FLASH: Fast Landmark Aligned Spherical Harmonic Parameterization for Genus-0 Closed Brain Surfaces

@article{Choi2015FLASHFL,
  title={FLASH: Fast Landmark Aligned Spherical Harmonic Parameterization for Genus-0 Closed Brain Surfaces},
  author={Pui Tung Choi and Ka Chun Lam and Lok Ming Lui},
  journal={SIAM J. Imaging Sci.},
  year={2015},
  volume={8},
  pages={67-94}
}
Surface registration between cortical surfaces is crucial in medical imaging for performing systematic comparisons between brains. Landmark-matching registration that matches anatomical features, called the sulcal landmarks, is often required to obtain a meaningful 1-1 correspondence between brain surfaces. This is commonly done by parameterizing the surface onto a simple parameter domain, such as the unit sphere, in which the sulcal landmarks are consistently aligned. Landmark-matching surface… 
Adaptive area-preserving parameterization of open and closed anatomical surfaces
Graph-Constrained Surface Registration Based on Tutte Embedding
  • W. Zeng, Yi-Jun Yang, M. Razib
  • Computer Science
    2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2016
TLDR
This work presents an efficient method to compute the registration between surfaces with consistent graph constraints based on Tutte graph embedding, which solves sparse linear systems and is computationally efficient and robust.
Large Deformation Multiresolution Diffeomorphic Metric Mapping for Multiresolution Cortical Surfaces: A Coarse-to-Fine Approach
TLDR
A fast coarse-to-fine algorithm for surface registration is proposed by adapting the large diffeomorphic deformation metric mapping (LDDMM) framework for surface mapping and improvements in speed and accuracy are shown via a multiresolution analysis of surface meshes and the construction ofMultiresolution diffeomorph transformations.
Shape analysis via inconsistent surface registration
TLDR
This work develops a framework for shape analysis using inconsistent surface mapping using quasi-conformal distortion and differences in mean and Gaussian curvatures, thereby providing a natural way for shape classification and shed light on the interplay between function and shape in nature.
Structural Surface Mapping for Shape Analysis
TLDR
This dissertation explores structural mappings for shape analysis of surfaces using the feature graphs as constraints by proposing structural brain mapping which maps the brain cortical surface onto a planar convex domain and proposing a novel brain registration technique based on an intrinsic atlas-constrained harmonic map.
Intrinsic Patch-Based Cortical Anatomical Parcellation Using Graph Convolutional Neural Network on Surface Manifold
TLDR
The method aims to learn the highly nonlinear mapping between cortical attribute patterns (on local intrinsic surface patches) and parcellation labels, which achieves comparable accuracy to the methods using spherical mapping, and works well on cortical surfaces violating the spherical topology.
Modeling the Space of Point Landmark Constrained Diffeomorphisms
TLDR
A novel model of the space of point landmark constrained diffeomorphisms based on Teichmuller theory, where the Beltrami coefficients are the solutions to a linear equation group, is proposed, which proves the efficiency and efficacy of the proposed method.
...
...

References

SHOWING 1-10 OF 40 REFERENCES
Surface-Constrained Volumetric Brain Registration Using Harmonic Mappings
TLDR
This work describes a method for volumetric registration that also produces an accurate one-to-one point correspondence between cortical surfaces by first parameterizing and aligning the cortical surfaces using sulcal landmarks and then using a constrained harmonic mapping to extend this surface correspondence to the entire cortical volume.
Optimized Conformal Surface Registration with Shape-based Landmark Matching
TLDR
This work generates optimized conformal parameterizations which match landmark curves exactly with shape-based correspondences between them and proposes a variational method to minimize a compound energy functional that measures the harmonic energy of the parameterization maps and the shape dissimilarity between mapped points on the landmark curves.
Hyperbolic Harmonic Mapping for Constrained Brain Surface Registration
  • Rui Shi, W. Zeng, X. Gu
  • Computer Science
    2013 IEEE Conference on Computer Vision and Pattern Recognition
  • 2013
TLDR
The algorithm is applied to study constrained human brain surface registration problem and demonstrates that, by changing the Riemannian metric, the registrations are always diffeomorphic, and achieve relative high performance when evaluated with some popular cortical surface registration evaluation standards.
Diffeomorphic Sulcal Shape Analysis on the Cortex
TLDR
A diffeomorphic approach for constructing intrinsic shape atlases of sulci on the human cortex by computing geodesics in the quotient space of shapes modulo scales, translations, rigid rotations, and reparameterizations is presented.
Optimization of Brain Conformal Mapping with Landmarks
TLDR
A new method is proposed, based on a new energy functional, to optimize the conformal parameterization of cortical surfaces by using landmarks, showing that the landmark mismatch energy can be greatly reduced while effectively preserving conformality.
Diffeomorphic Brain Registration Under Exhaustive Sulcal Constraints
TLDR
A global, geometric approach that performs the alignment of the exhaustive sulcal imprints (cortical folding patterns) across individuals and how the optimized alignment of cortical folds across subjects improves sensitivity in the detection of functional activations in a group-level analysis of neuroimaging data is illustrated.
Brain Surface Conformal Parameterization Using Riemann Surface Structure
TLDR
A parameterization method based on Riemann surface structure is introduced, which uses a special curvilinear net structure (conformal net) to partition the surface into a set of patches that can each be conformally mapped to a parallelogram.
A surface-based technique for warping three-dimensional images of the brain
TLDR
A fast, spatially accurate technique for calculating the high-dimensional deformation field relating the brain anatomies of an arbitrary pair of subjects and applications are discussed, including the transfer of multisubject 3-D functional, vascular, and histologic maps onto a single anatomic template.
Measuring Brain Variability Via Sulcal Lines Registration: A Diffeomorphic Approach
In this paper we present a new way of measuring brain variability based on the registration of sulcal lines sets in the large deformation framework. Lines are modelled geometrically as currents,
...
...