![]() 4 An OCT or functional OCT volume usually has a size of hundreds of megabytes or several gigabytes, which requires not only fast and wide-band analog-to-digital converter (ADC) or frame grabber for data acquisition, but also advanced graphics processing units (GPUs) for real-time imaging alignment. However, with the evolution of OCT techniques, the increased data size becomes a major issue. 2 In the technical aspect, the acquisition speed of OCT systems keeps increasing from tens of hertz at the beginning of its invention to several megahertz today, 3 which enables the OCT imaging to have fewer motion artifacts, a wider field of view, better resolutions, and higher detection sensitivity. 1 In ophthalmology, OCT has become the clinical standard for the examination of nonsuperficial retinal lesions, such as choroidal neovascularization, macular edema, and pigment epithelial detachment. Optical coherence tomography (OCT) is a noninvasive cross-sectional high-resolution imaging modality that has been widely used in various medical fields, such as ophthalmology, cardiovascular endoscopy, and dermatology. The adopted pix2pixGAN is superior to other possible deep learning and compressed sensing solutions.Ĭonclusions: Our work demonstrates that the proper integration of OCT and deep learning could benefit the development of healthcare in low-resource settings. Results: Extensively, qualitative and quantitative results show our method could significantly improve the SNR of the low bit-depth OCT images. We employ a pixel-to-pixel generative adversarial network (pix2pixGAN) architecture in the low-to-high bit-depth OCT image transition. However, a low bit depth will lead to the degradation of the detection sensitivity, thus reducing the signal-to-noise ratio (SNR) of OCT images.Īim: We propose using deep learning to reconstruct high SNR OCT images from low bit-depth acquisition.Īpproach: The feasibility of our approach is evaluated by applying this approach to the quantized 3- to 8-bit data from native 12-bit interference fringes. Significance: Reducing the bit depth is an effective approach to lower the cost of an optical coherence tomography (OCT) imaging device and increase the transmission efficiency in data acquisition and telemedicine.
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