Direct Parametric Reconstruction Using Anatomical Regularization for Simultaneous PET/MRI Data

PROJECT TITLE :

Direct Parametric Reconstruction Using Anatomical Regularization for Simultaneous PET/MRI Data

ABSTRACT:

Pharmacokinetic analysis of dynamic positron emission tomography (PET) imaging data maps the measured time activity curves to a group of model-specific pharmacokinetic parameters. Voxel-based mostly parameter estimation via curve fitting is conventionally performed indirectly on a sequence of independently reconstructed PET pictures, leading to high variance and bias in the parametric pictures. We have a tendency to propose an immediate parametric reconstruction algorithm with raw projection data as input that leverages high-resolution anatomical data simultaneously obtained from magnetic resonance (MR) imaging in a very PET/MRI scanner for regularization. The reconstruction drawback is formulated during a versatile Bayesian framework with Gaussian Markov Random field modeling of activity, parameters, or both simultaneously. MR info is incorporated through a Bowsher-like previous function. Optimization transfer using an expectation-maximization surrogate and a new Bowsher-like penalty surrogate is applied to obtain a voxel-separable algorithm that interleaves a reconstruction with a fitting step. An analytical input operate model is used. The algorithm is evaluated on simulated [ ]FDG and clinical [ ]FET brain data acquired with a Biograph mMR. The results indicate that direct and simultaneously regularized parametric reconstruction increases image quality. Anatomical regularization leads to higher distinction than conventional distance-weighted regularization.

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