Gabor optical coherence tomographic angiography (GOCTA) (Part I): human retinal imaging in vivo

Optical flow optical coherence tomography for determining accurate velocity fields

Determining micron-scale fluid flow velocities using optical coherence tomography (OCT) is important in both biomedical research and clinical diagnosis. Numerous methods have been explored to quantify the flow information, which can be divided into either phase-based or amplitude-based methods. However, phase-based methods, such as Doppler methods, are less sensitive to transverse velocity components and suffer from wrapped phase and phase instability problems for axial velocity components. On the other hand, amplitude-based methods, such as speckle variance OCT, correlation mapping OCT and split-spectrum amplitude-decorrelation angiography, focus more on segmenting flow areas than quantifying flow velocities. In this paper, we propose optical flow OCT (OFOCT) to quantify accurate velocity fields. The equivalence between optical flow and real velocity fields is validated in OCT imaging. The sensitivity fall-off of a Fourier-domain OCT (FDOCT) system is considered in the modified optical flow continuity constraint. Spatial-temporal smoothness constraints are used to make the optical flow problem well-posed and reduce noises in the velocity fields. An iteration solution to the optical flow problem is implemented in a graphics processing unit (GPU) for real-time processing. The accuracy of the velocity fields is verified through phantom flow experiments by using a diluted milk powder solution as a scattering medium. Velocity fields are then used to detect flow turbulence and reconstruct flow trajectory. The results show that OFOCT is accurate in determining velocity fields and applicable to research concerning fluid dynamics.

Real-time en-face Gabor optical coherence tomographic angiography on human skin using CUDA GPU

We recently proposed an optical coherence tomographic angiography (OCTA) algorithm, Gabor optical coherence tomographic angiography (GOCTA), which can extract microvascular signals from a spectral domain directly with lower computational complexity compared to other algorithms. In this manuscript, we combine a programmable swept source, an OCT complex signal detecting unit, and graphics process units (GPU) to achieve a real-time en-face GOCTA system for human skin microvascular imaging. The programmable swept source can balance the A-scan rate and the spectral tuning range; the polarization-modulation based complex signal detecting unit can double the imaging depth range, and the GPU can accelerate data processing. C++ and CUDA are used as the programming platform where five parallel threads are created for galvo-driving signal generation, data acquisition, data transfer, data processing, and image display, respectively. Two queues (for the raw data and en-face images, respectively) are used to improve the data exchange efficiency among different devices. In this study, the data acquisition time and data processing time for each 3D complex volume (256×304×608 pixels,) are 405.3 and 173.7 milliseconds respectively. To the best of our knowledge, this is the first time to show en-face microvascular images covering 3×3 mm² at a refresh rate of 2.5 Hz.

Gabor optical coherence tomographic angiography (GOCTA) (Part II): theoretical basis of sensitivity improvement and optimization for processing speed

We previously proposed a Gabor optical coherence tomography angiography (GOCTA) algorithm for spectral domain optical coherence tomography (SDOCT) to extract microvascular signals from spectral fringes directly, with speed improvement of 4 to 20 times over existing methods. In this manuscript, we explored the theoretical basis of GOCTA with comparison of experimental data using solid and liquid displacement sample targets, demonstrating that the majority of the GOCTA sensitivity advantage over speckle variance based techniques was in the small displacement range (< 10 ∼ 20 µm) of the moving target (such as red blood cells). We further normalized GOCTA signal by root-mean-square (RMS) of original fringes, achieving a more uniform image quality, especially at edges of blood vessels where slow flow could occur. Furthermore, by transecting the spectral fringes and using skipped convolution, the data processing speed could be further improved. We quantified the trade-off in signal-to-noise-ratio (SNR) and contrast-to-noise-ratio (CNR) under various sub-spectral bands and found an optimized condition using 1/4 spectral band for minimal angiography image quality degradation, yet achieving a further 26.7 and 34 times speed improvement on GPU and CPU, respectively. Our optimized GOCTA algorithm has a speed advantage of over 140 times compared to existing speckle variance OCT (SVOCT) method.