A massively parallel approach to deformable matching of 3D medical images via stochastic differential equations

The deformable matching of 3D medical images remains a difficult problem due to the high dimension of both geometric transformations and data. The matching problem is usually expressed as the minimization of a highly non-linear energy (objective) function, yielding a hard, computationally intensive, optimization problem. This paper presents a comprehensive parallel approach that yields computation times compatible with clinical routine. The image matching is based on the simulation of stochastic differential equations, enabling the optimization of the global objective function, through an annealing process. The resulting algorithm allows a fully parallel sampling of the parameters to be optimized. Due to the large number of parameters involved in deformable matching, this approach is naturally suited to massively parallel implementations. We present implementation issues and timing analysis on an MIMD parallel processing computer (SGI Origin 2000). The performances of the approach are assessed on real data, using 3D brain MR images from different individuals. Beside yielding accurate registrations, the parallel algorithm exhibits excellent relative speedups.