Spatiotemporal PET reconstruction using ML-EM with learned diffeomorphic deformation
This addresses scalability issues in motion correction for PET imaging, which is an incremental improvement for medical imaging applications.
The paper tackles motion blur in PET reconstruction by proposing a novel motion correction algorithm that combines enhanced ML-EM with deep learning-based registration, showing it can significantly reduce noise compared to using limited gates.
Patient movement in emission tomography deteriorates reconstruction quality because of motion blur. Gating the data improves the situation somewhat: each gate contains a movement phase which is approximately stationary. A standard method is to use only the data from a few gates, with little movement between them. However, the corresponding loss of data entails an increase of noise. Motion correction algorithms have been implemented to take into account all the gated data, but they do not scale well, especially not in 3D. We propose a novel motion correction algorithm which addresses the scalability issue. Our approach is to combine an enhanced ML-EM algorithm with deep learning based movement registration. The training is unsupervised, and with artificial data. We expect this approach to scale very well to higher resolutions and to 3D, as the overall cost of our algorithm is only marginally greater than that of a standard ML-EM algorithm. We show that we can significantly decrease the noise corresponding to a limited number of gates.