Minhaz Ahmed Tahseen, Murali Archana, Örge Faruk H, Wilson David L, Bayat Mahdi
Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
J Imaging Inform Med. 2025 Apr 10. doi: 10.1007/s10278-025-01488-5.
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.
本研究旨在解决廉价的高频超声生物显微镜（UBM）系统在可视化小眼部结构和解剖标志方面的局限性，特别是在焦区以外的区域，通过提高临床眼科应用中重要眼部结构的图像质量和可视性来实现。我们为三维超声生物显微镜（3D-UBM）成像系统开发了一种生成对抗网络（GAN）方法，称为空间可变反卷积GAN（SDV-GAN）。我们采用空间可变反卷积和补丁融合来增强原始UBM图像。这种计算成本高昂的迭代反卷积过程产生了用于训练SDV-GAN的成对原始图像和增强图像。SDV-GAN实现了高性能指标，结构相似性指数测量（SSIM）为0.96，峰值信噪比（PSNR）为36.92dB。在测试图像中可以更清晰地看到结构，且没有明显的伪影。SDV-GAN反卷积改善了从UBM图像进行的生物测量，在SDV-GAN反卷积前后的房角开放距离（AOD，p<0.0001）和房角隐窝面积（ARA，p<0.0001）测量中有显著差异。通过更清晰地识别顶点，SDV-GAN提高了反卷积前后图像中ARA测量的阅片者间一致性（组内相关系数，[ICC]分别为0.62和0.73）。在标准GPU上实现了实时增强，推理时间为40ms/帧（25帧/秒），而使用迭代反卷积时为93ms/帧（11帧/秒）。SDV-GAN有效地增强了UBM图像，提高了重要眼部结构的可视性和评估。其实时处理能力突出了GAN增强在利用现有扫描仪促进眼科准确诊断和治疗规划方面的临床潜力。
