Multi-shaping sparse-continuous reconstruction for an optical coherence tomography sidelobe suppression.

Optical coherence tomography (OCT) images are commonly affected by sidelobe artifacts due to spectral non-uniformity and spectral leakage. Conventional frequency domain spectral shaping methods widen the mainlobe and compromise axial resolution. While image-domain deconvolution techniques can address the trade-off between axial resolution and artifact suppression, their reconstruction quality relies on accurate measurement or estimation of system point spread function (PSF). Inaccurate PSF estimation leads to loss of details in the reconstructed images. In this Letter, we introduce multi-shaping sparse-continuous reconstruction (MSSCR) for an OCT image, a novel, to the best of our knowledge, framework that combines spectral multi-shaping and iterative image reconstruction with sparse-continuous priors. The MSSCR achieves sidelobe suppression without requiring any PSF measurement or estimation and effectively preserving the axial resolution. The experimental results demonstrate that the MSSCR achieves sidelobe suppression of more than 8 dB. We believe that the MSSCR holds potential for addressing sidelobe artifacts in OCT.

Full-range optical coherence refraction tomography.

In full-range optical coherence tomography (FROCT), the axial resolution is often superior to the lateral resolution, which is degraded by its signal processing and presents nonuniformity at different imaging depths due to the defocus effect. Optical coherence refraction tomography (OCRT) uses images from multiple angles to computationally reconstruct an image with isotropic resolution, solving the problem of image resolution anisotropy in the sub-millimeter imaging depth range. In this work, we report full-range OCRT (FROCRT), which uses full-range complex conjugate-free optical coherence tomography (OCT) images from multiple angles to reconstruct an isotropic spatial resolution image with extended imaging range. We build a system that can automatically acquire images from 360° for reconstruction. We further apply FROCRT to tape phantom, optical-cleared mouse leg bone and spinal cord samples, and aloe sample, achieving extended imaging depth and isotropic resolution. We propose FROCRT, as an extension to OCRT, will enable broader applications.

Robotic-arm-assisted flexible large field-of-view optical coherence tomography.

Optical coherence tomography (OCT) is a three-dimensional non-invasive high-resolution imaging modality that has been widely used for applications ranging from medical diagnosis to industrial inspection. Common OCT systems are equipped with limited field-of-view (FOV) in both the axial depth direction (a few millimeters) and lateral direction (a few centimeters), prohibiting their applications for samples with large and irregular surface profiles. Image stitching techniques exist but are often limited to at most 3 degrees-of-freedom (DOF) scanning. In this work, we propose a robotic-arm-assisted OCT system with 7 DOF for flexible large FOV 3D imaging. The system consists of a depth camera, a robotic arm and a miniature OCT probe with an integrated RGB camera. The depth camera is used to get the spatial information of targeted sample at large scale while the RGB camera is used to obtain the exact position of target to align the image probe. Eventually, the real-time 3D OCT imaging is used to resolve the relative pose of the probe to the sample and as a feedback for imaging pose optimization when necessary. Flexible probe pose manipulation is enabled by the 7 DOF robotic arm. We demonstrate a prototype system and present experimental results with flexible tens of times enlarged FOV for plastic tube, phantom human finger, and letter stamps. It is expected that robotic-arm-assisted flexible large FOV OCT imaging will benefit a wide range of biomedical, industrial and other scientific applications.

Phase unwrapping based on a residual en-decoder network for phase images in Fourier domain Doppler optical coherence tomography.

To solve the phase unwrapping problem for phase images in Fourier domain Doppler optical coherence tomography (DOCT), we propose a deep learning-based residual en-decoder network (REDN) method. In our approach, we reformulate the definition for obtaining the true phase as obtaining an integer multiple of 2π at each pixel by semantic segmentation. The proposed REDN architecture can provide recognition performance with pixel-level accuracy. To address the lack of phase images that are noise and wrapping free from DOCT systems for training, we used simulated images synthesized with DOCT phase image background noise features. An evaluation study on simulated images, DOCT phase images of phantom milk flowing in a plastic tube and a mouse artery, was performed. Meanwhile, a comparison study with recently proposed deep learning-based DeepLabV3+ and PhaseNet methods for signal phase unwrapping and traditional modified networking programming (MNP) method was also performed. Both visual inspection and quantitative metrical evaluation based on accuracy, specificity, sensitivity, root-mean-square-error, total-variation, and processing time demonstrate the robustness, effectiveness and superiority of our method. The proposed REDN method will benefit accurate and fast DOCT phase image-based diagnosis and evaluation when the detected phase is wrapped and will enrich the deep learning-based image processing platform for DOCT images.