To permit optimization of 4D flow protocols in imaging of thoracic aorta, a flow phantom was designed and constructed from clear acrylic plastic. The phantom was precision machined out of clear acrylic plastic for continuous flow, ability to see unwanted air bubbles, and MR compatibility. The solid model of the phantom was designed in SolidWorks and fed to a computer numeric control (CNC) machine for precision machining. The design permits the operator to switch aortic valves constructed from a silicone mold with various degrees of calcifications (different percentage openings), modeling an aortic valve at various stages of disease. The valve opens and closes during the cardiac cycle as in the in-vivo case. The inner diameter of the tube throughout the phantom was 1”, which corresponds to human anatomical measurements in the average person. The phantom was placed in an MR compatible flow circuit, with a 60:40 distilled water/glycerol fluid mixture resulting in a viscosity of 0043 Pa*s and density of 1,060 kg/m3 similar to those of blood. The pump driving the working fluid in the phantom is programmable, capable of delivering physiologic flow rates up to peak flow of 400 ml/s The phantom was placed inside a Philips Achieva 1.5 T scanner and imaged with a 16 element XL Torso Coil. 4D flow imaging was performed at a Venc of 250 cm/s. The field of view was 120 mm x 120 mm x 150 mm, with a voxel size of 1.5 mm x 1.5 mm x 5 mm, and 14 phases. Other scan parameters were as follows: TR=11 ms, TE=4 ms and TFE factor=2.
Segmentation of the aorta from CT and MR data is important in order to quantitatively assess diseases of the aorta including aortic dissection and distention of aortic aneurysm, among others. In this paper, we propose a segmentation method to extract exact the 3D boundary of the aorta via graph-cuts segmentation. The graph-cuts technique is able to avoid local minima with global optimization and can be applied to 3D and higher dimension with fast computation. We performed 3D segmentation using this method for five CT data sets. The user selects seed points for aorta region as 'object' and surrounding tissues as 'background' on an axial slice of the 3D CT data and the algorithm calculates the cost of n-link (neighborhood-link) and t-link (terminal-link), and computes the minimum cut separating the aorta from the background by applying the max-flow/min-cut algorithm. Results were validated against manually traced aorta boundaries. The mean Dice Similarity Coefficient for the five 3D segmentations was 0.9381. The 3D segmentation took less than five minutes for data sets of size 512×512×244 to 512×512×284.