A quadratic majorize-minimize framework for statistical estimation with noisy rician- and noncentral chi-distributed MR images
The statistics of noisy MR magnitude and square-root sum-of-squares MR images are well-described by the Rice and noncentral chi distributions, respectively. Statistical estimation involving these distributions is complicated by the facts that they have first- and second-order moments that depend nonlinearly on the noiseless image, and can have nonconvex negative log-likelihoods. This paper proposes a new majorize-minimize framework to ease the computational burden associated with statistical estimation involving these distributions. We derive quadratic tangent majorants for the negative log-likelihoods, which enables statistical cost functions to be optimized using a sequence of much simpler least-squares or regularized least-squares surrogate problems. We demonstrate the use of this framework in the context of regularized MR image denoising, with both simulated and experimental data.