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

TL;DR

This paper introduces an adaptive Modified Kalman Filter for photoacoustic imaging that improves noise reduction and image quality by tuning noise matrices and incorporating smoothing techniques, validated on phantom and tissue data.

Contribution

The paper presents a novel adaptive MKF approach for PAI de-noising, combining real-time noise estimation with a smoothing method, not previously applied in this context.

Findings

Enhanced SNR in reconstructed images

Effective noise suppression in deep tissue imaging

Validated on phantom and biological tissue data

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 recently, 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 (SNR), 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 estimating condition due to its fixed model. Moreover, KF-based de-noising algorithm has not been applied in PAI before. In this paper, we propose an adaptive Modified Kalman Filter (MKF) targeted at PAI de-noising 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 KF, we cascade the backward part of Rauch-Tung-Striebel smoother (BRTS), which also 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 the validity of the algorithm.