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Noise-compensated homotopic non-local regularized reconstruction for rapid retinal optical coherence tomography image acquisitions
© Liu et al.; licensee BioMed Central Ltd. 2014
- Received: 4 June 2014
- Accepted: 3 October 2014
- Published: 15 October 2014
Optical coherence tomography (OCT) is a minimally invasive imaging technique, which utilizes the spatial and temporal coherence properties of optical waves backscattered from biological material. Recent advances in tunable lasers and infrared camera technologies have enabled an increase in the OCT imaging speed by a factor of more than 100, which is important for retinal imaging where we wish to study fast physiological processes in the biological tissue. However, the high scanning rate causes proportional decrease of the detector exposure time, resulting in a reduction of the system signal-to-noise ratio (SNR). One approach to improving the image quality of OCT tomograms acquired at high speed is to compensate for the noise component in the images without compromising the sharpness of the image details.
In this study, we propose a novel reconstruction method for rapid OCT image acquisitions, based on a noise-compensated homotopic modified James-Stein non-local regularized optimization strategy. The performance of the algorithm was tested on a series of high resolution OCT images of the human retina acquired at different imaging rates.
Quantitative analysis was used to evaluate the performance of the algorithm using two state-of-art denoising strategies. Results demonstrate significant SNR improvements when using our proposed approach when compared to other approaches.
A new reconstruction method based on a noise-compensated homotopic modified James-Stein non-local regularized optimization strategy was developed for the purpose of improving the quality of rapid OCT image acquisitions. Preliminary results show the proposed method shows considerable promise as a tool to improve the visualization and analysis of biological material using OCT.
- Optical Coherence Tomography
- Optical Coherence Tomography Image
- Dual Tree Complex Wavelet Transformation
- Complex Wavelet Transformation
- Noise Compensation
Optical coherence tomography (OCT)  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.
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 . 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  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, 6–8].
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. . 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.
It results in overall increased imaging time since multiple B-scans must be acquired at the same location,
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. . 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, 16–19], or wavelet filtering strategies [20–22]. More complicated wavelet thresholding denoising approaches such as dual tree complex wavelet transformation  and curvelets transformations , 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  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 .
The work presented in  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  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.
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 . As such, the proposed work is different and novel from an algorithmic standpoint as well relative to .
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.
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”.
The main principle behind the proposed approach is the introduction of a modified James-Stein non-local regularization strategy and noise compensation within a homotopic minimization framework.
Adjacent patches in a neighborhood should be similar;
The reconstructed signal should be in reasonable proximity to the measurements in the k-space domain.
To evaluate the effectiveness of the proposed method we applied it to the reconstruction of a series of rapid human retinal OCT cross-sectional image acquisitions (Figure 1). The tomograms were acquired with a research grade, high-speed, UHROCT system , operating at 1060 nm wavelength, that utilizes a super-luminescent diode (λ-c = 1020 nm, δ λ = 110 nm, P out = 10 mW), a 47 kHz InGaAs linear array, and a 1024 pixel camera (SUI, Goodrich) interfaced with a high performance spectrometer. The UHROCT system provides 6 μm resolution and 97 dB SNR for 1.5 mW of optical power incident on the cornea. Cross-sectional retinal tomograms were acquired from the foveal region of the retinas of healthy subjects using an imaging procedure that was approved by the University of Waterloo Office of Research Ethics. Written informed consent for participation in the study was obtained from the subjects. Each retinal tomogram was comprised of 1000 A-scans, each of 512 pixels. The raw OCT data was processed to generate images with SNR and CNR corresponding to the original data acquisition rate of 47 kHz, as well as corresponding to significantly higher scanning rates of 188 kHz (=4×47 kHz) and 376 kHz (=8×47 kHz) (simulated). In the implementation of NCHR, the parameters were set to σ 1=0.3, and λ=0.7, and a patch size of 9×9 and neighborhood size of 21×21. The parameters used in the implementation of NCHR were found to provide strong results based on extensive empirical testing.
Therefore, based on both quantitative SNR and CNR analysis as well as qualitative visual assessment, NCHR provided improved reconstruction performance and visual quality compared to all other algorithms. Finally, we performed a performance analysis between the modified James-Stein homotopic non-local regularization strategy and the conventional homotopic non-local regularization strategy within the context of the proposed reconstruction framework, and found that the modified James-Stein homotopic non-local regularization strategy to achieve an average SNR and CNR increase of 1.3 dB and 0.02 dB, respectively, across scan speeds compared to the conventional homotopic non-local regularization strategy.
A novel noise-compensation approach based on homotopic, non-local regularization was presented for reconstructing images from rapid retinal OCT acquisitions. Results show that the proposed approach is able to achieve a significantly higher signal-to-noise ratio and better visual quality under all different scan speeds, thus illustrating the potential for obtaining high resolution images with lower equipment costs and reduced imaging times. Future work involves the study of the proposed approach for rapid corneal OCT acquisitions, which entails the investigation of different parameters and potentially modifications to the optimization framework.
This work was supported by the Natural Sciences and Engineering Research Council (NSERC) of Canada, the Canada Research Chairs program, the Canadian Institutes for Health Research, the Ontario Ministry of Economic Development and Innovation, and the Chinese Council Scholarship.
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