Adaptive denoising of photoacoustic signal and image based on modified Kalman filter

Abstract

As a burgeoning medical imaging method based on hybrid fusion of light and ultrasound, photoacoustic imaging (PAI) has demonstrated high potential in various biomedical applications, especially in revealing the functional and molecular information to improve diagnostic accuracy. However, stemming from weak amplitude and unavoidable random noise, caused by limited laser power and severe attenuation in deep tissue imaging, PA signals are usually of low signal‐to‐noise ratio, and reconstructed PA images are of low quality. Despite that conventional Kalman filter (KF) can remove Gaussian noise in time domain, it lacks adaptability in real‐time estimation due to its fixed model. Moreover, KF‐based denoising algorithm has not been applied in PAI before. In this paper, we propose an adaptive modified KF (MKF) targeted at PAI denoising by tuning system noise matrix Q and measurement noise matrix R in the conventional KF model. Additionally, in order to compensate the signal skewing caused by MKF, we cascade the backward part of Rauch–Tung–Striebel smoother, which utilizes the newly determined Q. Finally, as a supplement, we add a commonly used differential filter to remove in‐band reflection artifacts. Experimental results using phantom and ex vivo colorectal tissue are provided to prove validity of the algorithm.

Sunflower Origin Identification Based on Multi-Source Information Fusion Technique of Kernel Extreme Learning Machine

“…Due to the low main magnetic field strength, the received spin-echo NMR signals are weak, and real echo signals can easily be drowned out by background noise, significantly impacting the accuracy and precision of subsequent sunflower origin identification models. Therefore, the experiment utilized multiple sampling averages, followed by the application of non-local means (NLMs) and Kalman Filter (KF) algorithms to suppress noise in the spin-echo NMR signals to enhance the SNR [67,68]. In the near-infrared spectroscopy (NIR) experiments, the heterogeneity in particle size and uniformity of samples led to the raw spectra containing disturbances such as fluorescence background, detector noise, and laser power fluctuations.…”

Gradient‐based adaptive wavelet de‐noising method for photoacoustic imaging in vivo

“…However, there also exists many challenges in PAI technology for wide clinical use. One of the challenges is the low SNR of PA signal [8], especially in deep tissue imaging under limited laser energy. The underlying reason is that energy conversion efficiency from light to acoustic pressure, arising from the thermoelastic mechanism, is very low (ranging from 10 −12 to 10 −8 ) [9].…”