A NEW METRIC DESIGN TO AUTOMATIC DETECTION OF PATIENT MOTION IN SINGLE PHOTON EMISSION COMPUTED TOMOGRAPHY

Amongst the motion detection and correction algorithms during the scanning procedures, data-processing methods are the most frequently proposed solution to detect and correct patient motions. There are different distance metrics which have been used to detect the patient motions using information contained in the projections. Unfortunately, the performance of usually used metrics is low in the case of small motions while detecting the motions with magnitude of 1 pixel and smaller are very important in the accuracy of diagnosis. In this work, a new distance metric, normalized prediction of projection data algorithm (NPPDA) is developed based on the linear prediction filter. The performance of the NPPDA is quantitatively evaluated and compared with usual distance metrics by different experimental studies. A high detection rate is achieved by means of the newly developed distance metric, NPPDA.