
Bookmark
Share
View Full Paper