Data-driven motion compensated SPECT reconstruction for liver radioembolization

The need for quantitative accuracy of single photon emission computed tomography (SPECT) image analysis is increasing with the emergence of targeted radionuclide therapies such as liver radioembolization. Breathing motion is a major issue for quantitation as it leads to misestimation of the tumor activity in the SPECT images. In this paper, we developed a data-driven motion compensated SPECT reconstruction algorithm to account for respiratory motion. A respiratory signal was retrospectively extracted from SPECT listmode data with the Laplacian Eigenmaps algorithm and used to sort the projections into temporal bins of fixed phase width. A 2D affine motion was then estimated between projections at different phases. The transformation parameters were used to re-bin the list-mode data into one set of compensated projections that was then used to reconstruct a 3D motion-compensated SPECT image using all available events of the list-mode data. The method was evaluated on both simulated and real SPECT acquisitions of liver patients, and compared to respiratory-gated reconstruction. The motion-compensated reconstruction retrieved larger activity in the tumors compared to conventional 3D SPECT reconstruction with a better contrast-to-noise ratio than gated reconstruction.