Eigendecomposition-Based Clutter Filtering Technique for Optical Microangiography

Abstract

In this paper, we propose eigendecposition- (ED-) based clutter filtering technique for 3D optical imaging of blood flow. Due to its best mean square approximation of the clutter, eigen-regression filters can theoretically provide maximum clutter suppression. Compared to the existing clutter rejection techniques in the literature used for optical imaging of blood flow, ED-based clutter filtering is less sensitive to tissue motion and can efficiently suppress the clutter while preserving the flow information. Therefore, it creates images with better contrast in the presence of bulk motion. The performance of the proposed ED-based technique is compared with that of phase compensation method and static high-pass filtering. The quantitative and qualitative performances are compared with each other in phantom studies and in vivo imaging, respectively. Also, 3D image of microvascular structures in mouse ear is presented where the clutter has been suppressed with ED-based technique. This technique can be used in applications where involuntary movements due to cardiac and respiratory cycles are inevitable (such as retinal imaging).

Highly efficient eigen decomposition based statistical optical microangiography

“…However, the bulk tissue motion due to, e.g., heartbeat and breathing is inevitable for in vivo imaging, which causes the central frequency of the tissue components slightly drifting away from the zero frequency. Under this situation, the adaptive nature of ED filtering fits perfectly well to separate the moving signals from the tissue signals ( Figure 1D), leading to a better mapping of vascular networks, as experimentally demonstrated by ED-OMAG on the basis of pixel by pixel or voxel by voxel computation in (11). While statistically sound, voxel based analysis has serious drawbacks to implement ED-OMAG for real time imaging because (I) it requires expensive computational power (typically >1 h to generate a 3D optical angiogram for a typical OCT dataset of 1,024×240×240 voxels); and (II) it may fail to work when the probing voxel/pixel is totally located within moving blood, due to the fact that under this circumstance, the moving speckle, rather than tissue signal dominates the measured OCT signals, which does not satisfy ED filtering requirement to remove static tissue components (13).…”

“…Some other methods use the phase information, such as phase variance OCT (7). Leveraging the complete information available in the OCT system, the complex OCT signal is explored, such as optical microangiography (OMAG) (8,9), complex differential variance (CDV) (10), and eigendecomposition (ED) based OMAG (11). Among them, ED approach is a statistical analysis method that utilizes the statistical properties of time varying complex OCT signals to achieve the purpose of contrasting blood flow within tissue.…”

Quantifying the vascular response to ischemia with speckle variance optical coherence tomography

“…Approaches range from intensity thresholding and band pass filtering [62] to clutter rejection [67] and directional filters (Gabor) [34]. Here, we chose to implement a Hessian filter [55][56][57] due to speed of computation and the multi-scale estimation of vessel-like structures that is compatible with segmenting the range of vessel sizes observed in the HLI model.…”

Automated three-dimensional registration and volume rebuilding for wide-field angiographic and structural optical coherence tomography

“…With the increase of OCT imaging speed, 2,3 an OCT angiography (OCTA) technique was developed. [4][5][6][7][8][9][10][11][12][13][14] Multiple B-frames can be taken on the same position, and the changes of OCT reflectance properties can be measured to differentiate vasculature from static tissues. These B-frames can also be averaged to generate structural OCT images.…”