DATA-DRIVEN APPROACH FOR RESPIRATORY MOTION CORRECTION IN CARDIAC SPECT DATA

Cardiac SPECT perfusion imaging is important for diagnosis and evaluation of coronary artery diseases. However, the acquired image data are subject to motion blur due to patient respiratory motion. We propose a maximum-likelihood estimation (MLE) approach to determine a surrogate respiratory signal from short-time acquisition frames for motion correction. In the experiments we validated this approach first on a set of simulated phantom data with known respiratory motion, and then on clinical acquisitions from seven subjects. The results demonstrate that the proposed MLE approach could yield an accurate motion signal even with acquisition frame duration as short as 100 ms, and outperformed center-of-mass (CoM) and Laplacian eigenmaps (LE) methods.