Motion artifact and background noise suppression on optical microangiography frames using a naïve Bayes mask

Abstract

Optical coherence tomography (OCT) is a technique that allows for the three-dimensional imaging of small volumes of tissue (a few millimeters) with high resolution (~10μm). Optical microangiography (OMAG) is a method of processing OCT data, which allows for the extraction of the tissue vasculature with capillary resolution from the OCT images. Cross-sectional B-frame OMAG images present the location of the patent blood vessels; however, the signal-to-noise-ratio (SNR) of these images can be affected by several factors such as the quality of the OCT system and the tissue motion artifact. This background noise can appear in the en face projection view image. In this work we propose to develop a binary mask that can be applied on the cross-sectional B-frame OMAG images, which will reduce the background noise while leaving the signal from the blood vessels intact. The mask is created by using a Naïve Bayes (NB) classification algorithm trained with a gold standard image which is manually segmented by an expert. The masked OMAG images present better contrast for binarizing the image and quantifying the result without the influence of noise. The results are compared with a previously developed frequency rejection filter (FRF) method which is applied on the en face projection view image. It is demonstrated that both the NB and FRF methods provide similar vessel length fractions. The advantage of the NB method is that the results are applicable in 3D, and that its use is not limited to periodic motion artifacts.

In vivo tissue injury mapping using optical coherence tomography based methods

“…The high resolution images of the cortex were taken through the open skull cranial window [14], using the spectral domain OCT system [20] with a superluminescent diode (Thorlabs Inc.) as the light source, which has a central wavelength of 1340 nm with a bandwidth of 110 nm, providing a ~7 μm axial resolution in the air. In the sample arm, 10X scan lens (Thorlabs Inc.) was used to achieve ~7 μm lateral resolution.…”

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

“…Streaks due to motion artifacts (Fig. 2) may affect subsequent vessel segmentation procedures and quantification of morphology [64], so minimizing artifact signal while preserving vessel contrast in the image is desired. In this work, we developed an approach to minimizing these artifacts in the resulting vascular maps which consisted of identifying frames with bulk motion using an average B-scan variance threshold.…”

“…After the motion artifact frames were replaced with the average of the nearest motion-free frames, the depth projections were largely free of high intensity bands corresponding to motion. Other post-processing techniques for reducing motion artifacts include frequency rejection filters [65] and segmentation of vessels in B-scans with a classifier algorithm [64]. The former is effective for periodic motion artifacts but may be less so for removal of bulk motion that is non-periodic [64,65].…”

“…Other post-processing techniques for reducing motion artifacts include frequency rejection filters [65] and segmentation of vessels in B-scans with a classifier algorithm [64]. The former is effective for periodic motion artifacts but may be less so for removal of bulk motion that is non-periodic [64,65]. The latter can be applied to both periodic and non-periodic bulk motion, but the classifier algorithm requires a training set of manually segmented images [64].…”

“…The former is effective for periodic motion artifacts but may be less so for removal of bulk motion that is non-periodic [64,65]. The latter can be applied to both periodic and non-periodic bulk motion, but the classifier algorithm requires a training set of manually segmented images [64]. There are also several methods available for addressing the challenge of motion artifacts in speckle variance OCT during acquisition, including the use of orthogonal scan patterns [34,66], short acquisition times [62], optimizing frame rates (field of view) and gate length [43].…”