Accuracy of detecting enlargement of aneurysms using different MRI modalities and measurement protocols.

OBJECTIVE: Aneurysm growth is considered predictive of future rupture of intracranial aneurysms. However, how accurately neuroradiologists can reliably detect incremental aneurysm growth using clinical MRI is still unknown. The purpose of this study was to assess the agreement rate of detecting aneurysm enlargement employing generally used MRI modalities. METHODS: Three silicone flow phantom models, each with 8 aneurysms of various sizes at different sites, were used in this study. The aneurysm models were identical except for an incremental increase in the sizes of the 8 aneurysms, which ranged from 0.4 mm to 2 mm. The phantoms were imaged on 1.5-T and 3-T MRI units with both time-of-flight (TOF) and contrast-enhanced MR angiography. Three independent expert neuroradiologists measured the aneurysms in a blinded manner using different measurement approaches. The individual and agreement detection rates of aneurysm enlargement among the 3 experts were calculated. RESULTS: The mean detection rate of any increase in any aneurysmal dimension was 95.7%. The detection rates of the 3 observers (observers A, B, and C) were 98.0%, 96.6%, and 92.7%, respectively (p = 0.22). The detection rates of each MRI modality were 91.3% using 1.5-T TOF, 97.2% using 1.5-T with Gd, 95.8% using 3.0-T TOF, and 97.2% using 3.0-T with Gd (p = 0.31). On the other hand, the mean detection rate for aneurysm enlargement was 54.8%. Specifically, the detection rates of observers A, B, and C were 49.0%, 46.1%, and 66.7%, respectively (p = 0.009). As the incremental enlargement value increased, the detection rate for aneurysm enlargement increased. The use of 1.5-T Gd improved the detection rate for small incremental enlargement (e.g., 0.4­1 mm) of the aneurysm (p = 0.04). The location of the aneurysm also affected the detection rate for aneurysm enlargement (p < 0.0001). CONCLUSIONS: The detection rate and interobserver agreement were very high for aneurysm enlargement of 0.4­2 mm. The detection rate for at least 1 increase in any aneurysm dimension did not depend on the choice of MRI modality or measurement protocol. Use of Gd improved the accuracy of measurement. Aneurysm location may influence the accuracy of detecting enlargement.

From medical images to flow computations without user-generated meshes.

Biomedical flow computations in patient-specific geometries require integrating image acquisition and processing with fluid flow solvers. Typically, image-based modeling processes involve several steps, such as image segmentation, surface mesh generation, volumetric flow mesh generation, and finally, computational simulation. These steps are performed separately, often using separate pieces of software, and each step requires considerable expertise and investment of time on the part of the user. In this paper, an alternative framework is presented in which the entire image-based modeling process is performed on a Cartesian domain where the image is embedded within the domain as an implicit surface. Thus, the framework circumvents the need for generating surface meshes to fit complex geometries and subsequent creation of body-fitted flow meshes. Cartesian mesh pruning, local mesh refinement, and massive parallelization provide computational efficiency; the image-to-computation techniques adopted are chosen to be suitable for distributed memory architectures. The complete framework is demonstrated with flow calculations computed in two 3D image reconstructions of geometrically dissimilar intracranial aneurysms. The flow calculations are performed on multiprocessor computer architectures and are compared against calculations performed with a standard multistep route.