Correcting fast irregular motion in PET: Maximum-Likelihood Motion and Activity (MLMA) reconstruction

Purpose PET imaging naturally suffers from motion blur due to long acquisitions. As such, motion-compensation provides a promising solution to improve image quality. Traditional methods for motion-correction often involve a combination of gating and data-binning, assuming that motion is periodic, to accumulate sufficient counts per motion-state frame. Irregular motion can be estimated but it requires complex motion-capture systems or elaborate data-driven algorithms, which are difficult to configure (physical or model setup) and hampered by the high noise of short timeframes. We propose a new method that alternatingly estimates and corrects for motion at high temporal frequency in PET imaging: Maximum-Likelihood Motion and Activity (MLMA) reconstruction. MLMA estimates both the time-series of deformation vector fields and the motion-corrected activity image for the whole acquisition. Together, this allows to visualize anatomical structures moving in time. Methods The method exploits the high compressibility and spatial smoothness of motion through a cubic B-spline motion-model and through spatial regularization. MLMA was configured to 2Hz resolution and applied on A) the digital XCAT phantom, B) acquisitions of a moving anthropomorphic torso phantom and C) clinical patient data. Results The results show that MLMA can accurately correct motion at high frequency (2Hz), with subvoxel accuracy (up to 2.5mm RMSE on 4mm isotropic voxels) and realistic breathing (amplitude range 14.7mm and average period 4.5s). This enables visually-noticeable improvements on image quality. Conclusion The proposed MLMA reconstruction method resolves the motion encoded in very-short PET timeframes, irrespective of the very low counts and noise inherent to PET projection data.