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Modeling Photo-Bleaching Kinetics to Create High Resolution Maps of Rod Rhodopsin in the Human Retina.

Ehler M, Dobrosotskaya J, Cunningham D, Wong WT, Chew EY, Czaja W, Bonner RF - PLoS ONE (2015)

Bottom Line: Our approach is based on analyzing the brightening of detected lipofuscin autofluorescence within small pixel clusters in registered imaging sequences taken with a commercial 488nm confocal scanning laser ophthalmoscope (cSLO) over a 1 minute period.We modeled the kinetics of rhodopsin bleaching by applying variational optimization techniques from applied mathematics.The physical model and the numerical analysis with its implementation are outlined in detail.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Mathematics, University of Vienna, Vienna, Austria.

ABSTRACT
We introduce and describe a novel non-invasive in-vivo method for mapping local rod rhodopsin distribution in the human retina over a 30-degree field. Our approach is based on analyzing the brightening of detected lipofuscin autofluorescence within small pixel clusters in registered imaging sequences taken with a commercial 488nm confocal scanning laser ophthalmoscope (cSLO) over a 1 minute period. We modeled the kinetics of rhodopsin bleaching by applying variational optimization techniques from applied mathematics. The physical model and the numerical analysis with its implementation are outlined in detail. This new technique enables the creation of spatial maps of the retinal rhodopsin and retinal pigment epithelium (RPE) bisretinoid distribution with an ≈ 50μm resolution.

No MeSH data available.


Bleaching curve and its fit (I).Temporal sequences of the intensity values (blue) and the corresponding fit (red) through minimizing E in Eq (9). Due to the low signal to noise ratio in cSLO measurements, there are large variations, but we can still recognize the overall trend. By averaging the cSLO movie over an angulus of 3 degree width, the noise and hence the variation are suppressed and the fit becomes tighter.(a) typical temporal sequence with its fitted curve at one pixel(b) temporal sequence from averaging intensities over an annulus (5–8 degrees) and fitted curve.
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pone.0131881.g003: Bleaching curve and its fit (I).Temporal sequences of the intensity values (blue) and the corresponding fit (red) through minimizing E in Eq (9). Due to the low signal to noise ratio in cSLO measurements, there are large variations, but we can still recognize the overall trend. By averaging the cSLO movie over an angulus of 3 degree width, the noise and hence the variation are suppressed and the fit becomes tighter.(a) typical temporal sequence with its fitted curve at one pixel(b) temporal sequence from averaging intensities over an annulus (5–8 degrees) and fitted curve.

Mentions: While Fig 3a shows a typical bleaching curve of a single pixel, we compute initial values a0, b0, and c0 from averages over a large spatial area in the autofluorescence movie, cf. Fig 3b. The parameter b0 is determined as the maximal count in the underlying histogram as described in Section 6.1, and a0 is spatially modified according to the macular pigment maps.


Modeling Photo-Bleaching Kinetics to Create High Resolution Maps of Rod Rhodopsin in the Human Retina.

Ehler M, Dobrosotskaya J, Cunningham D, Wong WT, Chew EY, Czaja W, Bonner RF - PLoS ONE (2015)

Bleaching curve and its fit (I).Temporal sequences of the intensity values (blue) and the corresponding fit (red) through minimizing E in Eq (9). Due to the low signal to noise ratio in cSLO measurements, there are large variations, but we can still recognize the overall trend. By averaging the cSLO movie over an angulus of 3 degree width, the noise and hence the variation are suppressed and the fit becomes tighter.(a) typical temporal sequence with its fitted curve at one pixel(b) temporal sequence from averaging intensities over an annulus (5–8 degrees) and fitted curve.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4510609&req=5

pone.0131881.g003: Bleaching curve and its fit (I).Temporal sequences of the intensity values (blue) and the corresponding fit (red) through minimizing E in Eq (9). Due to the low signal to noise ratio in cSLO measurements, there are large variations, but we can still recognize the overall trend. By averaging the cSLO movie over an angulus of 3 degree width, the noise and hence the variation are suppressed and the fit becomes tighter.(a) typical temporal sequence with its fitted curve at one pixel(b) temporal sequence from averaging intensities over an annulus (5–8 degrees) and fitted curve.
Mentions: While Fig 3a shows a typical bleaching curve of a single pixel, we compute initial values a0, b0, and c0 from averages over a large spatial area in the autofluorescence movie, cf. Fig 3b. The parameter b0 is determined as the maximal count in the underlying histogram as described in Section 6.1, and a0 is spatially modified according to the macular pigment maps.

Bottom Line: Our approach is based on analyzing the brightening of detected lipofuscin autofluorescence within small pixel clusters in registered imaging sequences taken with a commercial 488nm confocal scanning laser ophthalmoscope (cSLO) over a 1 minute period.We modeled the kinetics of rhodopsin bleaching by applying variational optimization techniques from applied mathematics.The physical model and the numerical analysis with its implementation are outlined in detail.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Mathematics, University of Vienna, Vienna, Austria.

ABSTRACT
We introduce and describe a novel non-invasive in-vivo method for mapping local rod rhodopsin distribution in the human retina over a 30-degree field. Our approach is based on analyzing the brightening of detected lipofuscin autofluorescence within small pixel clusters in registered imaging sequences taken with a commercial 488nm confocal scanning laser ophthalmoscope (cSLO) over a 1 minute period. We modeled the kinetics of rhodopsin bleaching by applying variational optimization techniques from applied mathematics. The physical model and the numerical analysis with its implementation are outlined in detail. This new technique enables the creation of spatial maps of the retinal rhodopsin and retinal pigment epithelium (RPE) bisretinoid distribution with an ≈ 50μm resolution.

No MeSH data available.