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Optical coherence tomography (OCT) [1] is a minimally invasive imaging technique, based on low-coherence interferometry, that utilizes the spatial and temporal coherence properties of optical waves backscattered from biological tissue. Given the high level of resolution (close to cellular) and non-invasiveness that can be achieved using OCT, a very promising application is in the in-vivo imaging of the retina for studying physiological processes as well as detecting retinal dystrophies in a clinical setting. Recent advances in swept source OCT (SS-OCT) and spectral domain OCT (SD-OCT) technology has resulted in image acquisition rates of hundreds to millions of A-scans per second [2, 3]. The obvious advantages of the high data acquisition rates are the ability to image larger volumes of the imaged retina with sufficiently high pixel density in 3D, to allow for simultaneous visualizationof small and large scale morphological details in the retina, to track fast physiological processes in biological tissue, as well as to reduce the effect of motion artefacts resulting from natural motion in living biological tissue that can affect the quality of the retinal imaging.
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One of the key challenges to rapid retinal OCT acquisitions is the increasing presence of noise as acquisition speed increases. Since OCT is based on the detection of partially coherent light, speckle noise is an inherent component of any OCT tomogram [4]. The presence of speckle results in a grainy appearance of the OCT images, which can blur the boundaries between features in the image with different structural or optical properties, or even obscure structural details of small size or low reflectivity. Moreover, the presence of speckle can affect negatively the performance of other image processing algorithms such as feature segmentation [5] and pattern recognition. Since speckle contains both information about the structure and optical properties of the imaged object and a noise component, different approaches were utilized in the past to suppress speckle noise and improve the image quality [4, 6, 7, 8].
The presence of speckle noise is made worse by rapid OCT acquisitions, since the OCT signal-to-noise ratio (SNR) is directly proportional to the integration time of the signal detection and thus inversely proportional to the image acquisition rate [2, 9, 10], OCT imaging at the rate of hundreds of kHz or tens of MHz results in a significant drop in the image SNR. Therefore, morphological features in imaged biological tissue samples such as retinal tissue layers, small blood vessels, lipid deposits, etc, can be blurred or obscured by the presence of noise in unprocessed OCT images. Therefore, speckle noise reduction has drawn significant interest from the OCT community, since it can improve the image SNR and contrast, provide better visualization of morphological features in biological tissue that could be of clinical diagnostic value, as well as potentially improve the precision and overall performance of the other image post-processing algorithms such as layer segmentation, registration, cell detection, etc.
In general, these approached can be divided into two categories: instrumentation and software. Given the complexity, cost, and relatively limited gain in modifying the instrumentation to reduce the presence of noise, much attention has been focused on the software front. Previous studies on reducing speckle noise can be categorized into two groups: multi-frame averaging and digital image denoising approaches. The first strategy was mainly used for post-processing, where a sequence of B-scan images from a unique position are first captured, then registered and averaged to get a high SNR image [11, 12]. Recently, a quantitative comparison of frame averaging approaches has been performed by Eichel et al. [13]. Some SD-OCT systems have a built-in registration and averaging system to do this post-processing progress automatically, such as Spectralis (Heidelberg Engineering, Heidelberg, Germany), which can help improve the image SNR directly.
Frame averaging has been proven to be simple and effective [Software Downloads for 'Rapidshare Premium. RapidShare Premium Download at the maximum. 3.0 and SharePoint Server 2007 users who wants to. Problemy z serwerami do uploadu m.in. Click this link to download this release as mp3 or flac.
11, 14], however, it has two significant drawbacks:- 1.
It results in overall increased imaging time since multiple B-scans must be acquired at the same location,
- 2.
Precise image registration needs to be applied prior to averaging, which is time consuming and can lead to blurring in the frame-averaged tomogram if done incorrectly.
Another approach would be to use standard digital image denoising technologies to suppress speckle noise. An extensive comparison of standard digital denoising methods has been performed by Ozcan et al. [15]. Classic denoising algorithms often assume a priori parametric or non-parametric model for signal and noise, and operate on the reconstructed OCT tomogram in the spatial domain from a single acquisition to suppress noise. Some methods include adaptive non-linear filtering strategies [6, 16, 17, 18, 19], or wavelet filtering strategies [20, 21, 22]. More complicated wavelet thresholding denoising approaches such as dual tree complex wavelet transformation [23] and curvelets transformations [24], are able to generate satisfactory results in terms of improved image SNR with tolerable blurring. More recently, a weighted wavelet multiframe reconstruction algorithm was proposed [25] and used for preprocessing OCT for retinal layer segmentation, and a denoising algorithm was introduced based on a sparse representation dictionary approach [26, 27]. However, all these denoising methods have the disadvantage that they have been designed to work only in the spatial domain, and therefore they do not take into account the inherent characteristics of the measured spectral signal from a SD-OCT system, which can lead to reduced performance in maintaining signal fidelity. A very interesting approach that was more recently taken was that is capable of not only reducing noise but also interpolate missing data using sparse representation dictionaries constructed from previously collected datasets [27].
In this paper, a noise-compensated homotopic modified James-Stein non-local reconstruction (NCHR) framework is introduced to improve the reconstruction of rapid retinal OCT image acquisitions, that can result in SNR and contrast-to-noise (CNR) improvements while preserving the sharpness and visibility of structural details in the reconstructed tomogram. The framework’s performance was tested on a series of human retinal OCT tomograms acquired in-vivo and was compared quantitatively with the performance of some of the most advanced published denoising approaches. It is important to note that, while it builds upon a homotopic reconstruction framework as with our previous work on sparse reconstruction [28], there are significant differences between the proposed work and our previous work, and as such highlights the main novel contributions of the proposed work:- 1.
The work presented in [28] is designed for reconstructing OCT imagery from sparse spectral data acquired using compressed sensing, where a random sampling pattern is used to acquire incomplete measurements in the spectral domain. Since the acquisitions are made at regular scanning speed, the individual sparse measurements that were made have relatively higher SNR compared to that in this proposed work. Therefore, the goal of [28] is to reconstruct based on missing information, with the aim to allow for high resolution OCT imagery with limited camera pixels. However, the methodology in the proposed work is designed for reconstructing OCT imagery from rapidly acquired fully-sampled spectral data, where the scan speed is high and thus the amount of light captured at each scan is much lower than that in the sparse measurements case. Therefore, the goal of this work is to reconstruct based on fully-sampled but low-SNR acquisitions, with the aim to allow for rapid OCT imaging with higher effective SNR.
- 2.
While both employ a homotopic minimization framework, the proposed work introduces a modified James-Stein non-local regularization strategy, while a conventional non-local regularization strategy is employed in [28]. As such, the proposed work is different and novel from an algorithmic standpoint as well relative to [28].
- 3.
The proposed work incorporates a noise compensation strategy into the proposed homotopic modified James-Stein non-local regularized minimization framework to account for the noise characteristics of the underlying system.
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The rest of the paper is organized as follows. First, the underlying methodology behind the proposed use of a homotopic modified James-Stein non-local regularization (NCHR) reconstruction framework for the reconstruction of rapid OCT tomograms is described in Section “Methods”. The experimental results using rapid in-vivo acquisitions of the human retina are presented and discussed in Section “Experiments”. Finally, conclusion are drawn and future work is discussed in Section“Conclusion”.