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Mutual information (Ml) is popular for registration via function optimization. This work proposes an inverse compositional formulation of Ml for Levenberg-Marquardt optimization. This yields a constant Hessian, which may be precomputed. Speed improvements of 15 percent were obtained, with convergence accuracies similar those of the standard formulation.