Preconditioned stochastic gradient descent optimisation for monomodal image registration

Authors:
Stefan Klein;Marius Staring;Patrik Andersson;Josien P. W. Pluim
Affiliations:
Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands;LKEB, Leiden University Medical Center, Leiden, The Netherlands;Image Sciences Institute, University Medical Center Utrecht, The Netherlands;Image Sciences Institute, University Medical Center Utrecht, The Netherlands
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Year:
2011

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Abstract

We present a stochastic optimisation method for intensitybased monomodal image registration. The method is based on a Robbins-Monro stochastic gradient descent method with adaptive step size estimation, and adds a preconditioning matrix. The derivation of the preconditioner is based on the observation that, after registration, the deformed moving image should approximately equal the fixed image. This prior knowledge allows us to approximate the Hessian at the minimum of the registration cost function, without knowing the coordinate transformation that corresponds to this minimum. The method is validated on 3D fMRI time-series and 3D CT chest follow-up scans. The experimental results show that the preconditioning strategy improves the rate of convergence.