Reconstruction of visible light optical coherence tomography images retrieved from discontinuous spectral data using a conditional generative adversarial network.
Antonia LichteneggerMatthias SalasAlexander SingMarcus DuelkRoxane LicandroJohanna GespergerBernhard BaumannWolfgang DrexlerRainer A LeitgebPublished in: Biomedical optics express (2021)
Achieving high resolution in optical coherence tomography typically requires the continuous extension of the spectral bandwidth of the light source. This work demonstrates an alternative approach: combining two discrete spectral windows located in the visible spectrum with a trained conditional generative adversarial network (cGAN) to reconstruct a high-resolution image equivalent to that generated using a continuous spectral band. The cGAN was trained using OCT image pairs acquired with the continuous and discontinuous visible range spectra to learn the relation between low- and high-resolution data. The reconstruction performance was tested using 6000 B-scans of a layered phantom, micro-beads and ex-vivo mouse ear tissue. The resultant cGAN-generated images demonstrate an image quality and axial resolution which approaches that of the high-resolution system.
Keyphrases
- optical coherence tomography
- high resolution
- image quality
- diabetic retinopathy
- dual energy
- computed tomography
- deep learning
- mass spectrometry
- visible light
- optic nerve
- electronic health record
- big data
- high speed
- magnetic resonance
- magnetic resonance imaging
- single molecule
- convolutional neural network
- machine learning
- reduced graphene oxide
- density functional theory