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As a burgeoning medical imaging method based on hybrid fusion of light andultrasound, photoacoustic imaging (PAI) has demonstrated high potential invarious biomedical applications recently, especially in revealing thefunctional and molecular information to improve diagnostic accuracy. However,stemming from weak amplitude and unavoidable random noise, caused by limitedlaser power and severe attenuation in deep tissue imaging, PA signals areusually of low signal-to-noise ratio (SNR), and reconstructed PA images are oflow quality. Despite that conventional Kalman Filter (KF) can remove Gaussiannoise in time domain, it lacks adaptability in real-time estimating conditiondue to its fixed model. Moreover, KF-based de-noising algorithm has not beenapplied in PAI before. In this paper, we propose an adaptive Modified KalmanFilter (MKF) targeted at PAI de-noising by tuning system noise matrix Q andmeasurement noise matrix R in the conventional KF model. Additionally, in orderto compensate the signal skewing caused by KF, we cascade the backward part ofRauch-Tung-Striebel smoother (BRTS), which also utilizes the newly determinedQ. Finally, as a supplement, we add a commonly used differential filter toremove in-band reflection artifacts. Experimental results using phantom and exvivo colorectal tissue are provided to prove the validity of the algorithm.

Adaptive De-noising of Photoacoustic Signal and Image based on Modified Kalman Filter