Authors: Joshua M. Herzog University of Michigan Mechanical Engineering jmherzog@umich.edu 0000-0001-9089-819X Angela Verkade University of Michigan Ophthalmology and Visual Sciences 0000-0002-7033-4853 Volker Sick University of Michgan Mechanical Engineering 0000-0001-5756-9714 Created: July 31, 2024 Introduction: Quantitative fluorescence imaging has been investigated for the development of low-cast, rapid, and non-invasive imaging of anterior segment disease. In particular, quantitative fluorescence imaging is being investigated to detect corneal infections, to quantify corneal aberrations and disease, and to quantify damage and chemical changes in the lens. Two of these aspects are explored in the following articles: Herzog, Joshua M., Verkade, Angela, and Sick, Volker. "Corneal shadowgraphy: a simple, low-cost, rapid, and quantitative tool with potential clinical utility." Manuscript submitted to Communications Medicine. 2024. Herzog, Joshua M., Verkade, Angela, and Sick, Volker. "Quantitative and rapid in vivo imaging of human lenticular fluorescence." Manuscript submitted to Investigative Ophthalmology and Visual Science. 2024. The data in this repository is the first set of quantitative fluorescence images used in these two approaches. Review Board Approval: Institutional Review Boards of the University of Michigan Medical School (IRBMED) Approval #HUM00210338 Approved on: November 30, 2023 Methodology: Fluorescence of human eyes was investigated in a population of 30 generally healthy adults. The study was approved (approval #HUM00210338) and overseen by the Institutional Review Boards of the University of Michigan Medical School (IRBMED). All activities were conducted exclusively on the University of Michigan's College of Engineering Campus in Ann Arbor, Michigan, and conformed to the ethical standards of IRBMED. Participants were recruited exclusively online using the University of Michigan's UMHealthResearch.org webportal, which is an IRBMED-approved electronic recruiting platform, and all participants provided comprehensive, written informed consent prior to participation. The study was open to adults age 18 to 65. Photosensitive individuals, individuals taking photosensitive medications, and individuals with acute eye disease were excluded from the study. Fluorescence images of the 60 human eyes (30 participants, 2 eyes per participant) were collected using a custom fluorescence imaging system. A near- UV LED (365 nm, 2 W peak power) is collimated and directed onto the eye with a peak irradiance of 30 mW/cm2 and for a duration of 50 ms. The UV source excites fluorescence from chemicals inside the eye, especially in the lens. The fluorescence emission is collected by an achromatic doublet lens and split between two image sensors. The "Blue" band captures emission of approximately 400 to 450 nm, and the "Red" band captures emission above 450 nm which typically extends to around 600 nm. A series of calibration images are collected to provide a quantitative reference, including images of a fluorescent glass window and background/black-reference images. A detailed radiometric analysis of the imaging setup is provided in: Herzog, J. M., & Sick, V. (2023). Design of a line-of-sight fluorescence-based imaging diagnostic for classification of microbe species. Measurement Science and Technology, 34(9), 095703. Data Description: Data deposited here includes 60 image sets (30 individual participants, and 2 eyes per individual) consisting of raw fluorescence images, diffuse reflection images using ambient lighting, images used for correction, and calibration, and metadata. Images are split into two wavelength bands as described in the methodology. Image sets are stored in Hierarchical Data Format 5 (HDF5) files with the following nodes: Red-band fluorescence image: /Raw/Fluorescence/Red Red-band fluorescence background image: /Raw/Fluorescence/RedRef Blue-band fluorescence image: /Raw/Fluorescence/Blue Blue-band fluorescence background image: /Raw/Fluorescence/BlueRef Red-band reference image: /Calibration/Profile/Red Red-band reference background image: /Calibration/Profile/RedRef Blue-band reference image: /Calibration/Profile/Blue Blue-band reference background image: /Calibration/Profile/BlueRef Red-band diffuse reflection images: /Raw/Diffuse/Red Blue-band diffuse reflection images: /Raw/Diffuse/Red Additionally, each image node contains a tag for frame rate, exposure duration, and timestamp. The approved informed consent document associated with this study is included in "InformedConsentForm.pdf". This form is exactly as approved by IRBMED and as signed by all participants. Summary statistics including demographic data, participant-reported diseases (e.g., diabetes, keratoconus), and pupil size are also stored in a Microsoft Excel spreadsheet file, and again as a text-file for easier access. Finally, 2-channel pseudocolor images combining the two fully-processed image bands are stored as portable network graphics (PNG) files. Image processing here includes background and flatfield correction, image registration, pupil detection, and 2x2 software binning. Image processing methods are described in detail in: Herzog, J. M., & Sick, V. (2024). Fluorescence imaging for the anterior segment of the eye. Frontiers in Photonics, 4, 1336541. The corrected intensity at pixel i of image j for patient k is calculated as I(i,j,k) = (I_raw(i,j,k) - ) / ( - ) * I_std where the angle brackets <> denote an average and the subscripts "raw", "bg", "ref", and "ref,bg" refer to the raw, background, reference, and reference background images, respectively. In the above notation, the average is taken over the missing subscript. The subscript "std" refers to a fluorescence standard, which here is taken to be equal to I_std = - or rather, the reference image set for patient #1 is used. Note that the standard is a number (rather than an array or image) as it is averaged over multiple pixels as well. In this case, the average is taken over the center 50x50 square.