Band-Limited Stokes LDDMM


The class of registration methods proposed in the framework of Stokes Large Deformation
Diffeomorphic Metric Mapping is a particularly interesting family of physically
meaningful diffeomorphic registration methods.
Stokes-LDDMM methods are formulated as a conditioned variational problem,
where the different physical models are imposed using the associated partial differential equations
as hard constraints.
The most significant limitation of Stokes-LDDMM framework is its huge computational complexity.
The objective of this work is to promote the use of Stokes-LDDMM in Computational Anatomy applications
with an efficient approximation of the original variational problem.
Thus, we propose a novel method for efficient Stokes-LDDMM diffeomorphic registration.
Our method poses the conditioned variational problem in the space of band-limited vector fields and
it is implemented in the GPU.
The performance of band-limited Stokes-LDDMM has been compared and evaluated with original Stokes-LDDMM,
EPDiff-LDDMM, and band-limited EPDiff-LDDMM.
The evaluation has been conducted in 3D with the Non-rigid image registration evaluation project database.  
Since the update equation in Stokes-LDDMM involves the action of low-pass filters,
the computational complexity has been greatly alleviated with a modest accuracy lose.
We have obtained a competitive performance for some method configurations.
Overall, our proposed method may make feasible the extensive use of novel physically meaningful
Stokes-LDDMM methods in different Computational Anatomy applications.
In addition, our results reinforce the usefulness of band-limited vector fields in diffeomorphic
registration methods involving the action of low-pass filters in the optimization, even in
algorithmically challenging environments such as Stokes-LDDMM.


This dataset accompanies the work titled Band-Limited Stokes LDDMM, submitted for consideration to the Journal of Biomedical and Health informatics. The data set contains the velocity fields used to generate the results shown in the manuscript.

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Citation Author(s):
Monica Hernandez
Submitted by:
Monica Hernandez
Last updated:
Tue, 10/10/2017 - 04:15
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[1] Monica Hernandez, "Band-Limited Stokes LDDMM", IEEE Dataport, 2017. [Online]. Available: http://dx.doi.org/10.21227/H24D0Q. Accessed: Oct. 23, 2017.
doi = {10.21227/H24D0Q},
url = {http://dx.doi.org/10.21227/H24D0Q},
author = {Monica Hernandez },
publisher = {IEEE Dataport},
title = {Band-Limited Stokes LDDMM},
year = {2017} }
T1 - Band-Limited Stokes LDDMM
AU - Monica Hernandez
PY - 2017
PB - IEEE Dataport
UR - 10.21227/H24D0Q
ER -
Monica Hernandez. (2017). Band-Limited Stokes LDDMM. IEEE Dataport. http://dx.doi.org/10.21227/H24D0Q
Monica Hernandez, 2017. Band-Limited Stokes LDDMM. Available at: http://dx.doi.org/10.21227/H24D0Q.
Monica Hernandez. (2017). "Band-Limited Stokes LDDMM." Web.
1. Monica Hernandez. Band-Limited Stokes LDDMM [Internet]. IEEE Dataport; 2017. Available from : http://dx.doi.org/10.21227/H24D0Q
Monica Hernandez. "Band-Limited Stokes LDDMM." doi: 10.21227/H24D0Q