Optical coherence tomography (OCT) is a pivotal biomedical imaging technique based on the low coherence interference principle. It facilitates the production of tomographic scans of biological tissues, extensively applied to medical fields such as ophthalmology and dermatology. However, the pursuit of heightened axial resolution compels OCT systems to harness broadband light sources, and it is an approach that inadvertently introduces dispersion effects and gives rise to imaging artifacts, blurring, and consequently diminished image quality. Therefore, it is necessary to conduct dispersion compensation in OCT systems. While hardware-based compensation techniques are plagued by increased costs and complexity, their efficacy remains limited, which spurs the exploration and application of more flexible dispersion compensation algorithms. However, commonly employed algorithms based on search strategies suffer from suboptimal adaptability and concealed computational intricacies. Thus, we introduce an innovative dispersion compensation algorithm established based on the concept of spatial pulse degradation resulting from dispersion. The algorithm integrated into frequency domain OCT system experiments eliminates the requirements for manual dispersion range adjustments. Meanwhile, it features notable computational efficiency to offset the shortcomings of conventional search strategies in adaptability and computational efficacy. The proposed method is proven to be instrumental in enhancing the engineering practicality of OCT systems and improving the quality of tomographic images.

We propose an efficient dispersion compensation algorithm grounded in spatial pulse degradation due to dispersion and apply it to frequency domain OCT system experiments. The algorithm consists of two parts including dispersion extraction and compensation. By adopting the principle that dispersion causes widening spatial pulse, the algorithm estimates the dispersion of the signal to be corrected and subsequently applies compensation. A linear equation establishes the relationship between the square of spatial pulse width and the square of second-order dispersion. Additional dispersion phases are generated numerically and integrated into the original spectral signal to yield new dispersion signals. After transformation to the spatial domain, these signals' spatial pulse widths are measured. By substituting these pulse width values into the equation set, the second-order dispersion of the original signal can be calculated. Finally, a dispersion compensation phase is constructed and incorporated into the original spectral signal's phase for dispersion correction.

To validate the efficacy of this algorithm, we devise a swept source OCT (SS-OCT) system for data collection. The method is applied to correct dispersion in the point spread function (PSF) of the system and biological tissue images. The experimental results show that the algorithm's dispersion estimates exhibit a relative error of less than 10% when compared to actual dispersion values in different dispersion conditions (Table 1). After implementing this algorithm for dispersion compensation, notable enhancements are observed in the system's peak signal-to-noise ratio and axial resolution. In scenarios of similar correction efficiency, this algorithm surpasses the commonly employed iterative method by a factor of 5 in terms of speed and outpaces the fractional Fourier transform method by a remarkable 50-fold (Table 2). Furthermore, after applying dispersion compensation, the image quality is notably improved. The grape flesh image boundaries exhibit enhanced sharpness, with significantly enhanced internal tissue clarity and more concentrated image energy (Fig. 4). Additionally, human retinal images display clearer layer differentiation, accompanied by image contrast improvement (Fig. 5). These results collectively prove the algorithm's efficacy in enhancing image quality.

We introduce a novel high-efficiency dispersion compensation algorithm grounded in spatial pulse width. The algorithm mitigates axial broadening in PSF and enhances the system's signal-to-noise ratio. Notably, the algorithm's strength lies in its independence from prior knowledge about system dispersion or manual dispersion search interval selection. It accurately estimates system dispersion, and when compared with other search strategy-based algorithms, it demonstrates superior computing efficiency and achieves comparable compensation efficacy. The dispersion compensation experiments conducted on grape pulp and human retinal images yield effective results. The algorithm suppresses axial broadening blur, amplifies image contrast, and elucidates intricate structural features within biological tissues. These outcomes underscore the algorithm's capacity to proficiently rectify dispersion issues in OCT systems, thereby enhancing visual image quality. Nevertheless, certain limitations deserve consideration. Primarily, the algorithm's applicability is confined to addressing second-order dispersion, and higher-order dispersion tackling necessitates further exploration into the numerical relationship between spatial pulse distortion and higher-order dispersion. Furthermore, the algorithm exclusively addresses system dispersion, ignoring sample dispersion intricacies tied to specific sample structures and depths. Future research should explore depth-adaptive sample dispersion compensation, and leverage the algorithm's high computational efficiency to potentially enable depth-dependent dispersion compensation.