Retinal layer measurements after successful macula-off retinal detachment repair using optical coherence tomography

PURPOSE Optical coherence tomography (OCT) was used to analyze the thickness of various retinal layers of patients following successful macula-off retinal detachment (RD) repair. METHODS Optical coherence tomography scans of patients after successful macula-off RD repair were reanalyzed with a subsegmentation algorithm to measure various retinal layers. Regression analysis was performed to correlate time after surgery with changes in layer thickness. In addition, patients were divided in two groups. Group 1 had a follow-up period after surgery of up to 7 weeks (range, 21-49 days). In group 2, the follow-up period was >8 weeks (range, 60-438 days). Findings were compared to a group of age-matched healthy controls. RESULTS Correlation analysis showed a significant positive correlation between inner nuclear-outer plexiform layer (INL-OPL) thickness and time after surgery (P=0.0212; r2=0.1551). Similar results were found for the ellipsoid zone-retinal pigment epithelium complex (EZ-RPE) thickness (P=0.005; r2=0.2215). Ganglion cell-inner plexiform layer thickness (GCL-IPL) was negatively correlated with time after surgery (P=0.0064; r2=0.2101). For group comparison, the retinal nerve fiber layer in both groups was thicker compared to controls. The GCL-IPL showed significant thinning in group 2. The outer nuclear layer was significantly thinner in groups 1 and 2 compared to controls. The EZ-RPE complex was significantly thinner in groups 1 and 2 compared to controls. In addition, values in group 1 were significantly thinner than in group 2. CONCLUSIONS Optical coherence tomography retinal layer thickness measurements after successful macular-off RD repair revealed time-dependent thickness changes. Inner nuclear-outer plexiform layer thickness and EZ-RPE thickness was positively correlated with time after surgery. Ganglion cell-inner plexiform layer thickness was negatively correlated with time after surgery.

Yellow laser light generation by frequency doubling of the output from a master oscillator fiber power amplifier system

We present a power-scalable approach for yellow laser-light generation based on standard Ytterbium (Yb) doped fibers. To force the cavity to lase at 1154 nm, far above the gain-maximum, measures must be taken to fulfill lasing condition and to suppress competing amplified spontaneous emission (ASE) in the high-gain region. To prove the principle we built a fiber-laser cavity and a fiber-amplifier both at 1154 nm. In between cavity and amplifier we suppressed the ASE by 70 dB using a fiber Bragg grating (FBG) based filter. Finally we demonstrated efficient single pass frequency doubling to 577 nm with a periodically poled lithium niobate crystal (PPLN). With our linearly polarized 1154 nm master oscillator power fiber amplifier (MOFA) system we achieved slope efficiencies of more than 15 % inside the cavity and 24 % with the fiber-amplifier. The frequency doubling followed the predicted optimal efficiency achievable with a PPLN crystal. So far we generated 1.5 W at 1154nm and 90 mW at 577 nm. Our MOFA approach for generation of 1154 nm laser radiation is power-scalable by using multi-stage amplifiers and large mode-area fibers and is therefore very promising for building a high power yellow laser-light source of several tens of Watt.

Differentiation of Diabetic Macular Edema From Pseudophakic Cystoid Macular Edema by Spectral-Domain Optical Coherence Tomography.

PURPOSE: To differentiate diabetic macular edema (DME) from pseudophakic cystoid macular edema (PCME) based solely on spectral-domain optical coherence tomography (SD-OCT). METHODS: This cross-sectional study included 134 participants: 49 with PCME, 60 with DME, and 25 with diabetic retinopathy (DR) and ME after cataract surgery. First, two unmasked experts classified the 25 DR patients after cataract surgery as either DME, PCME, or mixed-pattern based on SD-OCT and color-fundus photography. Then all 134 patients were divided into two datasets and graded by two masked readers according to a standardized reading-protocol. Accuracy of the masked readers to differentiate the diseases based on SD-OCT parameters was tested. Parallel to the masked readers, a computer-based algorithm was established using support vector machine (SVM) classifiers to automatically differentiate disease entities. RESULTS: The masked readers assigned 92.5% SD-OCT images to the correct clinical diagnose. The classifier-accuracy trained and tested on dataset 1 was 95.8%. The classifier-accuracy trained on dataset 1 and tested on dataset 2 to differentiate PCME from DME was 90.2%. The classifier-accuracy trained and tested on dataset 2 to differentiate all three diseases was 85.5%. In particular, higher central-retinal thickness/retinal-volume ratio, absence of an epiretinal-membrane, and solely inner nuclear layer (INL)-cysts indicated PCME, whereas higher outer nuclear layer (ONL)/INL ratio, the absence of subretinal fluid, presence of hard exudates, microaneurysms, and ganglion cell layer and/or retinal nerve fiber layer cysts strongly favored DME in this model. CONCLUSIONS: Based on the evaluation of SD-OCT, PCME can be differentiated from DME by masked reader evaluation, and by automated analysis, even in DR patients with ME after cataract surgery. The automated classifier may help to independently differentiate these two disease entities and is made publicly available.