RATIONALE AND OBJECTIVES: An image registration method was developed to automatically correct motion artifacts, mostly from breathing, from cardiac cine magnetic resonance (MR) images. MATERIALS AND METHODS: The location of each slice in an image stack was optimized by maximizing a similarity measure of the slice with another image slice stack. The optimization was performed iteratively and both image stacks were corrected simultaneously. Two procedures to optimize the similarity were tested: standard gradient optimization and stochastic optimization in which one slice is chosen randomly from the image stacks and its location is optimized. In this work, cine short- and long-axis images were used. In addition to visual inspection results from real data, the performance of the algorithm was evaluated quantitatively by simulating the movements in four real MR data sets. The mean error and standard deviation were defined for 50 simulated movements as each slice was randomly displaced. The error rate, defined as the percentage of non-satisfactory registration results, was evaluated. The paired t-test was used to evaluate the statistical difference between the tested optimization methods. RESULTS: The algorithm developed was successfully applied to correct motion artifacts from real and simulated data. The results, where typical motion artifacts were simulated, indicated an error rate of about 3%. Subvoxel registration accuracy was also achieved. When different optimization methods were compared, the registration accuracy of the stochastic approach proved to be superior to the standard gradient technique (P < 10(-9)). CONCLUSIONS: The novel method was capable of robustly and accurately correcting motion artifacts from cardiac cine MR images.
RATIONALE AND OBJECTIVES: An image registration method was developed to automatically correct motion artifacts, mostly from breathing, from cardiac cine magnetic resonance (MR) images. MATERIALS AND METHODS: The location of each slice in an image stack was optimized by maximizing a similarity measure of the slice with another image slice stack. The optimization was performed iteratively and both image stacks were corrected simultaneously. Two procedures to optimize the similarity were tested: standard gradient optimization and stochastic optimization in which one slice is chosen randomly from the image stacks and its location is optimized. In this work, cine short- and long-axis images were used. In addition to visual inspection results from real data, the performance of the algorithm was evaluated quantitatively by simulating the movements in four real MR data sets. The mean error and standard deviation were defined for 50 simulated movements as each slice was randomly displaced. The error rate, defined as the percentage of non-satisfactory registration results, was evaluated. The paired t-test was used to evaluate the statistical difference between the tested optimization methods. RESULTS: The algorithm developed was successfully applied to correct motion artifacts from real and simulated data. The results, where typical motion artifacts were simulated, indicated an error rate of about 3%. Subvoxel registration accuracy was also achieved. When different optimization methods were compared, the registration accuracy of the stochastic approach proved to be superior to the standard gradient technique (P < 10(-9)). CONCLUSIONS: The novel method was capable of robustly and accurately correcting motion artifacts from cardiac cine MR images.
Authors: Rebecca A B Burton; Gernot Plank; Jürgen E Schneider; Vicente Grau; Helmut Ahammer; Stephen L Keeling; Jack Lee; Nicolas P Smith; David Gavaghan; Natalia Trayanova; Peter Kohl Journal: Ann N Y Acad Sci Date: 2006-10 Impact factor: 5.691