Aneurysm growth evaluation and detection: a computer-assisted follow-up MRA analysis

Abstract

Growing intracranial aneurysms pose a high risk of rupture, making the detection and quantification of the growth crucial for timely treatment strategy adoption. In this paper we propose a computer-assisted approach based on the extraction of IA shapes from associated baseline and follow-up angiographic scans and non-rigid morphing of the two shapes. From the obtained shape deformations we computed four novel features, including differential volume (dV), surface area (dSA), aneurysm-size normalized median deformation path length (dMPL), and integral of cumulative deformation distances (dICDD). An experienced neuroradiologist manually extracted the IA shape models from the baseline and follow-up MRAs and, by utilizing size change and visual assessments, classified each aneurysm into stable with morphology changes, stable or growing. We investigated the classification performance and found that three of the novel and one cross-sectional feature exhibited significantly different mean values (p-value $<0.05$ ; Tukey's HSD test) between the stable and growing IA groups, while the mean dICDD was significantly different between all the three groups. The cross-sectional features has sensitivity to growing IAs in range 0.05-0.86, while novel features had generally higher sensitivity in range 0.81-0.90, making them promising candidates as surrogate follow-up imaging-based biomarkers for IA growth detection. These findings may offer valuable information for clinical management of patients with IAs based on follow-up imaging.

Keywords: Change detection; Feature extraction; Follow-up monitoring; Intracranial aneurysm; Rupture risk assessment; Shape morphing.

Fig. 1

Baseline to follow-up aneurysm shape morphing in the AGED approach from left to right: the baseline shape (gray surface) transitions into the follow-up shape (red boundary). This figure demonstrates the examples of growing aneurysms, and one example each of stable and semi-stable aneurysms.

Fig. 2

Aneurysm size change feature distributions in form of violin plots, according to the three groups. Upper row: four novel longitudinal and bottom row four cross-sectional features. Horizontal lines indicate tentative separation thresholds (t) between the semi-stable/stable vs. growing aneurysm groups and associated sensitivity or true positive rate (TPR; value of 1 is optimal).

Fig. 3

Feature agreement using scatter (left) and Bland–Altman (right) plots between cross-sectional and longitudinal computational approach: graphs on the top are for dSA-cSA, and on the bottom for cV-dV assessment.

Fig. 4

(a) Cross-sectional approach: morphologic features computed independently on baseline and follow-up aneurysm shape and their relative change considered as an aneurysm size change biomarker. (b) The AGED approach utilizes rigid initial shape baseline to follow-up co-registration and their non-rigid shape morphing to compute novel aneurysm size change features.

Algorithm 1

Shape morphing based on iterative mesh to mesh deformation (IMTMD) algorithm.

Fig. 5

Choosing the matching intersection point $p_j$ (cyan star on the enlarged circle) from all intersections $P_{j,k}^{\mathit{II}}$ (blue stars on the follow-up shape).

Fig. 6

(a) Maximal aneurysm dome height (HMAX) and path length (PL) computation. (b) Cumulative density for stable (green) and growing (red) aneurysms. (c) Volume change and (d) surface area change computation is based on baseline shape’s and corresponding morphed faces.