论文标题
基于修改的Kalman滤波器的光声信号和图像的自适应降压
Adaptive De-noising of Photoacoustic Signal and Image based on Modified Kalman Filter
论文作者
论文摘要
作为基于光和超声融合的迅速发展的医学成像方法，光声成像（PAI）最近在各种生物医学应用中表现出了很高的潜力，尤其是在揭示功能和分子信息以提高诊断准确性时。然而，由于激光功率有限和深层组织成像中的严重衰减引起的幅度弱和不可避免的随机噪声，PA信号通常具有低信噪比（SNR）（SNR），而重建的PA图像质量低。尽管传统的卡尔曼过滤器（KF）可以在时域消除高斯噪声，但由于其固定模型，它在实时估计条件下缺乏适应性。此外，以前在PAI中尚未使用基于KF的De-Noising算法。在本文中，我们提出了一种针对PAI DE-NOISING的自适应修饰的Kalman滤波器（MKF），通过调谐系统噪声矩阵Q和测量噪声矩阵rix rix在常规KF模型中。此外，为了补偿KF引起的信号偏斜，我们将Rauch-Tung-Striebel的向后部分降级，这也使用了新确定的Q。提供了使用幻影和离体结肠直肠组织的实验结果，以证明该算法的有效性。
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.
