We present an optical coherence tomography (OCT) imaging system that effectively compensates unwanted axial motion with micron-scale accuracy. The OCT system is based on a swept-source (SS) engine (1060-nm center wavelength, 100-nm full-width sweeping bandwidth, and 100-kHz repetition rate), with axial and lateral resolutions of about 4.5 and 8.5 microns respectively. The SS-OCT system incorporates a distance sensing method utilizing an envelope-based surface detection algorithm. The algorithm locates the target surface from the B-scans, taking into account not just the first or highest peak but the entire signature of sequential A-scans. Subsequently, a Kalman filter is applied as predictor to make up for system latencies, before sending the calculated position information to control a linear motor, adjusting and maintaining a fixed system-target distance. To test system performance, the motioncorrection algorithm was compared to earlier, more basic peak-based surface detection methods and to performing no motion compensation. Results demonstrate increased robustness and reproducibility, particularly noticeable in multilayered tissues, while utilizing the novel technique. Implementing such motion compensation into clinical OCT systems may thus improve the reliability of objective and quantitative information that can be extracted from OCT measurements.
Many prior studies performed in the area of compressive optical coherence tomography (OCT) have mostly dealt with the problem of compressive sensing and sparse recovery of processed OCT images. Unlike these studies, in this paper, we study the application of compressive sensing in terms of efficient data storage and generating OCT images from undersampled raw unprocessed spectral domain OCT data. High resolution spectral domain OCT requires acquisition of enormous amount of data at very high sampling rate but such a large amount of the raw data impedes fast and efficient data storage and communication. To solve the problem of storing a large amount of data, we propose a specific undersampling method guided by the energy density of the spectral domain data in order to facilitate sparse representation of the raw data in terms of its salient frequency domain samples. This method takes into account not just the higher amplitude spectral data, as suggested in some previous studies but samples data based on nearly uniform distribution of energy over all the sampling intervals in the entire spectrum. Finally, we apply some state of the art sparse recovery methods involving L1 minimization to recover our desired high resolution images from the undersampled spectral domain data. We demonstrate the performance of our proposed scheme by comparing it with the recovery accuracy of some recent energy-guided undersampling methods and the conventional compressive sensing with random undersampling. We also compare the performance of our method with the other methods in terms of data compression ratio with respect to the reconstruction error.
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