Registration of 3D Intraoperative MR Images of the Brain Using a Finite Element Biomechanical Model
By M. Ferrant, A. Nabavi, Benoit M. Macq, F. Jolesz, Ron Kikinis, Simon Keith Warfield. IEEE Trans. Medical Imaging, 2001
299 citations recorded by the source corpus.
We present a new algorithm for the nonrigid registration of three-dimensional magnetic resonance (MR) intraoperative image sequences showing brain shift. The algorithm tracks key surfaces of objects (cortical surface and the lateral ventricles) in the image sequence using a deformable surface matching algorithm. The volumetric deformation field of the objects is then inferred from the displacements at the boundary surfaces using a linear elastic biomechanical finite-element model. Two experiments on synthetic image sequences are presented, as well as an initial experiment on intraoperative MR images showing brain shift. The results of the registration algorithm show a good correlation of the internal brain structures after deformation, and a good capability of measuring surface as well as subsurface shift. We measured distances between landmarks in the deformed initial image and the corresponding landmarks in the target scan. Cortical surface shifts of up to 10 mm and subsurface shifts of up to 6 mm were recovered with an accuracy of 1 mm or less and 3 mm or less respectively.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/ee12997f71a5d5d08272cf61480988d647201ff0
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In Biomedical Engineering
In Computer Vision and Pattern Recognition
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Citations in the corpus
- Iconic feature based nonrigid registration: the PASHA algorithm (2003, background): “For example, so-called biomechanical models are often a mix of parametric and competitive regularization via finite elements [27].”
- A Review of Geometric Transformations for Nonrigid Body Registration (2008, background): “For instance to correct for brain-shift [14], [15] and facilitate navigation.”
- Patient-specific model of brain deformation: application to medical image registration. (2007, unknown): “In non-rigid image registration, with one notable exception of Xu and Nowinski (2001), linear solid-mechanics formulation has been used (Ferrant et al. 1999, 2001, 2002; Castellano-Smith et al., 2001; Hagemann et al., 1999; Spicer et al., 2004; Warfield et al., 2000, 2005).”
- RABBIT: Rapid Alignment of Brains by Building Intermediate Templates (2009, unknown): “, 1996), biomechanical model (Ferrant et al., 2000), or statistical constraints learned from the training samples (Loeckx et al.”
- Meshfree Particle Methods (2004, background): “An incomplete survey of more recent works shows a wide range of FEM-based strategies in computer vision, including object segmentation [5, 23], shape representation and characterization [11, 20], correspondence and motion estimation [18, 21], image registration [9], and image guided surgery [4, 7].”