Internal carotid artery stenosis measurements from 3D reconstructed multi-directional views using phantom data set on MRA image sequence

Abstract

Purpose To evaluate the inter-observer variability of stenosis measurements by using multi-directional 3D reconstructed projection view of internal carotid artery (ICA) that simulate the pre-determined stenosis degree on magnetic resonance angiography (MRA) image sequence of phantom data set and to compare the difference in measured percentages of maximum ICA stenosis between projection and axial images. Materials and methods By using adaptive region growing and circumscribed quadrangle on the clinical data set, the cylinder shape of a mathematical phantom was modeled and implemented. The maximum ICA stenosis was categorized as mild (30%), moderate (50%), and severe (70%) stenosis those simulated on the tomographic image sequence of ICA only and synthesized phantom. The 36 maximum intensity projection (MIP) images were radially projected at 10° increments that were rotated about the long axis of the body by using the simulated stenosis degree on the tomographic image sequence of phantom data sets. The six different projection image data sets were used to measure the minimum residual lumen and reference diameter by three blinded observers. The percentage of maximum ICA stenosis was calculated as the following. The ICA stenosis grading was [1 − (minimum residual lumen/averaged reference diameter)] × 100%. Results The percentage of maximum ICA stenosis degree measured on projection image was underestimated on ICA only phantom and overestimated on synthesized phantom compared to the simulated ICA stenosis degree on the axial image. In addition, the synthesized phantom provided the less projection image for stenosis measurement than ICA only phantom. These results attributed to the nature of MIP algorithm and the overlapping effects of surrounded anatomic structures such as other ICA, pair of external carotid artery (ECA), pair of VA, and background tissues. Furthermore, the inter-observer variability was also introduced by manual measurement. Conclusion The automated scheme is recommended to measure the ICA stenosis by using axial image. This technique is not only accurate as possible but also robust, simple to handle, and less time consuming compared with manual measurements. A computerized ICA stenosis measuring method, which applied the image processing technique on the axial image, is necessary to overcome the drawbacks introduced by using the projection image and manual measurement.