Abstract
We applied compressed sensing (CS) to spectral domain optical coherence tomography (SD OCT) and studied its effectiveness. We tested the CS reconstruction by randomly undersampling the k-space SD OCT signal. We achieved this by applying pseudo-random masks to sample 62.5%, 50%, and 37.5% of the CCD camera pixels. OCT images are reconstructed by solving an optimization problem that minimizes the l 1 norm of a transformed image to enforce sparsity, subject to data consistency constraints. CS could allow an array detector with fewer pixels to reconstruct high resolution OCT images while reducing the total amount of data required to process the images.
Highlights
Optical coherence tomography (OCT) has been used widely in medical diagnosis and research [1,2,3,4,5]
We explore the potential of using compressed sensing for Spectral domain OCT (SD OCT) (CS SD OCT), which could reduce the burden of using a large pixel array camera and reduce the amount of data required and subsequent processing for high-resolution image reconstruction
Using fully sampled spectral data from CCD and applying the standard SD OCT image processing algorithm, we obtained the image in Fig. 7(a), which will be used as a ground truth image
Summary
Optical coherence tomography (OCT) has been used widely in medical diagnosis and research [1,2,3,4,5]. The camera has to have enough pixels to guarantee that at least two data points are sampled within one period of the spectral interferogram. Such CCD or CMOS cameras and associated electronics are usually expensive and limit the imaging speed. We explore the potential of using compressed sensing for SD OCT (CS SD OCT), which could reduce the burden of using a large pixel array camera and reduce the amount of data required and subsequent processing for high-resolution image reconstruction
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