Fully automatic nonrigid registration-based local motion estimation for motion-corrected iterative cardiac CT reconstruction.

PURPOSE Cardiac computed tomography is a rapidly emerging technique for noninvasive diagnosis of cardiovascular diseases. Nevertheless, the cardiac motion continues to be a limiting factor. Electrocardiogram-gated cardiac computed tomography reconstruction methods yield excellent results, but these are limited in their temporal resolution due to the mechanical movement of the gantry, and lead to residual motion blurring artifacts. If the motion of the cardiac region of interest is determined, motion compensated gated reconstructions can be applied to reduce motion artifacts. In this paper it is shown that elastic image registration methods can be an accurate solution to determine the cardiac motion. A method, which combines elastic registration and iterative computed tomography reconstruction methods delivering motion-corrected images of a chosen cardiac region of interest, is introduced. METHODS Using a gated four-dimensional region of interest image data set, a fully automatic elastic image registration is applied to recover a cardiac displacement field from a reference phase to a number of phases within the RR interval. Here, a stochastic optimizer and multiresolution approach are adopted to speed up the registration process. Subsequently, motion-compensated iterative reconstruction using the determined motion field is carried out. For the image representation volume-adapted spherical basis functions (blobs) are used to take the volume change caused by a divergent motion vector field into account. RESULTS The method is evaluated on phantom data and on four clinical data sets at a strong cardiac motion phase. Comparing the method to standard gated iterative reconstruction results shows that motion compensation strongly improves the image quality in these phases. A qualitative and quantitative accuracy study is presented for the estimated cardiac motion field. For the first time a blob-volume adaptation is applied on clinical data, and in the case of divergent motion it yields improved image quality. CONCLUSIONS A fully automatic local cardiac motion compensated gated iterative method with volume-adapted blobs is proposed. The method leads to excellent motion-corrected images which outperform nonmotion corrected results in phases of strong cardiac motion. In clinical cases, a volume-dependent blob-footprint adaptation proves to be a good solution to take care of the change in the blob volume caused by a divergent motion field.