Robust automated bolus tracker positioning for MRI liver scans

Purpose To improve the workflow of MRI abdominal scans by reducing the examination time and operator skill dependence related to bolus tracker positioning. Methods Ten or more axial images of two-dimensional scout scan were analyzed to identify the aorta and detect its center position using the mean shift to allow automated bolus tracker placement. Adaptive boosting (AdaBoost) classifier was used to identify the aorta rotating a sub-window around the cerebrospinal fluid (CSF), the location of which was detected in each axial image in advance. The search region of the aorta in the next inferior axial image was restricted to half to reduce computation time. Tests were conducted using the proposed method with a 1.5 T scanner in 31 volunteers. Results The success rate of aorta detection was 98.4%, and the accuracy of center location was around 0 – 5 mm shift from the true center. The computation time was 30 s on MATLAB, which was half that required for non-restrictive search. Conclusion The proposed algorithm was able to accurately detect the aorta in all volunteers with practical computation time so that the automated bolus tracker placement improved the workflow of MRI abdominal scans.