• Melissa J Suter, Joseph M Reinhardt, and Geoffrey McLennan.
• Harvard Medical School and Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA, USA.
• Acad Radiol. 2008 Jun 1; 15 (6): 786-98.

Rationale And ObjectivesMany imaging modalities and methodologies exist for evaluating the pulmonary airways. Individually, each modality provides insight to the state of the airways; however, alone they do not necessarily provide a comprehensive description. The goal of this work is to integrate complementary medical imaging datasets to form a synergistic description of the airways.Materials And MethodsTwo digital bronchoscopic techniques were used to evaluate the pulmonary mucosa. A digital color bronchoscopic system was used to detect mucosal color alterations, and a fluorescence detection system was used to assess the microvasculature of the bronchial mucosa. Study participants were also imaged with a multidetector row computed tomographic (MDCT) scanner. Virtual bronchoscopic and image registration techniques were exploited to combine three-dimensional surface renderings, extracted from the MDCT data, together with the two-dimensional digital bronchoscopic images. Validation of the fusion process was performed on a rubber phantom of an adult airway with 4 embedded metal beads.ResultsThe fusion of the MDCT extracted airway tree and the digital bronchoscopic datasets were presented for three study participants. In addition, the detected accuracy of the registration method to reliably align the MDCT and bronchoscopic image datasets was determined to be 1.98 mm in the phantom airway model.ConclusionWe have demonstrated that merging of three distinct digital datasets to provide a single synergistic description of the airways is possible. This is a pilot project in the field of eidomics, the process of combining digital image datasets and image-based processes together. We anticipate that in the future eidomics will provide a universal and predictive imaging language that will change health care delivery.

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