Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images Export

@article{citeulike:917284, abstract = {Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.}, author = {Jenkinson, Mark and Bannister, Peter and Brady, Michael and Smith, Stephen}, citeulike-article-id = {917284}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/S1053-8119(02)91132-8}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6WNP-46XH1RW-Y/2/29d8df4eb5d8124e9ea9315869e3c6b9}, doi = {10.1016/S1053-8119(02)91132-8}, journal = {NeuroImage}, keywords = {artifacts, fmri, noise}, month = {October}, number = {2}, pages = {825--841}, posted-at = {2006-10-30 06:57:19}, priority = {3}, title = {Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images}, url = {http://dx.doi.org/10.1016/S1053-8119(02)91132-8}, volume = {17}, year = {2002} }

TY - JOUR ID - citeulike:917284 L3 - citeulike-article-id:917284 N2 - Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods. IS - 2 JF - NeuroImage EP - 841 TI - Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images VL - 17 SP - 825 KW - artifacts KW - fmri KW - noise AU - Jenkinson, Mark AU - Bannister, Peter AU - Brady, Michael AU - Smith, Stephen PY - 2002/10// UR - http://dx.doi.org/10.1016/S1053-8119(02)91132-8 ER -