Retinal Photocoagulation by Lasers

Kapany, N. S.; Peppers, N. A.; Zweng, H. Christian; Flocks, Milton

doi:10.1038/199146a0

Cited by 93 publications

(24 citation statements)

References 5 publications

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“…3 One of the earliest experimental studies demonstrating the feasibility of retinal photocoagulation by a ruby laser reported a nonlinear correlation between the incident energy and the diameter of the retinal lesion. 4 Modeling of retinal heating with pulsed lasers has predicted an increase in the width and depth of the coagulated zone with increasing pulse durations. 5 Exposures from 10 to 50 milliseconds have been shown to reduce pain associated with photocoagulation.…”

Section: He Treatment Parametersmentioning

confidence: 99%

Effect of Pulse Duration on Size and Character of the Lesion in Retinal Photocoagulation

et al. 2008

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Section: He Treatment Parametersmentioning

confidence: 99%

Effect of Pulse Duration on Size and Character of the Lesion in Retinal Photocoagulation

et al. 2008

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“…Single high registration errors may occur, when the SDO is out of focus. The result of out of focus blur are blurry vessels, which 1 Demo video material can be found under http://www.artorg.unibe. ch/content/research_units/ophthalmic_technology/research/index_eng.html.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Multimodal Retinal Image Registration for a Computer-Assisted Laser Photocoagulation System

Broehan¹,

Rudolph

Amstutz

et al. 2011

IEEE Trans. Biomed. Eng.

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An algorithm for the real-time registration of a retinal video sequence captured with a scanning digital ophthalmoscope (SDO) to a retinal composite image is presented. This method is designed for a computer-assisted retinal laser photocoagulation system to compensate for retinal motion and hence enhance the accuracy, speed, and patient safety of retinal laser treatments. The procedure combines intensity and feature-based registration techniques. For the registration of an individual frame, the translational frame-to-frame motion between preceding and current frame is detected by normalized cross correlation. Next, vessel points on the current video frame are identified and an initial transformation estimate is constructed from the calculated translation vector and the quadratic registration matrix of the previous frame. The vessel points are then iteratively matched to the segmented vessel centerline of the composite image to refine the initial transformation and register the video frame to the composite image. Criteria for image quality and algorithm convergence are introduced, which assess the exclusion of single frames from the registration process and enable a loss of tracking signal if necessary. The algorithm was successfully applied to ten different video sequences recorded from patients. It revealed an average accuracy of 2.47 ± 2.0 pixels (∼23.2 ± 18.8 μm) for 2764 evaluated video frames and demonstrated that it meets the clinical requirements.

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“…Retinal laser therapy was developed over 50 years ago with investigations by Kapany and colleagues in 1963 using the ruby laser (Kapany et al 1963) and has had a profound impact on the treatment of numerous retinal neovascular disorders. Seminal early studies established laser as the first treatment for numerous eye conditions.…”

Section: Introductionmentioning

confidence: 99%