Morphological analysis of sigmoid sinus anatomy: clinical applications to neurotological surgery

Abstract

ObjectivesThe primary objective of this study was to use high-resolution micro-CT images to create accurate three-dimensional (3D) models of several intratemporal structures, and to compare several surgically important dimensions within the temporal bone. The secondary objective was to create a statistical shape model (SSM) of a dominant and non-dominant sigmoid sinus (SS) to provide a template for automated segmentation algorithms.MethodsA free image processing software, 3D Slicer, was utilized to create three-dimensional reconstructions of the SS, jugular bulb (JB), facial nerve (FN), and external auditory canal (EAC) from micro-CT scans. The models were used to compare several clinically important dimensions between the dominant and non-dominant SS. Anatomic variability of the SS was also analyzed using SSMs generated using the Statismo software framework.ResultsThree-dimensional models from 38 temporal bones were generated and analyzed. Right dominance was observed in 74% of the paired SSs. All distances were significantly shorter on the dominant side (p < 0.05), including: EAC – SS (dominant: 13.7 ± 3.4 mm; non-dominant: 15.3 ± 2.7 mm), FN – SS (dominant: 7.2 ± 1.8 mm; non-dominant: 8.1 ± 2.3 mm), 2nd genu FN – superior tip of JB (dominant: 8.7 ± 2.2 mm; non-dominant: 11.2 ± 2.6 mm), horizontal distance between the superior tip of JB – descending FN (dominant: 9.5 ± 2.3 mm; non-dominant: 13.2 ± 3.5 mm), and horizontal distance between the FN at the stylomastoid foramen – JB (dominant: 5.4 ± 2.2 mm; non-dominant: 7.7 ± 2.1). Analysis of the SSMs indicated that SS morphology is most variable at its junction with the transverse sinus, and least variable at the JB.ConclusionsThis is the first known study to investigate the anatomical variation and relationships of the SS using high resolution scans, 3D models and statistical shape analysis. This analysis seeks to guide neurotological surgical approaches and provide a template for automated segmentation and surgical simulation.

Fully automated preoperative segmentation of temporal bone structures from clinical CT scans

“…Such inclusion could reduce the independence of these sets. However, in most cases there is considerable anatomic variability between different ears of a given subject [31][32][33] . We therefore expect that this possibility will do little to reduce the robustness of the model.…”

Micro‐CT of the human ossicular chain: Statistical shape modeling and implications for otologic surgery

“…Statistical shape modeling is a technique for morphological study which uses a database of 3D model samples to calculate an average geometry and analyze how this geometry varies across samples (Cootes, Taylor, Cooper, & Graham, 1995). Statistical shape modeling has been used in the study of many anatomical entities, for example, the nasal cavity (Keustermans et al, 2018), the sigmoid sinus (Van Osch et al, 2019), and the facial nerve (Hudson, Gare, Allen, Ladak, & Agrawal, 2020). Not only are statistical shape models (SSMs) useful for direct study of structure but they also serve as a basis for creating automated segmentation algorithms, the development of which would replace exhaustive manual image segmentation (subdividing the region of interest from surrounding anatomy).…”

Mastoid notch as a landmark for localization of the transverse-sigmoid sinus junction

“…Over the past decades, neurosurgeons have been using innovative techniques to safely expose the transverse and sigmoid sinuses without resulting in extensive bony defects during craniotomy. The accurate placement of the keyhole at the transverse-sigmoid sinus junction is one of the most important steps in this procedure [11][12][13][14]. Goto T used the lateral end of the transverse sinus, the ASTS, the mastoid emissary foramen and the midpoint of the transverse sinus as the site of 4 key holes to complete the exposure of the sigmoid sinus [2].…”

“…Image-guided surgical planning, including neuronavigation and other methods based on the 3D volume rendering (3D VR) technique, can yield morphometric data in individual patients and can overcome extreme individual variations [7,11,25,26]. For instance, Xia utilized a line connecting the digastric point and the asterion to establish a coordinates systemon for 3D VR images; then he analyzed the coordinate relationship between the asterion and the IMTS to assist with localizing the IMTS [22].…”

“…The transverse-sigmoid sinus junction was defined as the transitional zone where the transverse sinus ended by the vertical descending segment of the sigmoid sinus; the ASTS and IMTS were identified as the anterosuperior and inferomedial points of the junction, respectively [8,11] (Fig. 1).…”