Real-time automated thickness measurement of the in vivo human tympanic membrane using optical coherence tomography.

Background: Otitis media (OM), an infection in the middle ear, is extremely common in the pediatric population. Current gold-standard methods for diagnosis include otoscopy for visualizing the surface features of the tympanic membrane (TM) and making qualitative assessments to determine middle ear content. OM typically presents as an acute infection, but can progress to chronic OM, and after numerous infections and antibiotic treatments over the course of many months, this disease is often treated by surgically inserting small tubes in the TM to relieve pressure, enable drainage, and provide aeration to the middle ear. Diagnosis and monitoring of OM is critical for successful management, but remains largely qualitative.

Methods: We have developed an optical coherence tomography (OCT) system for high-resolution, depth-resolved, cross-sectional imaging of the TM and middle ear content, and for the quantitative assessment of in vivo TM thickness including the presence or absence of a middle ear biofilm. A novel algorithm was developed and demonstrated for automatic, real-time, and accurate measurement of TM thickness to aid in the diagnosis and monitoring of OM and other middle ear conditions. The segmentation algorithm applies a Hough transform to the OCT image data to determine the boundaries of the TM to calculate thickness.

Results: The use of OCT and this segmentation algorithm is demonstrated first on layered phantoms and then during real-time acquisition of in vivo OCT from humans. For the layered phantoms, measured thicknesses varied by approximately 5 µm over time in the presence of large axial and rotational motion. In vivo data also demonstrated differences in thicknesses both spatially on a single TM, and across normal, acute, and chronic OM cases.

Conclusions: Real-time segmentation and thickness measurements of image data from both healthy subjects and those with acute and chronic OM demonstrate the use of OCT and this algorithm as a robust, quantitative, and accurate method for use during real-time in vivo human imaging.