Detection of motion during tomographic acquisition by an optical flow algorithm.

An automatic procedure to detect and quantify patient motion during the acquisition of the tomographic views in single photon emission computerized tomography (SPECT) is proposed. This method first computes the optical flow vector field which assigns to each pixel of a tomographic view the two-dimensional displacement vector that describes its motion between two successive views. The average optical flow in a region of interest is then computed to measure its inter-view global motion. This algorithm is tested on a point source, on a cardiac phantom (with and without induced motion), and on a patient. The proposed method can accurately detect the presence of motion, localize the camera angle at which motion occurred, and measure the distance of motion. The optical flow method can be used to control the quality of the tomographic acquisition and to alert the user to the potential of reconstruction artifacts due to patient motion. It can also be used to correct for the translational motion in the direction of the axis of rotation of the camera.

An expectation maximization reconstruction algorithm for emission tomography with non-uniform entropy prior.

A Bayesian image reconstruction algorithm is proposed for emission tomography. It incorporates the Poisson nature of the noise in the projection data and uses a non-uniform entropy as an a priori probability distribution of the image in a maximum a posteriori (MAP) approach. The expectation maximization (EM) method was applied to find the MAP estimator. The Newton-Raphson numerical method whose convergence and positive solutions are proven, was used to solve the EM problem. The prior mean at iteration k was determined by smoothing the image obtained at iteration k-1. Comparisons between the ML and the MAP algorithm were carried out with a numerical phantom that contains a narrow valley region. The ML solution after 50 iterations was chosen as the initial solution for the MAP algorithm, since the global performance of the ML algorithm deteriorates with increasing number of iterations while its local performance in the valley region is always improving. The resulting algorithm is a compromise between ML who has the best local performance in the valley region and the MAP who has the best global performance.