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Eosinophilic Esophagitis (EoE) is an inflammatory disease caused by inhaled or ingested food allergies, and characterized by the infiltration of eosinophils in the esophagus. The gold standard for diagnosing EoE is to conduct endoscopy and obtain multiple biopsy specimens from different portions of the esophagus; an exam is considered positive if more than 15 eosinophils per high power field (HPF) in any of the biopsies. This method of diagnosis is problematic because endoscopic biopsy is expensive and poorly tolerated and the esophageal eosinophil burden needs to be monitored frequently during the course of the disease. Spectrally encoded confocal microscopy (SECM) is a high-speed confocal microscopy technology that can visualize individual eosinophils in large microscopic images of the human esophagus, equivalent to more than 30,000 HPF. Previously, we have demonstrated that tethered capsule SECM can be conducted in unsedated subjects with diagnosed EoE. However, speckle noise and the relatively low resolution in images obtained with the first capsule prototypes made it challenging to distinguish eosinophils from other cells. In this work, we present a next-generation tethered SECM capsule, which has been modified to significantly improve image quality. First, we substituted the single mode fiber with a dual-clad fiber to reduce speckle noise. A gradient-index multimode fiber was fusion spliced at the tip of the dual-clad fiber to increase the effective numerical aperture of the fiber from 0.09 to 0.15, expanding the beam more rapidly to increase the illumination aperture at the objective. These modifications enabled the new SECM capsule to achieve a lateral resolution of 1.8 µm and an axial resolution of 16.1 µm, which substantially improves the capacity of this probe to visualize cellular features in human tissue. The total size of the SECM capsule remained 6.75 mm in diameter and 31 mm in length. We are now in the process of testing this new SECM capsule in humans. Early results using this new SECM capsule suggest that this technology has the potential to be an effective tool for the diagnosis of EoE.
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