Abstract

AbstractComputed tomography is the technique of choice to study the malformations of the auricle, external auditory canal (EAC) and middle ear. The best image quality with the lowest radiation dose can be achieved when high-end Cone Beam CT scanners are used. The 125 μm spatial resolution images they provide are crucial in the detection of subtle ossicular and oval/round window malformations. Knowledge of the embryology helps to understand which malformations can be found and atresia and stenosis of the EAC are the most frequently found malformations of the outer ear. First Branchial Cleft Anomalies are rare and are best studied using MR. Middle ear malformations can develop in association with or in the absence of EAC deformities. Anomalies of the ossicles, facial nerve, oval window, round window, etc., can all be studied in detail with CT. However, MR is needed for the detection of congenital middle ear cholesteatomas and for cholesteatomas which are caused by congenital middle ear malformations and their resulting bad middle ear aeration. Inner ear malformations normally are not associated with middle and outer ear anomalies and high-resolution MR is the best adapted technique to detect vestibular, cochlear and cochleovestibular nerve malformations. New classifications of the labyrinthine malformations and VIIIth nerve malformations are used and their goal is to warn the surgeon for potential hazards during surgery and especially when cochlear implantation is considered. Finally the outer, middle and inner ear can be involved together in syndromes and therefore both MR and CT are often required in these patients. In this chapter the embryology and most frequent malformations of the outer, middle and inner ear will be discussed as well as the contemporary imaging techniques that should be used.KeywordsSemicircular CanalExternal Auditory CanalCochlear ImplantationBranchial ArchInternal Auditory CanalThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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