Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network

Complex conjugate removal in optical coherence tomography using phase aware generative adversarial network

Significance

Current methods for complex conjugate removal (CCR) in frequency-domain optical coherence tomography (FD-OCT) often require additional hardware components, which increase system complexity and cost. A software-based solution would provide a more efficient and cost-effective alternative.

Aim

We aim to develop a deep learning approach to effectively remove complex conjugate artifacts (CCAs) from OCT scans without the need for extra hardware components.

Approach

We introduce a deep learning method that employs generative adversarial networks to eliminate CCAs from OCT scans. Our model leverages both conventional intensity images and phase images from the OCT scans to enhance the artifact removal process.

Results

Our CCR-generative adversarial network models successfully converted conventional OCT scans with CCAs into artifact-free scans across various samples, including phantoms, human skin, and mouse eyes imaged in vivo with a phase-stable swept source-OCT prototype. The inclusion of phase images significantly improved the performance of the deep learning models in removing CCAs.

Conclusions

Our method provides a low-cost, data-driven, and software-based solution to enhance FD-OCT imaging capabilities by the removal of CCAs.

Reconstruction of Optical Coherence Tomography Images from Wavelength Space Using Deep Learning

Conventional Fourier domain Optical Coherence Tomography (FD-OCT) systems depend on resampling into a wavenumber (k) domain to extract the depth profile. This either necessitates additional hardware resources or amplifies the existing computational complexity. Moreover, the OCT images also suffer from speckle noise, due to systemic reliance on low-coherence interferometry. We propose a streamlined and computationally efficient approach based on Deep Learning (DL) which enables reconstructing speckle-reduced OCT images directly from the wavelength (λ) domain. For reconstruction, two encoder-decoder styled networks, namely Spatial Domain Convolution Neural Network (SD-CNN) and Fourier Domain CNN (FD-CNN), are used sequentially. The SD-CNN exploits the highly degraded images obtained by Fourier transforming the (λ) domain fringes to reconstruct the deteriorated morphological structures along with suppression of unwanted noise. The FD-CNN leverages this output to enhance the image quality further by optimization in the Fourier domain (FD). We quantitatively and visually demonstrate the efficacy of the method in obtaining high-quality OCT images. Furthermore, we illustrate the computational complexity reduction by harnessing the power of DL models. We believe that this work lays the framework for further innovations in the realm of OCT image reconstruction.

Super-resolution algorithm for the characterization of sweat glands in fingerprint OCT images

Optical coherence tomography (OCT) is a noninvasive optical imaging technique that can be used to produce three-dimensional images of fingerprints. However, the low quality and poor resolution of the regions of interest (ROIs) in OCT images make it challenging to segment small tissues accurately. To address this issue, a super-resolution (SR) network called ESRNet has been developed to enhance the quality of OCT images, facilitating their applications in research. Firstly, the performance of the SR images produced by ESRNet is evaluated by comparing it to those generated by five other SR methods. Specifically, the SR performance is evaluated using three upscale factors (2×, 3×, and 4×) to assess the quality of the enhanced images. Based on the results obtained from the three datasets, it is evident that ESRNet outperforms current advanced networks in terms of SR performance. Furthermore, the segmentation accuracy of sweat glands has been significantly improved by the SR images. The number of sweat glands in the top view increased from 102 to 117, further substantiating the performance of the ESRNet network. The spiral structure of sweat glands is clear to human eyes and has been verified by showing similar left-right-handed spiral numbers. Finally, a sweat gland recognition method for the SR 3D images is proposed.

30 Years of Optical Coherence Tomography: introduction to the feature issue

The guest editors introduce a feature issue commemorating the 30th anniversary of Optical Coherence Tomography.