  Reliably and physically accurately transferring information between images
through deformable image registration with large anatomical differences is an
open challenge in medical image analysis. Most existing methods have two key
shortcomings: first, they require extensive up-front parameter tuning to each
specific registration problem, and second, they have difficulty capturing large
deformations and content mismatches between images. There have however been
developments that have laid the foundation for potential solutions to both
shortcomings. Towards the first shortcoming, a multi-objective optimization
approach using the Real-Valued Gene-pool Optimal Mixing Evolutionary Algorithm
(RV-GOMEA) has been shown to be capable of producing a diverse set of
registrations for 2D images in one run of the algorithm, representing different
trade-offs between conflicting objectives in the registration problem. This
allows the user to select a registration afterwards and removes the need for
up-front tuning. Towards the second shortcoming, a dual-dynamic grid
transformation model has proven effective at capturing large differences in 2D
images. These two developments have recently been accelerated through GPU
parallelization, delivering large speed-ups. Based on this accelerated version,
it is now possible to extend the approach to 3D images. Concordantly, this work
introduces the first method for multi-objective 3D deformable image
registration, using a 3D dual-dynamic grid transformation model based on
simplex meshes while still supporting the incorporation of annotated guidance
information and multi-resolution schemes. Our proof-of-concept prototype shows
promising results on synthetic and clinical 3D registration problems, forming
the foundation for a new, insightful method that can include bio-mechanical
properties in the registration.
