Computational approaches for parametric imaging of dynamic PET data

TL;DR

This paper introduces a fast, regularized optimization method for parametric imaging in dynamic PET data, improving the reconstruction of kinetic parameter maps from complex, ill-posed inverse problems.

Contribution

It proposes a novel regularized affine-scaling Trust Region algorithm for efficient parametric image reconstruction in brain PET imaging.

Findings

The new method outperforms traditional algorithms in simulation tests.

It provides faster convergence and improved stability.

Validation shows accurate kinetic parameter maps.

Abstract

Parametric imaging of nuclear medicine data exploits dynamic functional images in order to reconstruct maps of kinetic parameters related to the metabolism of a specific tracer injected in the biological tissue. From a computational viewpoint, the realization of parametric images requires the pixel-wise numerical solution of compartmental inverse problems that are typically ill-posed and nonlinear. In the present paper we introduce a fast numerical optimization scheme for parametric imaging relying on a regularized version of the standard affine-scaling Trust Region method. The validation of this approach is realized in a simulation framework for brain imaging and comparison of performances is made with respect to a regularized Gauss-Newton scheme and a standard nonlinear least-squares algorithm.