Fig 1.
Schematic of the FLIO instrumentation and the instrument response functions.
(A) shows a schematic of the FLIO instrumentation. A 473 nm pulse laser is fed into a scanning laser ophthalmoscope to excite the autofluorescence of the eye. The fluorescence emission is transmitted by a multimode fiber to a dichroic mirror (DM), which divides the fluorescence signal into two spectral channels: 498–560 nm and 560–720 nm. Hybrid photomultiplier tube detectors convert the fluorescence photons into electrical pulses, which are processed by a TCSPC device for each detector. A continuous wave (CW) infrared laser (IR) illuminates the fundus for online image registration. Blocking filters (BF) protect the detectors from excitation and infrared light. The FLIO instrument response functions are given in (B).
Fig 2.
Comparison of static and adaptive binning.
TCSPC data obtained from a 39 year old diabetes patient is evaluated. The top row of the photon histograms displays the raw data (C, E), the middle row shows the photon histograms after static binning (F, H) and the bottom row is after adaptive binning (J, L). In the magnified insets (A, B), static binning uses all of the pixels inside the gray box, while adaptive binning uses the white pixels (note the different sizes of the insets). The small black square indicates the seed pixel for the binning. Static binning uses more neighboring pixels for each bright pixel than adaptive binning (right inset, B), while adaptive binning uses more neighboring pixels for each dark pixel than static binning (left inset, A). The left column (C, F, J) shows photon histograms for one pixel (black square in A) in a dark region of the intensity image, while the right column (E, H, L) shows photon histograms for one pixel in a bright region. The middle column displays the fluorescence intensity image (170 x 170 pixels, 59 x59 μm2/pixel) of the raw data (D), after static binning (G) and after adaptive binning (K). The greater loss of spatial contrast with adaptive binning yields a more balanced SNR in the photon histograms of the underlying binned pixels. The data in the photon histograms were analyzed using a multi-exponential model (Eq 3) with three exponential functions in combination with incomplete decay (Eq 9). The resulting curve (red) and the corresponding fluorescence lifetimes, the average fluorescence lifetime, the figure of merit χ2 (Eqs 10 and 11) as well as the total number of photons are shown next to the photon histogram.
Fig 3.
Example of TCSPC data approximated by the multi-exponential model and the lens-corrected approach.
TCSPC data obtained from a healthy volunteer is evaluated using adaptive binning (black) and approximated by the multi-exponential approach (left) using three exponential functions and the lens-corrected approach (right) using two exponential function and a separate measurement of the crystalline lens. The data are divided into three intervals: the pre-excitation interval (A), the fluorescence rising edge (B) and the fluorescence decay (C). The measured data and the multi-exponential model diverge due to the fluorescence of the crystalline lens in interval (B), which is magnified in the inset. The lens-corrected approach utilizes a scaled and shifted curve of a separate measurement of the crystalline lens to correct for the influence of the crystalline lens fluorescence in interval (B). For better visibility, the fluorescence intensity is plotted on a logarithmic scale. The fluorescence lifetimes of the exponential components (τ1, τ2, τ3), the average fluorescence lifetime τm as well figure of merit χ2 (Eqs 10 and 11) are shown next to the photon histogram.
Fig 4.
Reflection artifacts in TCSPC data after binning all of the pixels of an image.
TCSPC data after binning all of the pixels of an image from a healthy volunteer (black). The automatically detected artifacts caused by reflections in the optical pathway are divided into the rise of the artifact (dark red) and the decay of the artifact (light red). The latter is approximated as three times the width of the rise. For better visibility, the fluorescence intensity is plotted on a logarithmic scale. The insets show the magnified segment of the TCSPC data taken from a single pixel (gray). The results of a multi-exponential analysis (Eq 3) using three exponential functions are shown as orange (without removal of the reflection artifacts) and blue curves (with removal of the reflection artifacts).
Fig 5.
Example of the iterative algorithm for the treatment of outliers.
Comparison of a 75 x 75 pixels section (59 x59 μm2/pixel) of fluorescence lifetime τ1 from the left eye of a healthy volunteer before (left) and after (right) correction for outliers. The color scaling of both fluorescence lifetime plots is identical. The 259 detected outliers are colored black in the middle subplot, of which 178 could be improved to generate the corrected image. To provide better orientation, a gray scale image of the fluorescence intensity has been added as an overlay to all three subplots. The macula is in the lower right corner of the image, where the shortest fluorescence lifetimes (red) occur.
Fig 6.
Profile of the CPU and memory requirements for FLIMX.
(A) shows the fractional CPU core activity for a quad core processor and MATLAB’s total memory consumption (red) during a distributed fluorescence lifetime approximation of a measurement. (B) shows a breakdown of the CPU time spent on the different stages of the algorithm (C).
Fig 7.
Comparison of static and adaptive binning in a diabetes mellitus patient without diabetic retinopathy.
The images (149 x 169 pixels, 59 x59 μm2/pixel) of the fluorescence intensity, the fluorescence lifetimes τm, τ1, and τ2 are shown in the rows from top to bottom. The columns are static binning and adaptive binning for both spectral channels respectively. The fluorescence lifetimes were determined using the lens-corrected approach (Eq 8), with two exponential functions, ß set to 1 and a separate crystalline lens measurement. The color scaling is identical for the fluorescence intensity and the fluorescence lifetimes in each spectral channel for better comparison. The ETRS grid is drawn on each subplot for orientation. The low amount of detected fluorescence photons in the lower left part of the image causes a prolongation of especially fluorescence lifetimes τ2 in both spectral channels as well as the average fluorescence lifetime τm in spectral channel 2 in case of static binning. The largest differences are highlighted by white arrows.
Fig 8.
Comparison of the different fluorescent lifetime modelling approaches in a healthy volunteer.
The fluorescence intensity as well as the fluorescence lifetimes τm (A) and τ1 (B) in spectral channel 1 are shown (154 x 154 pixels, 59 x59 μm2/pixel). The color scaling of the fluorescence lifetime plots is identical in each subfigure. The ETDRS grid is drawn on each subplot, and the mean values are given for each subfield of the grid. The fluorescence intensity image is the measured signal before binning.
Fig 9.
Overview of FLIMX’s visualization capabilities and comparison of static binning + multi-exponential model and adaptive binning + lens-corrected approach for a patient with macular hole.
(A) shows the infrared image of the fundus (256 x 256 pixels). The FLIO data are analyzed using a multi-exponential model with three exponential functions based on static binning (factor two) and by the lens-corrected approach with two exponential functions and a separate crystalline lens measurement based on adaptive binning (threshold 100,000 photons per pixel). A vertical cross-section through the fovea centralis at pixel 128 on the x axis is highlighted as a black line. (B) and (D) show the remaining fundus section (pixels 128 to 256 on the x axis, all y pixels) in a three-dimensional view of the average fluorescence lifetime τm in spectral channel 1 for static binning + multi-exponential model and adaptive binning + lens-corrected approach respectively. The average fluorescence lifetimes τm along the cross-section are shown in detail in (C) and (E). The average fluorescence lifetimes are shorter in (D) and (E) because of the eliminated influence of the crystalline lens.
Fig 10.
Results of the Holm-Bonferroni method applied to FLIO measurements to allow for group comparisons between diabetes patients and controls.
The normalized histograms of the fluorescence amplitudes α and lifetimes τ in both spectral channels are obtained from a multi-exponential approximation using three exponential functions, for controls (blue) and diabetes patients (red). Histogram classes with significant differences, according to the Holm-Bonferroni method, are colored in light gray. The class with the highest significance level (the smallest p value) is indicated in dark gray. Only the fluorescence lifetimes showed significant differences, except for τ2 in spectral channel 2. For the class with the highest significance level, the corresponding receiver operating characteristic curve (orange) is shown next to the histogram. The cut-off point as best trade-offs between true positive rate and false positive rate is colored in light blue. The AUC is given under the ROC curve.
Fig 11.
Fluorescence intensity and average fluorescence lifetime of the ganglion cell layer in a porcine retina ex vivo sample.
The 256 x 256 pixels images (34 x34 μm2/pixel) of the fluorescence intensity before binning (left) and the average fluorescence lifetime τm (right) of the ganglion cell layer in a porcine retina sample are shown in two spectral channels (top: 500–560 nm; bottom: 560–700 nm). Adaptive binning with a threshold of 10,000 photons per pixel was applied. A multi-exponential model with two exponential functions was used to determine the fluorescence lifetimes. The length of the white bar is 20 μm.