Improved biometric quantification in 3D ultrasound biomicroscopy via generative adversarial networks-based image enhancement.

This study addresses the limitations of inexpensive, high-frequency ultrasound biomicroscopy (UBM) systems in visualizing small ocular structures and anatomical landmarks, especially outside the focal area, by improving image quality and visibility of important ocular structures for clinical ophthalmology applications. We developed a generative adversarial network (GAN) method for the 3D ultrasound biomicroscopy (3D-UBM) imaging system, called Spatially variant Deconvolution GAN (SDV-GAN). We employed spatially varying deconvolution and patch blending to enhance the original UBM images. This computationally expensive iterative deconvolution process yielded paired original and enhanced images for training the SDV-GAN. SDV-GAN achieved high performance metrics, with a structural similarity index measure (SSIM) of 0.96 and a peak signal-to-noise ratio (PSNR) of 36.92 dB. Structures were more clearly seen with no noticeable artifacts in the test images. SDV-GAN deconvolution improved biometric measurements made from UBM images, giving significant differences in angle opening distance (AOD, p < 0.0001) and angle recess area (ARA, p < 0.0001) measurements before and after SDV-GAN deconvolution. With clearer identification of apex, SDV-GAN improved inter-reader agreement in ARA measurements in images before and after deconvolution (intraclass correlation coefficient, [ICC] of 0.62 and 0.73, respectively). Real-time enhancement was achieved with an inference time of ~ 40 ms/frame (25 frames/s) on a standard GPU, compared to ~ 93 ms/frame (11 frames/s) using iterative deconvolution. SDV-GAN effectively enhanced UBM images, improving visibility and assessment of important ocular structures. Its real-time processing capabilities highlight the clinical potential of GAN enhancement in facilitating accurate diagnosis and treatment planning in ophthalmology using existing scanners.