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.
Hypothesis: To characterize anatomical measurements and shape variation of the facial nerve within the temporal bone, and to create statistical shape models (SSMs) to enhance knowledge of temporal bone anatomy and aid in automated segmentation. Background: The facial nerve is a fundamental structure in otologic surgery, and detailed anatomic knowledge with surgical experience are needed to avoid its iatrogenic injury. Trainees can use simulators to practice surgical techniques, however manual segmentation required to develop simulations can be time consuming. Consequently, automated segmentation algorithms have been developed that use atlas registration, SSMs, and deep learning. Methods: Forty cadaveric temporal bones were evaluated using three dimensional microCT (μCT) scans. The image sets were aligned using rigid fiducial registration, and the facial nerve canals were segmented and analyzed. Detailed measurements were performed along the various sections of the nerve. Shape variability was then studied using two SSMs: one involving principal component analysis (PCA) and a second using the Statismo framework. Results: Measurements of the nerve canal revealed mean diameters and lengths of the labyrinthine, tympanic, and mastoid segments. The landmark PCA analysis demonstrated significant shape variation along one mode at the distal tympanic segment, and along three modes at the distal mastoid segment. The Statismo shape model was consistent with this analysis, emphasizing the variability at the mastoid segment. The models were made publicly available to aid in future research and foster collaborative work. Conclusion: The facial nerve exhibited statistical variation within the temporal bone. The models used form a framework for automated facial nerve segmentation and simulation for trainees.
Summary The incudostapedial joint (ISJ) of the middle ear is important for proper transmission of sound energy to the cochlea. Recently, the biomechanics of the ISJ have been investigated using finite‐element (FE) modelling, using simplified geometry. The objective of the present study was to investigate the feasibility of synchrotron‐radiation phase‐contrast imaging (SR‐PCI) in visualising the ISJ ultrastructure. Three human cadaveric ISJs were dissected and scanned using SR‐PCI at 0.9 µm isotropic voxel size. One of the samples was previously scanned at 9 µm voxel size. The images were visually compared and contrast‐to‐noise ratios (CNRs) were calculated (of both bone and soft tissues) for quantitative comparisons. The ISJ ultrastructure as well as adjacent bone and soft tissues were clearly visible in images with a 0.9 µm voxel size. The CNRs of the 0.9 µm images were relatively lower than those of the 9 µm scans, while the ratio of bone to soft tissue CNRs were higher, indicating better discernibility of bone from soft tissue in the 0.9 µm scans. This study was the first known attempt to image the ISJ ultrastructure using an SR‐PCI scanner at submicron voxel size and results suggest that this method was successful. Future studies are needed to optimise the contrast and test the feasibility of imaging the ISJ in situ. Lay Description The human middle ear consists of the eardrum, three small bones (the malleus, incus and stapes) and two joints connecting the bones (the incudostapedial joint and the incudomallear joint). The role of the middle ear is to amplify and transfer sound energy to the cochlea, the end organ of hearing. The incudostapedial joint (ISJ) of the middle ear is a synovial joint which is important for proper transmission of sound energy to the cochlea. Similar to other synovial joints it consists of meniscus, fluid and articulating surfaces. Recently, the biomechanics of the ISJ have been investigated using computational models, using grossly simplified geometry. Synchrotron radiation phase contrast imaging (SR‐PCI) is a high‐resolution imaging technique used to visualise small structures in three dimensions. The objective of the present study was to investigate the feasibility of using SR‐PCI in visualising the ISJ ultrastructure. Three human cadaveric ISJs were dissected and scanned using SR‐PCI at 0.9 µm isotropic voxel size. One of the samples was previously scanned at 9 µm voxel size. The images were both qualitatively and quantitatively compared. This study was the first known attempt to image the ISJ ultrastructure using an SR‐PCI scanner at submicron voxel size and results suggest that this method was successful. Future studies are needed to optimise the contrast and feasibility of imaging the ISJ in situ.
To develop an accurate, automated multi-atlas segmentation algorithm for creating three-dimensional sigmoid sinus models from clinical computed tomography (CT) volumes for use in temporal bone mastoidectomy surgical simulation software.Methods: Clinical CT and micro-CT scans of 38 cadaveric temporal bones were used to develop and validate the algorithm. A single-atlas and multi-atlas segmentation were compared for accuracy using three different label fusion methods: majority voting, STAPLE, and joint label fusion. The automated segmentation algorithm was evaluated by comparing to ground truth manual segmentations through a combination of visual inspection and Dice, Hausdorff distance, and average Hausdorff distance metrics. Results:The best results were obtained for multi-atlas segmentation using joint label fusion for which a mean Dice value of 0.77 was found across all samples when compared to the manual segmentations. The mean Hausdorff distance was 10.39 mm, and the mean average Hausdorff distance was 0.30 mm, corresponding to less than two voxels. Visual inspection revealed accurate and high-resolution segmentations. Conclusion:The presented multi-atlas method is effective and accurate at automatically producing high-resolution segmentations of the sigmoid sinus for the purpose of surgical simulation.
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