title: Preconditioned stochastic gradient descent optimisation for monomodal image registration.
abstract: We present a stochastic optimisation method for intensity-based 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 pre-conditioner 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.
authors: Biomedical Imaging Group Rotterdam, Erasmus MC, Rotterdam, The Netherlands. s.klein@erasmusmc.nl
authors: Stefan
authors: S
authors: Klein
authors: Marius
authors: M
authors: Staring
authors: Patrik
authors: P
authors: Andersson
authors: Josien P W
authors: JP
authors: Pluim
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D008962:Models, Theoretical; D013902:Radiography, Thoracic / Q000379:methods; D013269:Stochastic Processes; D014057:Tomography, X-Ray Computed / Q000379:methods*