Computer-Assisted Three-Dimensional Morphology Evaluation of Intracranial Aneurysms

Hamidreza Rajabzadeh-Oghaz; Nicole Varble; Hussain Shallwani; Vincent M Tutino; Ashkan Mowla; Hakeem J Shakir; Kunal Vakharia; Gursant S Atwal; Adnan H Siddiqui; Jason M Davies; Hui Meng

doi:10.1016/j.wneu.2018.07.208

Computer-Assisted Three-Dimensional Morphology Evaluation of Intracranial Aneurysms

World Neurosurg. 2018 Nov:119:e541-e550. doi: 10.1016/j.wneu.2018.07.208. Epub 2018 Aug 1.

Affiliations

¹ Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA; Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, New York, USA.
² Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Gates Vascular Institute, Kaleida Health, Buffalo, New York, USA.
³ Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA; Department of Biomedical Engineering, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Gates Vascular Institute, Kaleida Health, Buffalo, New York, USA.
⁴ Stroke Division, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA.
⁵ Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Radiology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Gates Vascular Institute, Kaleida Health, Buffalo, New York, USA; Jacobs Institute, Buffalo, New York, USA.
⁶ Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Gates Vascular Institute, Kaleida Health, Buffalo, New York, USA; Jacobs Institute, Buffalo, New York, USA.
⁷ Canon Stroke and Vascular Research Center, University at Buffalo, Buffalo, New York, USA; Department of Mechanical and Aerospace Engineering, University at Buffalo, Buffalo, New York, USA; Department of Biomedical Engineering, University at Buffalo, Buffalo, New York, USA; Department of Neurosurgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, New York, USA. Electronic address: huimeng@buffalo.edu.

Abstract

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.

Keywords: Aneurysm shape; Computer-assisted 3-dimensional; Intracranial aneurysm; Morphology; Size measurement.

MeSH terms

Angiography, Digital Subtraction / methods*
Cerebral Angiography / methods*
Humans
Imaging, Three-Dimensional / methods*
Intracranial Aneurysm / diagnostic imaging*
Observer Variation
Pattern Recognition, Automated / methods
Radiographic Image Interpretation, Computer-Assisted / methods*

Computer-Assisted Three-Dimensional Morphology Evaluation of Intracranial Aneurysms

Authors

Affiliations

Abstract

MeSH terms

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