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Optical coherence tomography (OCT) attenuation imaging is a technique that uses the optical attenuation coefficient (OAC) to distinguish the types or pathological states of tissues and has been increasingly used in basic research and clinical diagnosis. With the increasing application of swept-source OCT, scholars are increasingly inclined to explore deep tissues. Unfortunately, the accuracy of OAC calculation when exploring deep tissues has yet to be improved. Existing methods generally have the following problems: overestimation error, underestimation error, severe fluctuation, or stripe artifacts in the OAC calculation of the OCT tail signal. The main reason for this is that the influence of the noise floor on the OCT weak signal at the tail-end is not paid enough attention. The noise floor can change the attenuation pattern of the OCT tail signal, which can lead to severe errors in the OAC. In this paper, we proposed a Kalman filter-based OAC optimal algorithm to solve this problem. This algorithm can not only eliminate the influence of the noise floor, but can also effectively protect the weak signal at the tail-end from being lost. The OAC of deep tissues can be calculated accurately and stably. Numerical simulation, phantom, and in vivo experiments were tested to verify the algorithm's effectiveness in this paper. This technology is expected to play an essential role in disease diagnosis and in the evaluation of the effectiveness of treatment methods. [ABSTRACT FROM AUTHOR]

Optical coherence tomography (OCT) attenuation imaging is a technique that uses the optical attenuation coefficient (OAC) to distinguish the types or pathological states of tissues and has been increasingly used in basic research and clinical diagnosis. With the increasing application of swept-source OCT, scholars are increasingly inclined to explore deep tissues. Unfortunately, the accuracy of OAC calculation when exploring deep tissues has yet to be improved. Existing methods generally have the following problems: overestimation error, underestimation error, severe fluctuation, or stripe artifacts in the OAC calculation of the OCT tail signal. The main reason for this is that the influence of the noise floor on the OCT weak signal at the tail-end is not paid enough attention. The noise floor can change the attenuation pattern of the OCT tail signal, which can lead to severe errors in the OAC. In this paper, we proposed a Kalman filter-based OAC optimal algorithm to solve this problem. This algorithm can not only eliminate the influence of the noise floor, but can also effectively protect the weak signal at the tail-end from being lost. The OAC of deep tissues can be calculated accurately and stably. Numerical simulation, phantom, and in vivo experiments were tested to verify the algorithm’s effectiveness in this paper. This technology is expected to play an essential role in disease diagnosis and in the evaluation of the effectiveness of treatment methods.