Virtual Averaging Making Nonframe-Averaged Optical Coherence Tomography Images Comparable to Frame-Averaged Images

虚拟平均法使非帧平均光学相干断层扫描图像与帧平均图像具有可比性

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Abstract

PURPOSE: Developing a novel image enhancement method so that nonframe-averaged optical coherence tomography (OCT) images become comparable to active eye-tracking frame-averaged OCT images. METHODS: Twenty-one eyes of 21 healthy volunteers were scanned with noneye-tracking nonframe-averaged OCT device and active eye-tracking frame-averaged OCT device. Virtual averaging was applied to nonframe-averaged images with voxel resampling and adding amplitude deviation with 15-time repetitions. Signal-to-noise (SNR), contrast-to-noise ratios (CNR), and the distance between the end of visible nasal retinal nerve fiber layer (RNFL) and the foveola were assessed to evaluate the image enhancement effect and retinal layer visibility. Retinal thicknesses before and after processing were also measured. RESULTS: All virtual-averaged nonframe-averaged images showed notable improvement and clear resemblance to active eye-tracking frame-averaged images. Signal-to-noise and CNR were significantly improved (SNR: 30.5 vs. 47.6 dB, CNR: 4.4 vs. 6.4 dB, original versus processed, P < 0.0001, paired t-test). The distance between the end of visible nasal RNFL and the foveola was significantly different before (681.4 vs. 446.5 μm, Cirrus versus Spectralis, P < 0.0001) but not after processing (442.9 vs. 446.5 μm, P = 0.76). Sectoral macular total retinal and circumpapillary RNFL thicknesses showed systematic differences between Cirrus and Spectralis that became not significant after processing. CONCLUSION: The virtual averaging method successfully improved nontracking nonframe-averaged OCT image quality and made the images comparable to active eye-tracking frame-averaged OCT images. TRANSLATIONAL RELEVANCE: Virtual averaging may enable detailed retinal structure studies on images acquired using a mixture of nonframe-averaged and frame-averaged OCT devices without concerning about systematic differences in both qualitative and quantitative aspects.

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