Motion-compensated image reconstruction in gated cardiac SPECT

Single photon emission computed tomography (SPECT) is currently one of the most important techniques for detection and evaluation of coronary artery diseases. However, its effectiveness is often hampered by the presence of substantial amount of noise in the acquired image data. In this work we propose a spatio-temporal reconstruction approach, based on the use of motion compensation, to address the important issue of noise reduction in gated cardiac SPECT images.; We first consider image reconstruction in traditional gated SPECT perfusion imaging. We introduce a temporal prior in the form of motion compensation to account for the statistical correlations among the frames in a sequence, and reconstruct all the frames collectively as a single function of space and time. We demonstrated the method in our experiments using both simulated SPECT cardiac-perfusion images and clinical data. Besides bias-variance analysis and time activity curves, we also used a channelized Hotelling observer to evaluate the detectability of perfusion defects. Our experimental results demonstrated that the incorporation of temporal regularization into image reconstruction could significantly improve the accuracy of cardiac images without causing any significant cross-frame blurring that may arise from the cardiac motion.; We next present a new approach for estimating the dynamics of a radio-pharmaceutical in the myocardium. To account for the heart motion, the cardiac cycle is divided into a number of intervals. Within each gated frame, the time evolution at a pixel is modeled by a spline function. The spline control points are then reconstructed using a Bayesian framework, in which a smoothing prior is defined on all the gated frames based on the motion of the heart. The proposed algorithm is evaluated using a phantom simulating a gated SPECT perfusion acquisition with Tc-99m Teboroxime. We used signal to noise ratio analysis, bias-variance plots and time activity curves to measure the accuracy of the reconstructions. Our results demonstrate that the use of motion compensation can improve significantly both the accuracy of the reconstruction and the differentiation between sick and healthy tissue.