Deep Convolutional Encoder‑Decoder Neural Network Approach for Functional Near Infrared Spectroscopic Imaging

Tieni Li; Dongyuan Liu; Pengrui Zhang; Zhiyong Li; Feng Gao

doi:10.3788/CJL230734

[1] Althobaiti M, Al-Naib I. Recent developments in instrumentation of functional near-infrared spectroscopy systems[J]. Applied Sciences, 10, 6522(2020).

[2] Herold F, Wiegel P, Scholkmann F et al. Applications of functional near-infrared spectroscopy (fNIRS) neuroimaging in exercise-cognition science: a systematic, methodology-focused review[J]. Journal of Clinical Medicine, 7, 466(2018).

[3] Fishell A K, Burns-Yocum T M, Bergonzi K M et al. Mapping brain function during naturalistic viewing using high-density diffuse optical tomography[J]. Scientific Reports, 9, 11115(2019).

[4] Aihara T, Shimokawa T, Ogawa T et al. Resting-state functional connectivity estimated with hierarchical Bayesian diffuse optical tomography[J]. Frontiers in Neuroscience, 14, 32(2020).

[5] Duan L, Zhao Z P, Lin Y L et al. Wavelet-based method for removing global physiological noise in functional near-infrared spectroscopy[J]. Biomedical Optics Express, 9, 3805-3820(2018).

[6] Intes X, Ntziachristos V, Culver J P et al. Projection access order in algebraic reconstruction technique for diffuse optical tomography[J]. Physics in Medicine and Biology, 47, N1-N10(2002).

[7] Cao X, Zhang B, Wang X et al. An adaptive Tikhonov regularization method for fluorescence molecular tomography[J]. Medical & Biological Engineering & Computing, 51, 849-858(2013).

[8] Li A, Miller E L, Kilmer M E et al. Tomographic optical breast imaging guided by three-dimensional mammography[J]. Applied Optics, 42, 5181-5190(2003).

[9] Brooksby B A, Dehghani H, Pogue B W et al. Near-infrared (NIR) tomography breast image reconstruction with a priori structural information from MRI: algorithm development for reconstructing heterogeneities[J]. IEEE Journal of Selected Topics in Quantum Electronics, 9, 199-209(2003).

[10] Wang H Q, Wu N, Zhao Z et al. Diffuse optical tomography reconstruction based on deep learning[J]. Laser & Optoelectronics Progress, 57, 040003(2020).

[11] Cai C J, Deng K X, Ma C et al. End-to-end deep neural network for optical inversion in quantitative photoacoustic imaging[J]. Optics Letters, 43, 2752-2755(2018).

[12] Feng J C, Sun Q W, Li Z et al. Back-propagation neural network-based reconstruction algorithm for diffuse optical tomography[J]. Journal of Biomedical Optics, 24, 051407(2018).

[13] Liu D Y, Zhang P R, Zhang Y et al. Suppressing physiological interferences and physical noises in functional diffuse optical tomography via tandem inversion filtering and LSTM classification[J]. Optics Express, 29, 29275-29291(2021).

[14] Bonomini V, Zucchelli L, Re R et al. Linear regression models and k-means clustering for statistical analysis of fNIRS data[J]. Biomedical Optics Express, 6, 615-630(2015).

[15] Yamada Y, Okawa S. Diffuse optical tomography: present status and its future[J]. Optical Review, 21, 185-205(2014).

[16] Liu D Y, Zhang Y, Bai L et al. Combining two-layer semi-three-dimensional reconstruction and multi-wavelength image fusion for functional diffuse optical tomography[J]. IEEE Transactions on Computational Imaging, 7, 1055-1068(2021).

[17] Ding X M, Wang B Y, Liu D Y et al. Multi-channel brain functional imaging system based on lock-in photon counting[J]. Chinese Journal of Lasers, 46, 0107001(2019).