Skip to Main Content
We developed a unique method for estimating and compensating rigid-body translations and rotations from scatter-and-attenuation-compensated projection data in iterative reconstruction when multiple projection angles are acquired at the same time. During reconstruction, both the non-attenuated and attenuated line-integrals are calculated. Their ratios are then multiplied to the scatter-corrected projection data to estimate scatter-and-attenuation-compensated projection data. At the end of each iteration, the sets of compensated projection data for the angles acquired at the same time are employed to calculate the center-of-mass and the inertia tensor, which are used to estimate the location and orientation of the imaging object by the principle-axes method. The estimated motion is applied in the next iteration to reposition the estimated slices and attenuation map in the projector and back-projector to match the pose of the patient at time the projections were acquired. To evaluate our method, we simulated an acquisition of the MCAT phantom with a 3-head SPECT system and imaged the Data Spectrum anthropomorphic phantom on a 3-head IRIX SPECT system. In simulations the phantom translated and rotated by the same amount 9 times. A numerical projector modeling the motion, attenuation, and distance-dependent blurring was used to generate the projection data. Poisson noise was added and 30 noise-realizations were generated. In the experiment with the anthropomorphic phantom, four 360-degree acquisitions were performed with the phantom translated or rotated beforehand. A motion-present dataset was made by mixing the 4 acquisitions. For both the MCAT phantom simulations and anthropomorphic phantom experiment, the motion-present data were reconstructed with 10 iterations of the OSEM which estimates and corrects the motion as described above. Our method obtained visually artifact-free reconstructions, while the reconstruction with no motion correction showed severe artifacts.- The motion estimated from our method was in good agreement with the motion simulated. We determined in MCAT simulated and actual phantom acquisitions that our data-driven approach was effective reducing motion artifacts.