Datasets for Low-Cost High Sensitivity Pulsed Endomicroscopy to Visualize Tri-Color Optical Signatures

  • Beth Mills (Creator)
  • Ian Murray (Creator)
  • Adam Marshall (Creator)
  • Tom Craven (Creator)
  • Philip Emanuel (Creator)
  • Tushar Choudhary (Creator)
  • Dominic Norberg (Creator)
  • Emma Scholefield (Creator)
  • Ahsan Akram (Creator)
  • Andrew Davie (Creator)
  • Nikhil Hirani (Creator)
  • Annya Bruce (Creator)
  • Anne Moore (Creator)
  • Mark Bradley (Creator)
  • Kevin Dhaliwal (Creator)
  • Nikola Krstajic (Creator)

Dataset

Abstract

A highly sensitive, modular three-color fluorescence endomicroscopy imaging platform spanning the visible to near-infrared (NIR) range is demonstrated. Light-emitting diodes (LEDs) were sequentially pulsed along with the camera acquisition to provide up to 20 frames per second (fps) three-color imaging performance or 60 fps single color imaging. The system was characterized for bacterial and cellular molecular imaging in ex vivo human lung tissue and for bacterial and indocyanine green imaging in ex vivo perfused sheep lungs. A practical method to reduce background tissue autofluorescence is also proposed. The platform was clinically translated into six patients with pulmonary disease to delineate healthy, cancerous, and fibrotic tissue autofluorescent structures. The instrument is the most broadband clinical endomicroscopy system developed to date (covering visible to the NIR, 500 to 900 nm) and demonstrates significant potential for future clinical utility due to its low cost and modular capability to suit a wide variety of molecular imaging applications.

Data Citation

Mills, Bethany; Murray, Ian; Marshall, Adam; Craven, Thomas; Emanuel, Philip; Choudhary, Tushar; William, Gareth; Norberg, Dominic; Scholefield, Emma; Akram, Ahsan; Davie, Andrew; Hirani, Nik; Bruce, Annya; Moore, Anne; Bradley, Mark; Dhaliwal, Kevin; Krstajic, Nikola. (2018). Datasets for Low-Cost High Sensitivity Pulsed Endomicroscopy to Visualize Tri-Color Optical Signatures, [dataset]. School of Engineering. University of Edinburgh. http://dx.doi.org/10.7488/ds/2355.
Date made available1 Jul 2018
PublisherEdinburgh DataShare

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