Deconvolution Techniques in Optical Coherence Tomography: Advancements, Challenges, and Future Prospects

Abstract

Optical coherence tomography (OCT) has become a breakthrough in medical imaging, providing high-resolution, non-invasive visualization of tissue microstructures. However, resolution degradation caused by the system's imperfect point spread function (PSF) necessitates advanced deconvolution algorithms. Despite progress, a comprehensive review of these techniques in the OCT field is lacking. This review examines the current state of deconvolution strategies to overcome the PSF-induced blurring and discusses their applications, challenges, potential solutions, and future directions. By surveying previous works, this work identifies technical milestones and categorizes them accordingly. Deconvolution in OCT continues to evolve, contributing to both fundamental research and biological imaging. Despite significant improvements in image resolution and signal-to-noise ratio (SNR), challenges remain in real-time clinical imaging, including optimization robustness and balancing accuracy with computational complexity. Future developments need to address these challenges, emphasizing the importance of comprehensive methodologies that integrate deconvolution with other image enhancement solutions like noise removal. Interdisciplinary collaboration and validation are crucial. The future of OCT deconvolution is rich with opportunities, from combining real-time artificial intelligence (AI)-aided OCT and multi-modal imaging to exploring complex algorithms with growing computational capabilities. As clinical adoption nears, rigorous testing and ethical diligence remain essential to fully realize the potential of OCT deconvolution techniques.