Speckle reduction in optical coherence tomography by “path length encoded” angular compounding

Abstract

Speckle, the dominant factor reducing image quality in optical coherence tomography (OCT), limits the ability to identify cellular structures that are essential for diagnosis of a variety of diseases. We describe a new high-speed method for implementing angular compounding by path length encoding (ACPE) for reducing speckle in OCT images. By averaging images obtained at different incident angles, with each image encoded by path length, ACPE maintains high-speed image acquisition and requires minimal modifications to OCT probe optics. ACPE images obtained from tissue phantoms and human skin in vivo demonstrate a qualitative improvement over traditional OCT and an increased SNR that correlates well with theory.

Angle-resolved Optical Coherence Tomography with sequential angular selectivity for speckle reduction

“…Spatial averaging in the transverse dimension could be performed in conjunction with a corresponding decrease in the focused spot size, but with this method speckle reduction would be achieved at the expense of a decreased confocal parameter. The angular compounding method of speckle reduction exploits the decorrelation of speckle with respect to the angle at which light is backscattered, thereby presenting the potential for skirting spatial resolution compromises [4][5][6][7]. This method involves incoherently averaging images that are acquired from different backscattering angles.…”

Application of Improved Homogeneity Similarity-Based Denoising in Optical Coherence Tomography Retinal Images

“…It has been shown that speckle noise is the most predominant noise source in OCT imaging [3][4][5][6][7][8][9]. Apart from other systemspecific noise like electric or shot noise, speckle is common to all OCT commercial systems [26].…”

“…In order to quantitatively measure the denoising performance with full reference metrics, synthetic noise OCT images were generated by computationally corrupting virtually noiseless clinical images using a known amount of noise. We obtained images of background noise from a commercial SD-OCT device (i.e., CirrusOCT) in order to characterize the expected noise, which was later employed to computationally alter averaged HD clinical B-scans in a signal-dependent manner (as expected with noise in SD-OCT systems [3][4][5][6][7][8][9]). The effect of corrupting the averaged HD clinical images with a more general simulated speckle noise was also investigated.…”

“…In Schmitt's paper [3], three hardwarebased speckle-reduction techniques (polarization diversity [6], spatial compounding [7,8], and frequency compounding [9]) were described. In the compounding technique, a series of images of one target are sampled at different times, with different frequencies, or in different scan directions.…”

Angular high-speed massively parallel detection spectral-domain optical coherence tomography for speckle reduction

“…However speckles provide information about both the structure of the imaged object as well as image noise. Therefore, several approaches have been developed for speckle reduction using angular compounding, [1][2][3][4] frequency compounding, 5 polarization compounding, 6 and strain compounding. 7,8 Angular compounding is performed by averaging multiple images acquired at different angles.…”