Motion-compensated reconstruction based on filtered backprojection for helical head CT

Abstract

In head CT imaging, it is assumed that the patient’s head does not move during the CT acquisition. In clinical practice, however, the head sometimes moves and thereby causes considerable motion artifacts on the reconstructed image. To solve this motion artifact problem, motion estimation (ME) and motion-compensated (MC) reconstruction are needed. Reliable MC reconstruction is especially critical, because it is usually used for ME in addition to motion compensation. In this work, we propose a novel MC reconstruction algorithm for helical head CT, under the assumption that the head motion is rigid. CT acquisition of a rigidly moving object in a helical scan geometry can be considered as the acquisition of a static object in the scan geometry virtually transformed according to the motion. Based on this consideration, we propose a MC reconstruction algorithm by assuming that the head motion is already estimated. The algorithm consists of three consecutive steps, namely, MC rebinning, tangential filtering, and weighted backprojection. In the rebinning step, a virtually transformed helical geometry according to the motion is carefully taken account of, and a new weighting function is introduced to the backprojection step to minimize unwanted artifacts. To evaluate the proposed algorithm, we perform simulations by using a numerical phantom with pre-defined motion in a helical scan geometry. The proposed MC algorithm well restores a reconstructed image that is corrupted by motion, and thereby achieves the image quality comparable to that of the phantom with no motion.