IEEE transactions on medical imaging
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IEEE Trans Med Imaging · Feb 2012
Reference-free PRFS MR-thermometry using near-harmonic 2-D reconstruction of the background phase.
Proton resonance frequency shift (PRFS) MR thermometry (MRT) is the generally preferred method for monitoring thermal ablation, typically implemented with gradient-echo (GRE) sequences. Standard PRFS MRT is based on the subtraction of a temporal reference phase map and is, therefore, intrinsically sensitive to tissue motion (including deformation) and to external perturbation of the magnetic field. Reference-free (or reference-less) PRFS MRT has been previously described by Rieke and was based on a 2-D polynomial fit performed on phase data from outside the heated region, to estimate the background phase inside the region of interest. ⋯ A reference-free PRFS thermometry method based on the theoretical framework of harmonic functions is described and evaluated here. The computing time is compatible with online monitoring during local thermotherapy. The current reference-free MRT approach expands the workflow flexibility, eliminates the need for respiratory triggers, enables higher temporal resolution, and is insensitive to unique-event motion of tissue.
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IEEE Trans Med Imaging · Feb 2012
HRF estimation in fMRI data with an unknown drift matrix by iterative minimization of the Kullback-Leibler divergence.
Hemodynamic response function (HRF) estimation in noisy functional magnetic resonance imaging (fMRI) plays an important role when investigating the temporal dynamic of a brain region response during activations. Nonparametric methods which allow more flexibility in the estimation by inferring the HRF at each time sample have provided improved performance in comparison to the parametric methods. In this paper, the mixed-effects model is used to derive a new algorithm for nonparametric maximum likelihood HRF estimation. ⋯ This allows the effective representation of a broader class of drift signals and therefore the reduction of the error in approximating the drift component. Estimates of the HRF and the hyperparameters are derived by iterative minimization of the Kullback-Leibler divergence between a model family of probability distributions defined using the mixed-effects model and a desired family of probability distributions constrained to be concentrated on the observed data. The performance of proposed method is demonstrated on simulated and real fMRI data, the latter originating from both event-related and block design fMRI experiments.