研究目的
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.
研究成果
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. This method 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.
研究不足
The proposed virtual averaging does not guarantee the use of all neighboring information due to its random nature and the limited number of processing repetitions. Additionally, the method assumes negligible deviation in the z direction, which may not hold for images with significant z motion.
1:Experimental Design and Method Selection:
The study involved scanning 21 eyes of 21 healthy volunteers with both noneye-tracking nonframe-averaged OCT and active eye-tracking frame-averaged OCT devices. Virtual averaging was applied to nonframe-averaged images with voxel resampling and adding amplitude deviation with 15-time repetitions.
2:Sample Selection and Data Sources:
Healthy volunteers were recruited, and their right eyes were scanned using Cirrus HD-OCT and Spectralis OCT devices.
3:List of Experimental Equipment and Materials:
Cirrus HD-OCT (software version 6.1; Zeiss, Dublin, CA) and Spectralis (software version 1.5; Heidelberg Engineering, Heidelberg, Germany) were used.
4:1; Zeiss, Dublin, CA) and Spectralis (software version 5; Heidelberg Engineering, Heidelberg, Germany) were used. Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: Images were processed with virtual averaging, and their quality was assessed using SNR, CNR, and the distance between the end of visible nasal RNFL and the foveola.
5:Data Analysis Methods:
Statistical analysis was performed using paired t-tests to compare image quality metrics before and after processing.
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