In this paper an evaluation of methods determining the bolus arrival time (BAT) using a four-dimensional flow phantom to simulate 4D MR angiography is presented. Spatiotemporal 4D MRA images were acquired for analyzing the hemodynamic characteristics of cerebral vessel anomalies. Model-independent and model-dependent methods for BAT extraction are published. Generally, for the evaluation no gold standard exists and datasets with known BAT values are required. Here, a 4D flow phantom is generated based on a synthetic 3D MRA dataset with BAT values defining the time point of blood inflow for each voxel. Then, voxel-by-voxel concentration-time curves based on the gamma-variate function were computed leading to a simulated 4D MRA dataset. Additionally, partial volume effects and Gaussian noise were integrated. The simulated 4D MRA was visually inspected and regarded as similar to clinical data. Finally, phantom datasets with different vessel diameter and signal-to-noise ratio are computed. Three state-of-the-art methods were used to extract BAT values. Computed and known values were compared. The results suggest that model-dependent approaches perform better than the model-independent method.
In this paper an evaluation of methods determining the bolus arrival time (BAT) using a four-dimensional flow phantom to simulate 4D MR angiography is presented. Spatiotemporal 4D MRA images were acquired for analyzing the hemodynamic characteristics of cerebral vessel anomalies. Model-independent and model-dependent methods for BAT extraction are published. Generally, for the evaluation no gold standard exists and datasets with known BAT values are required. Here, a 4D flow phantom is generated based on a synthetic 3D MRA dataset with BAT values defining the time point of blood inflow for each voxel. Then, voxel-by-voxel concentration-time curves based on the gamma-variate function were computed leading to a simulated 4D MRA dataset. Additionally, partial volume effects and Gaussian noise were integrated. The simulated 4D MRA was visually inspected and regarded as similar to clinical data. Finally, phantom datasets with different vessel diameter and signal-to-noise ratio are computed. Three state-of-the-art methods were used to extract BAT values. Computed and known values were compared. The results suggest that model-dependent approaches perform better than the model-independent method.