Computer-aided method for automated selection of optimal imaging plane for measurement of total cerebral blood flow by MRI Pang-yu Teng a, b , Ahmet Murat Bagci a , Noam Alperin* a, b a Department of Radiology, University of Illinois at Chicago, 1801 West Taylor ST ROOM 1A, Chicago, IL 60612; b Department of Bioengineering, University of Illinois at Chicago, Room 218 (M/C 063), 851 S. Morgan St., Chicago, IL 60607 ABSTRACT A computer-aided method for finding an optimal imaging plane for simultaneous measurement of the arterial blood inflow through the 4 vessels leading blood to the brain by phase contrast magnetic resonance imaging is presented. The method performance is compared with manual selection by two observers. The skeletons of the 4 vessels for which centerlines are generated are first extracted. Then, a global direction of the relatively less curved internal carotid arteries is calculated to determine the main flow direction. This is then used as a reference direction to identify segments of the vertebral arteries that strongly deviates from the main flow direction. These segments are then used to identify anatomical landmarks for improved consistency of the imaging plane selection. An optimal imaging plane is then identified by finding a plane with the smallest error value, which is defined as the sum of the angles between the plane’s normal and the vessel centerline’s direction at the location of the intersections. Error values obtained using the automated and the manual methods were then compared using 9 magnetic resonance angiography (MRA) data sets. The automated method considerably outperformed the manual selection. The mean error value with the automated method was significantly lower than the manual method, 0.09±0.07 vs. 0.53±0.45, respectively (p<.0001, Student’s t-test). Reproducibility of repeated measurements was analyzed using Bland and Altman’s test, the mean 95% limits of agreements for the automated and manual method were 0.01~0.02 and 0.43~0.55 respectively. Keywords: Image-guided procedures, volumetric flow measurement, phase contrast Magnetic Resonance Imaging, total cerebral blood flow, vessel centerline extraction 1. INTRODUCTION Measurement of total cerebral blood flow (tCBF) using velocity-encoded cine phase contrast magnetic resonance imaging (PCMRI) techniques are becoming feasible and more commonly used [1-4]. Measurement of tCBF with PCMRI is also used for calculation of other important clinical parameters such as regional CBF [5], and noninvasive intracranial compliance and pressure [6,7]. The measurement of tCBF is obtained by summation of the volumetric inflow in each of the main vessels leading blood to the brain, e.g., left and right internal carotid arteries (ICAs) and left and right vertebral arteries (VAs). Accurate measurement of the volumetric flow requires that the imaging plane is nearly perpendicular to the flow direction to avoid errors due to partial volume effects [11]. Zhao et al [8] used flow phantom to show that the error of the volumetric flow measurement increases above measurement variability when the angle between the flow direction and the normal to the imaging plane is greater than approximately 20 degrees. Therefore, selection of an imaging plane that is nearly perpendicular to the flow direction together with automated segmentation of the lumen boundaries [9] are important for measurement accuracy and reproducibility. Two semi-automated approaches for measurements of tCBF have been proposed [8,10]. The first approach requires separate scan for each of the four blood vessels. In this approach, the user selects a location in each of the four vessels and an imaging plane perpendicular to the flow at this location is determined based on an estimate of the direction of the vessel centerline at this location. In the second approach, a plane that is simultaneously nearly perpendicular to all 4 vessels is identified, allowing the tCBF measurement to be achieved using a single scan. *alperin@uic.edu; phone 1 312 996-3920 Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, edited by Michael I. Miga, Kenneth H. Wong, Proc. of SPIE Vol. 7261, 72613C © 2009 SPIE · CCC code: 1605-7422/09/$18 · doi: 10.1117/12.813819 Proc. of SPIE Vol. 7261 72613C-1