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Motion correction for cardiac SPECT using a RBI-ML partial-reconstruction approach

In single photon computed tomography, patient motion can significantly affect image quality. Methods to correct for patient motion rely on available information about the motion or stable algorithms have to be developed to detect and describe object motion. In this work we introduce a reconstruction method that corrects for rigid body motion and we investigate a method to exploit the motion information that might be hidden in the projection data and possibly modify motion descriptions that were retrieved by external motion tracking devices. The search method was based on reconstructing only parts of the projection angles using an RBI-ML partial-reconstruction approach (PRA). It tests a motion description with only one forward projection per detector head instead of a full reconstruction using all projections. A figure of merit based on the per angle likelihood function in projection space was introduced. With simulated MCAT data we could show that the PRA was able to identify the optimum 3D rigid body motion correction with an error of 1 pixel in each cartesian direction. Future work will address 3D translations, multiple motions, detector resolution compensation and will also concentrate on the improvement of the figure of merit.