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Quantification of Intracranial Aneurysm Morphodynamics from ECG-gated CT Angiography

Posted Dec 31, 2012 Updated Apr 10, 2023
By Azadeh Firouzian MSc , Rashindra Manniesing PhD , Coert T. Metz PhD , Roelof Risselada PhD , Stefan Klein PhD , Fop van Kooten PhD , Miriam C.J.M. Sturkenboom PhD , Aad van der Lugt PhD , Wiro J. Niessen PhD
10 min read

Rationale and Objectives

Aneurysm morphodynamics is potentially relevant for assessing aneurysm rupture risk. A method is proposed for automated quantification and visualization of intracranial aneurysm morphodynamics from electrocardiogram (ECG)-gated computed tomography angiography (CTA) data.

Materials and Methods

A prospective study was performed in 19 aneurysms from 14 patients with diagnostic workup for recently discovered aneurysms ( n = 15) or follow-up of untreated known aneurysms ( n = 4). The study was approved by the Institutional Review Board of the hospital and written informed consent was obtained from each patient. An image postprocessing method was developed for quantifying aneurysm volume changes and visualizing local displacement of the aneurysmal wall over a heart cycle using multiphase ECG-gated (four-dimensional) CTA. Percentage volume changes over the heart cycle were determined for aneurysms, surrounding arteries, and the skull.

Results

Pulsation of the aneurysm and its surrounding vasculature during the heart cycle could be assessed from ECG-gated CTA data. The percentage aneurysmal volume change ranged from 3% to 18%.

Conclusion

ECG-gated CTA can be used to study morphodynamics of intracranial aneurysms. The proposed image analysis method is capable of quantifying the volume changes and visualizing local displacement of the vascular structures over the cardiac cycle.

Subarachnoid hemorrhage (SAH) caused by rupture of an intracranial aneurysm is a devastating event associated with a high mortality and morbidity rate . Risk factors such as smoking, hypertension and alcohol consumption, and aneurysm characteristics including size, location, and shape, are known to be important indicators of aneurysm rupture risk . Associations have been found between these risk factors and the static state of the aneurysm. Aneurysmal wall motion may also be considered a risk factor and may provide additional predictive information on rupture risk because it is related to the mechanical properties of the aneurysmal wall. Several modalities have been used to study aneurysmal wall motion, such as cine phase-contrast magnetic resonance angiography (MRA) , transcranial Doppler ultrasound , time-resolved rotational angiography , and electrocardiogram (ECG)-gated computed tomography angiography (CTA) . Of these, ECG-gated CTA is a suitable technique to evaluate aneurysm morphodynamics because it provides high spatial and temporal resolution within a short scanning time. In addition, CTA is used as a routine modality for diagnosis of vascular diseases . Most of these studies focused on aneurysm visualization, except from a few studies in which aneurysm morphodynamics was quantified manually . However, the small size of intracranial aneurysms, their limited volume change over the cardiac cycle, and the limited signal-to-noise ratio (SNR) in four-dimensional (4D) CTA, make manual assessment of aneurysmal dynamic behavior a challenging task. Therefore, this study presents an automated image processing method to assess intracranial aneurysm morphodynamics from ECG-gated CTA data to facilitate visualization and quantification of aneurysmal wall motion.

Materials and methods

Patients

Patients aged ≥18 years were recruited via the outpatient clinic of the department of neurology. With the approval of the Institutional Review Board (IRB), an additional ECG-gated CTA of the intracranial circulation was performed for the purpose of this study and all patients gave written informed consent. In total 14 patients (5 men and 9 women, age range 47–70 years) with 19 unruptured aneurysms were included; they were scanned as a diagnostic workup for a recently discovered unruptured aneurysm or follow-up of a known untreated aneurysm between February 2008 and November 2009.

Scan Protocol

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Image Processing Method

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Registration method

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Figure 1, Top row: a schematic drawing of the four-dimensional (4D) registration method. Dashed lines show how voxels are displaced to be aligned with the corresponding voxels in other phases. Bottom row: an example of the deformation vector field, time-averaged image, and segmentation from the same dataset. 3D, three-dimensional.

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Segmentation method

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Volume quantification

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Experimental Details

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Results

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Table 1

Summary of Patient Information and Aneurysm Morphology and Morphodynamics in 19 Aneurysms and Neighboring Arteries from 14 Patient

Patient Aneurysm Artery No. Sex Age No. Location 1 Size (mm) V min (mm 3 ) %V change d max –d min (mm) %V change d max –d min (mm) 1 F 50 1 Basilar tip 3 33 3.1 0.040 3.5 0.044 2 M 70 2 PCoA L 9 139 8.0 0.168 15.1 0.162 2 M 70 3 PCoA R 7 56 5.4 0.084 13.1 0.175 3 F 55 4 PCoA R 5 166 6.0 0.135 14.3 0.200 3 F 55 5 ACoA L 2 38 10.7 0.143 6.2 0.082 4 F 60 6 Ophthalmic A L 5 149 4.9 0.105 2.0 0.024 5 F 63 7 MCA R 8 179 6.8 0.155 10.3 0.010 5 F 53 8 MCA L 4 53 8.6 0.131 6.9 0.090 6 M 47 9 Basilar tip 19 476 7.0 0.221 8.5 0.100 7 F 67 10 MCA L 8 235 17.7 0.428 2.4 0.026 8 M 68 11 Basilar tip 10 867 4.5 0.175 5.5 0.094 8 M 68 12 MCA L 7 387 10.2 0.300 5.4 0.053 9 F 52 13 MCA R 10 151 11.6 0.247 18.4 0.175 10 F 57 14 Ophthalmic A L 8 182 14.8 0.331 8.1 0.100 11 M 61 15 MCA L 8 106 10.0 0.190 8.5 0.118 12 F 57 16 Ophthalmic A L 12 1033 3.7 0.154 3.5 0.042 13 M 70 17 Basilar tip 8 522 2.7 0.100 10.9 0.200 14 F 54 18 MCA L 8 363 3.2 0.100 2.7 0.033 14 F 54 19 Pericallosal A 4 42 3.4 0.050 2.6 0.025

ACoA, anterior communicating artery; L, left; MCA, middle cerebral artery; PCoA, posterior communicating artery; R, right.

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Figure 2, Aneurysm volume as function of cardiac cycle obtained with the automated image processing method. Each graph shows the relative volume change with respect to the average volume for aneurysm and its surrounding vessel as a function of cardiac phase. The aneurysms in the top row have the same location but difference sizes: (a) middle cerebral artery (MCA, 10 mm), (b) middle cerebral artery (8 mm), and the bottom pair have the same size but different locations: (c) basilar tip (8 mm) and (d) middle cerebral artery (8 mm).

Figure 3, Percentage volume change in 19 aneurysms as a function of their minimum volume in the cardiac cycle ( left ). The aneurysms with the same location are represented with the same symbol. On the right, there is a schematic drawing of the circle of Willis indicating the aneurysm locations. ACoA, anterior communicating artery; MCA, middle cerebral artery; PCoA, posterior communicating artery.

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Figure 4, Maximum local displacement of the aneurysmal wall in millimeters with respect to the phase of the heart cycle with minimum volume, presented by colors on the three-dimensional volume rendered segmentation of the aneurysm domes. The domes are arranged in rows (from left to right the diameter decreases) and the locations are mentioned next to each row. All the aneurysms are oriented such that their neck is downwards and their head upwards. The color scale is shown in the bottom part of the figure (0.08–0.32 mm). The values below 0.08 are shown in dark blue and above 0.32 in red in order to increase the color resolution of the figure. ACoA, anterior communicating artery; MCA, middle cerebral artery; PCoA, posterior communicating artery.

Figure 5, Four time frames (25%, 50%, 75%, and 100% of the heart cycle) of three aneurysms with similar size (≈8 mm) but different locations are presented. The color code represents maximum local displacement of the dome with respect to the phase with minimal aneurysm volume. MCA, middle cerebral artery.

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Statistical Analysis

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Discussion

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Supplementary data

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Movie 1

Movie 2

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Academic Radiology, Volume 20, Issue 1
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