Dataset Information

Computer-assisted extraction of intracranial aneurysms on 3D rotational angiograms for computational fluid dynamics modeling.

ABSTRACT: Three-dimensional rotational angiography (3DRA) is an evolving imaging procedure from traditional digital subtraction angiography and is gaining much interest for detecting intracranial aneurysms. Computational fluid dynamics (CFD) modeling plays an important role in understanding the biomechanical properties and in facilitating the prediction of aneurysm rupture. A successful computational study relies on an accurate description of the vascular geometry that is obtained from volumetric images.The authors propose a new aneurysm segmentation algorithm to facilitate the study of CFD. This software combines a region-growing segmentation method with the 3D extension of a deformable contour based on a charged fluid model. A charged fluid model essentially consists of a set of charged elements that are governed by the nature of electrostatics. The approach requires no prior knowledge of anatomic structures and automatically segments the vasculature after the end-user selects a vessel section in a plane image.Experimental results on 15 cases indicate that aneurysm structures were effectively segmented and in good agreement with manual delineation outcomes. In comparison with the existing methods, the algorithm provided a much higher overlap index with respect to the ground truth. Furthermore, the outcomes of the proposed approach achieved a clean representation of vascular structures that is advantageous for hemodynamics analyses.A new aneurysm segmentation framework in an attempt to automatically segment vascular structures in 3DRA image volumes has been developed. The proposed algorithm demonstrated promising performance and unique characteristics to adequately segment aneurysms in 3DRA image volumes for further study in computational fluid dynamics.

SUBMITTER: Chang HH

PROVIDER: S-EPMC2789114 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature

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Computer-assisted extraction of intracranial aneurysms on 3D rotational angiograms for computational fluid dynamics modeling.

Chang Herng-Hua HH Duckwiler Gary R GR Valentine Daniel J DJ Chu Woei Chyn WC

Medical physics 20091201 12

<h4>Purpose</h4>Three-dimensional rotational angiography (3DRA) is an evolving imaging procedure from traditional digital subtraction angiography and is gaining much interest for detecting intracranial aneurysms. Computational fluid dynamics (CFD) modeling plays an important role in understanding the biomechanical properties and in facilitating the prediction of aneurysm rupture. A successful computational study relies on an accurate description of the vascular geometry that is obtained from vol ...[more]

PMID: 20095274

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Project description:OBJECTIVE:Precise morphologic evaluation is important for intracranial aneurysm (IA) management. At present, clinicians manually measure the IA size and neck diameter on 2-dimensional (2D) digital subtraction angiographic (DSA) images and categorize the IA shape as regular or irregular on 3-dimensional (3D)-DSA images, which could result in inconsistency and bias. We investigated whether a computer-assisted 3D analytical approach could improve IA morphology assessment. METHODS:Five neurointerventionists evaluated the size, neck diameter, and shape of 39 IAs using current and computer-assisted 3D approaches. In the computer-assisted 3D approach, the size, neck diameter, and undulation index (UI, a shape irregularity metric) were extracted using semiautomated reconstruction of aneurysm geometry using 3D-DSA, followed by IA neck identification and computerized geometry assessment. RESULTS:The size and neck diameter measured using the manual 2D approach were smaller than computer-assisted 3D measurements by 2.01 mm (P < 0.001) and 1.85 mm (P < 0.001), respectively. Applying the definitions of small IAs (<7 mm) and narrow-necked IAs (<4 mm) from the reported data, interrater variation in manual 2D measurements resulted in inconsistent classification of the size of 14 IAs and the necks of 19 IAs. Visual inspection resulted in an inconsistent shape classification for 23 IAs among the raters. Greater consistency was achieved using the computer-assisted 3D approach for size (intraclass correlation coefficient [ICC], 1.00), neck measurements (ICC, 0.96), and shape quantification (UI; ICC, 0.94). CONCLUSIONS:Computer-assisted 3D morphology analysis can improve accuracy and consistency in measurements compared with manual 2D measurements. It can also more reliably quantify shape irregularity using the UI. Future application of computer-assisted analysis tools could help clinicians standardize morphology evaluations, leading to more consistent IA evaluations.

Dataset Information

Computer-assisted extraction of intracranial aneurysms on 3D rotational angiograms for computational fluid dynamics modeling.

Publications

Computer-assisted extraction of intracranial aneurysms on 3D rotational angiograms for computational fluid dynamics modeling.

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