Motion is one major problem of magnetic resonance imaging (MRI) of the coronary vessels. Despite of cardiac motion of the beating heart itself respiratory motion has to be considered (see MR movie of respiratory motion of the heart). Respiratory motion is commonly suppressed by gating techniques, which reduce scan efficiency. In principle modern MR scanners are able to correct those motion patterns prospectively, which can be described by a 3D affine transformation. A prospective motion correction approach could be used to increase the respiratory gating window and in consequence to reduce scan time. However, in order to achieve a sufficient correction the motion must be described quantitatively. In this initial study the respiratory motion of the heart (coronary vessels) was analysed and a new motion model described by an affine transformation was compared to two rigid motion models. The affine transformation model achieved a better fit (mean error < 1 mm for coronary vessels) than a rigid motion model describing translation in all directions (mean error < 2 mm) and a rigid motion model covering only superior-inferior motion (mean error <6 mm).
D. Manke, P. Rösch, K. Nehrke, P. Börnert, and O. Dössel. Model evaluation and calibration for prospective respiratory motion correction in coronary MR angiography based on 3-D image registration. In IEEE Transactions on Medical Imaging, vol. 21(9) , pp. 1132-1141, 2002
Image processing was used as a fundamental tool to derive motion information from magnetic resonance (MR) images, which was fed back into prospective respiratory motion correction during subsequent data acquisition to improve image quality in coronary MR angiography (CMRA) scans. This reduces motion artifacts in the images and, in addition, enables the usage of a broader gating window than commonly used today to increase the scan efficiency. The aim of the study reported in this paper was to find a suitable motion model to be used for respiratory motion correction in cardiac imaging and to develop a calibration procedure to adapt the motion model to the individual patient. At first, the performance of three motion models [one-dimensional translation in feet-head (FH) direction, three-dimensional (3-D) translation, and 3-D affine transformation] was tested in a small volunteer study. An elastic image registration algorithm was applied to 3-D MR images of the coronary vessels obtained at different respiratory levels. A strong intersubject variability was observed. The 3-D translation and affine transformation model were found to be superior over the conventional FH translation model used today. Furthermore, a new approach is presented, which utilizes a fast model-based image registration to extract motion information from time series of low-resolution 3-D MR images, which reflects the respiratory motion of the heart. The registration is based on a selectable global 3-D motion model (translation, rigid, or affine transformation). All 3-D MR images were registered with respect to end expiration. The resulting time series of model parameters were analyzed in combination with additionally acquired motion information from a diaphragmatic MR pencil-beam navigator to calibrate the respiratory motion model. To demonstrate the potential of a calibrated motion model for prospective motion correction in coronary imaging, the approach was tested in CMRA examinations in five volunteers.