Dynamic fluorescence diffuse optical tomography using the adaptive EKF and GRNN-based learning

Xin Wang; Yuhong Liu; Jianwei Zhang

doi:10.1117/12.2608006

3 March 2022 Dynamic fluorescence diffuse optical tomography using the adaptive EKF and GRNN-based learning

Xin Wang, Yuhong Liu, Jianwei Zhang

Proceedings Volume 11974, Biomedical Applications of Light Scattering XII; 1197409 (2022) https://doi.org/10.1117/12.2608006
Event: SPIE BiOS, 2022, San Francisco, California, United States

Conference Poster

Abstract

Dynamic fluorescence diffuse optical tomography (FDOT) can provide contrast-enhanced and comprehensive information for tumor diagnosis and staging. Based on a two-compartmental model, the adaptive extended Kalman filtering (EKF)for dynamic FDOT is proposed in our previous work to obtain better estimation than the conventional EKF. However, the set of the measurement error covariance matrix still affect the performance of the adaptive-EKF that is always manually adjusted. In order to quickly and accurately obtain the measurement covariance matrix, this paper uses generalized regression neural network (GRNN) to predict the value in different measurements as the initial covariance value of the adaptive-EKF. The simulation results have proved the validity of the measurement covariance matrix predicted by GRNN, and the images of fluorescence pharmacokinetic parameters can be achieved excellently using the adaptive-EKF and GRNN-learning.

Citation Download Citation

Xin Wang, Yuhong Liu, and Jianwei Zhang "Dynamic fluorescence diffuse optical tomography using the adaptive EKF and GRNN-based learning", Proc. SPIE 11974, Biomedical Applications of Light Scattering XII, 1197409 (3 March 2022); https://doi.org/10.1117/12.2608006

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