Simultaneous Reconstruction and Segmentation for Dynamic SPECT Imaging

TL;DR

This paper introduces a variational framework for simultaneous reconstruction and segmentation in dynamic SPECT imaging, effectively handling Poisson noise and nonlinear operators, with theoretical analysis and computational validation.

Contribution

It proposes a novel combined reconstruction-segmentation model for dynamic SPECT that accounts for Poisson noise and nonlinear forward operators, with proven existence and error estimates.

Findings

The model successfully reconstructs and segments synthetic SPECT data.

The approach outperforms standard EM reconstructions under Poisson noise.

Theoretical analysis confirms existence of minimizers and provides error bounds.

Abstract

This work deals with the reconstruction of dynamic images that incorporate characteristic dynamics in certain subregions, as arising for the kinetics of many tracers in emission tomography (SPECT, PET). We make use of a basis function approach for the unknown tracer concentration by assuming that the region of interest can be divided into subregions with spatially constant concentration curves. Applying a regularized variational framework reminiscent of the Chan-Vese model for image segmentation we simultaneously reconstruct both the labelling functions of the subregions as well as the subconcentrations within each region. Our particular focus is on applications in SPECT with Poisson noise model, resulting in a Kullback-Leibler data fidelity in the variational approach. We present a detailed analysis of the proposed variational model and prove existence of minimizers as well as error estimates. The latter apply to a more general class of problems and generalize existing results in literature since we deal with a nonlinear forward operator and a nonquadratic data fidelity. A computational algorithm based on alternating minimization and splitting techniques is developed for the solution of the problem and tested on appropriately designed synthetic data sets. For those we compare the results to those of standard EM reconstructions and investigate the effects of Poisson noise in the data.