Since it was first reported in early 1990s [1
] optical coherence tomography (OCT) has been widely used to non-invasively provide high resolution, depth resolved cross-sectional and three-dimensional (3-D) images of highly scattering samples [2
]. More recently, spectral-domain OCT (SDOCT) and swept-source OCT (SSOCT) imaging have been developed where these frequency-domain OCT (FDOCT) methods have advantages over time-domain ones in terms of acquisition speed, sensitivity and signal to noise ratio [4
]. In order to compensate for the complex conjugate ambiguity and acquisition speed which limited the practical applications of FDOCT, real-time in vivo
full-range complex FDOCT has been developed to increase the ranging distance in the sample [6
There are various methods reported in the literature to visualize blood flow in vessels and contrast them from surrounding tissue microstructures using OCT imaging systems. Some methods utilize the Doppler shift in the phase information of OCT images for optical flow measurement [8
] while some statistical methods analyze the variations of speckle in the OCT structural images [19
Optical Doppler tomography (ODT) combines Doppler velocimetry with OCT and utilizes the Doppler shift in the frequency of light scattered from a moving particle (such as red blood cells) to measure the velocity both in time-domain [8
] and frequency-domain [9
] OCT. In order to detect the Doppler shifts in the frequency spectrum, multiple observations (ensembles) of the interference signal from the same location are acquired. Then, the Doppler center frequency is estimated using spectrogram method based on the short-time Fourier transform (STFT) algorithm. However, the minimum detectable Doppler frequency shift varies inversely with the FFT window time at each pixel, which introduces a trade-off between velocity sensitivity and imaging speed as well as spatial resolution [10
In order to remove the relationship between the velocity sensitivity and spatial resolution while maintaining a high acquisition speed, phase resolved optical Doppler tomography (PRODT) was introduced in time [10
] and frequency domain [9
]. PRODT evaluates the phase difference between adjacent A-lines within one B-image to estimate the Doppler frequency shift. Then, the estimated mean frequency is used to measure the blood flow velocity using Kasai autocorrelation technique [11
]. Although widely used, the sensitivity of PRODT to blood flow is low and makes it difficult to visualize 3D microcirculations in applications such as human skin where the blood flow within the capillary vessels is in the order of 0.1–0.9 mm/s [12
]. Also, it would be advantageous to extract Doppler information using the frequency domain method because the velocity dynamic range of a phase-resolved ODT system is determined by A-line scanning rate. The drawbacks of Doppler OCT are its insensitivity to transverse component of blood flow and also its dependence on the phase stability of the system.
Vokac et al. [13
] proposed a method to improve the sensitivity of PROCT by utilizing the phase variance between adjacent B-scans. However, their method was sensitive to slow flows within capillary vessels, because, the time interval between the adjacent B-scan images was relatively long (~ few microseconds). Because of the relatively long imaging time (~25 min), their method could not be used for in vivo
human applications such as human skin or retina where involuntary subject movements are inevitable.
Optical micro-angiography (OMAG) is a three dimensional imaging modality [14
] which has shown promising results for imaging cerebral blood flow in mice [15
] and rat [16
] and ocular blood flow in human retina [17
]. OMAG exploited the inherent properties of the OCT signals to efficiently separate the moving and static scattering elements within tissue, hence enabled precise localization of blood perfusion in three-dimensional microstructures. This was achieved by modulating the spatial OCT spectral interferogram at a constant modulation frequency while the probe beam was scanned across the B scans. Also, another extension of OMAG has been used to visualize detailed microcirculation within human skin tissue beds [18
]. In that extension, as opposed to the previous OMAG method, the A-line density of B-scan was decreased, while B-scan density was increased. The OMAG algorithm was then applied on C-scan direction (elevation direction), rather than B-scan direction (lateral direction) as in the conventional approach.
Clutter rejection is one of the most important data processing steps in visualizing blood perfusion and vascular structures. Clutter is the scattered signal from stationary or slowly moving tissues in the coherence sampling volume of the probe beam in the sample arm. Since clutter signal is typically stronger than the Doppler signal, it can reduce the sensitivity and accuracy in estimating the flow. The concept of clutter rejection filtering is similar to medical ultrasonic imaging where mechanical ultrasound waves are used to visualize the blood flow in arteries and veins. Color Doppler imaging (CDI), a tomographic real-time imaging technique, is one of the principal ultrasonic imaging modalities that is similar to optical micro-angiography and has a wide range of clinical applications. In CDI, blood flow of an ROI (region of interest) is color-coded and visualized on top of B-mode (gray-scale coded intensity image of tissue structures) images and displayed in real-time (33 frames per second or higher). In order to estimate the Doppler frequency shift, clutter should be suppressed using a post-processing step commonly known as clutter rejection filtering. Clutter filters can be divided into three main categories: static filters where an IIR or FIR filter with fixed coefficients is used, adaptive filters where the characteristics of the filter is adapted to the received signal, and a combination of static and adaptive filters. Static filters are widely used and preferred over adaptive ones in commercial products because they can be implemented in real-time. However, static filters cannot efficiently remove clutter due to nonstationary tissue motions from cardiac activities, respiration and the transducer/patient movements. Also, the assumption that clutter is centered around zero frequency is not always met in practice. Several adaptive filters have been proposed among which eigen-regression filters can theoretically provide the maximum clutter suppression due to its best mean square approximation of the clutter [19
There are several methods proposed in the literature for clutter rejection in OCT-based flow imaging. Ren and Li [23
] developed a delay line filter (DLF) to reject the clutter effect and showed that a first-order phase-shifted DLF could effectively remove the clutter in capillary flow phantom and in mouse ear. Compared to the conventional phase-resolved optical Doppler tomography, DLF was more sensitive to Doppler flow and picking up small blood vessels that were masked by clutter signal in PR-OCT. Also An and Wang [18
] applied a static high-pass filter in OMAG to remove the clutter component from the received Doppler signal. However, these static filters were sensitive to tissue movements and their performance in removing the clutter degraded at the presence of unwanted motion. In order to compensate bulk tissue motion, An and Wang [17
] proposed a phase compensation method to estimate clutter’s center frequency and shift it to zero, and then applied a static filter to remove the clutter component. However, this technique may not be effective when the clutter is broadband or the flow signal is very strong where the estimated center frequency may not be accurate.
In this paper, we propose eigendecposition-based filtering technique for clutter rejection in optical imaging of blood flow. A series of flow phantom studies are performed where tissue (phantom) motion is externally introduced by tapping over the imaging surface to simulate tissue motion. The performance of ED-based clutter rejection in removing the tissue motion and picking up the flow information is studied and the efficiency of the proposed technique is compared with those of phase-compensation method and static high-pass filtering. Also, in vivo experiments are performed for visualizing microcirculations within human skin tissue beds and the performance of different clutter filters is compared with each other. Finally, we show the sensitivity of the ED-based clutter rejection filters in picking up blood flow in the capillaries of mouse ear.