Validation of nonrigid image registration using finite-element methods: application to breast MR images

By Julia Anne Schnabel, Christine Tanner, A. D. Castellano-Smith, A. Degenhard, Martin O. Leach, D. Hose, Derek L. G. Hill, D. Hawkes. IEEE Transactions on Medical Imaging, 2003

295 citations recorded by the source corpus.

Presents a novel method for validation of nonrigid medical image registration. This method is based on the simulation of physically plausible, biomechanical tissue deformations using finite-element methods. Applying a range of displacements to finite-element models of different patient anatomies generates model solutions which simulate gold standard deformations. From these solutions, deformed images are generated with a range of deformations typical of those likely to occur in vivo. The registration accuracy with respect to the finite-element simulations is quantified by co-registering the deformed images with the original images and comparing the recovered voxel displacements with the biomechanically simulated ones. The functionality of the validation method is demonstrated for a previously described nonrigid image registration technique based on free-form deformations using B-splines and normalized mutual information as a voxel similarity measure, with an application to contrast-enhanced magnetic resonance mammography image pairs. The exemplar nonrigid registration technique is shown to be of subvoxel accuracy on average for this particular application. The validation method presented here is an important step toward more generic simulations of biomechanically plausible tissue deformations and quantification of tissue motion recovery using nonrigid image registration. It will provide a basis for improving and comparing different nonrigid registration techniques for a diversity of medical applications, such as intrasubject tissue deformation or motion correction in the brain, liver or heart.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/5422950c559342b28177523c21f254a836216b7c

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A Review of Geometric Transformations for Nonrigid Body Registration (2008, methodology): “In [124], a bio-mechanical model was used to generate deformations and these were compared with the ones determined using”
Image Similarity and Tissue Overlaps as Surrogates for Image Registration Accuracy: Widely Used but Unreliable (2011, methodology): “Another strategy is to compare the deformation field obtained by registration to one computed using a bio-mechanical (e.g., finite element [28]) simulation, but this method cannot be applied to inter-subject registration problems, and the accuracy of the bio-mechanical model prediction itself is…”
Medical image registration: a review (2014, methodology): “, 2003), breast (Karaçali, 2007; Rohlfing et al., 2003; Rueckert et al., 1999; Schnabel et al., 2003; Serifovic-Trbalic et al., 2008; Washington and Miga, 2004), abdomen (liver, kidney and spleen) (Brock et al.”
Evaluation of Registration Methods on Thoracic CT: The EMPIRE10 Challenge (2011, background): “[29,33,39]) is to apply a known artificial transformation to a single dataset and then attempt to register the original scan with the result.”
4D MR imaging of respiratory organ motion and its variability (2007, methodology): “The algorithm for non-rigid registration that is used in this paper was proposed by Rueckert et al (1999) and Schnabel et al (2003).”
Elastic moduli of normal and pathological human breast tissues: an inversion-technique-based investigation of 169 samples (2007, methodology): “Samani et al (2001a) proposed an FE-based approach for breast non-rigid registration while Schnabel et al (2003) used an FE breast model to validate a free-form deformation non-rigid registration technique.”