Dynamic Cell Imaging in PET with Optimal Transport Regularization
This addresses the problem of tracking single or multiple cells in PET imaging for medical research, offering a method that is robust in low-radioactivity settings and does not require prior knowledge of cell count, though it is incremental in improving reconstruction techniques.
The paper tackles dynamic PET imaging for tracking cells by proposing a reconstruction method that enforces temporal consistency via optimal transport regularization, achieving a tracking accuracy of 5.3 mm for four cells moving at 3.1 mm/s with a count rate of 1.1 cps.
We propose a novel dynamic image reconstruction method from PET listmode data that could be particularly suited to tracking single or small numbers of cells. In contrast to conventional PET reconstruction our method combines the information from all detected events not only to reconstruct the dynamic evolution of the radionuclide distribution, but also to improve the reconstruction at each single time point by enforcing temporal consistency. This is achieved via optimal transport regularization where in principle, among all possible temporally evolving radionuclide distributions consistent with the PET measurement, the one is chosen with least kinetic motion energy. The reconstruction is found by convex optimization so that there is no dependence on the initialization of the method. We study its behaviour on simulated data of a human PET system and demonstrate its robustness even in settings with very low radioactivity. In contrast to previously reported cell tracking algorithms, our technique is oblivious to the number of tracked cells. Without any additional complexity one or multiple cells can be reconstructed, and the model automatically determines the number of particles. For instance, four radiolabelled cells moving at a velocity of 3.1 mm/s and a PET recorded count rate of 1.1 cps (for each cell) could be simultaneously tracked with a tracking accuracy of 5.3 mm inside a simulated human body.