Cherenkov-Excited Luminescence Scanned Tomography Reconstruction Based on Approximate Message Passing

Feng Jinchao; Chang Di; Li Zhe; Sun Zhonghua; Jia Kebin

doi:10.3788/CJL202047.0207027

[1] Weissleder R. Molecular imaging: exploring the next frontier[J]. Radiology, 212, 609-614(1999).

[2] Zhang R X. D'souza A V, Gunn J R, et al. Cherenkov-excited luminescence scanned imaging[J]. Optics Letters, 40, 827-830(2015).

[3] Pogue B W, Feng J C. LaRochelle E P, et al. Maps of in vivo oxygen pressure with submillimetre resolution and nanomolar sensitivity enabled by Cherenkov-excited luminescence scanned imaging[J]. Nature Biomedical Engineering, 2, 254-264(2018).

[4] Lin H Y, Zhang R X, Gunn J R et al. Comparison of Cherenkov excited fluorescence and phosphorescence molecular sensing from tissue with external beam irradiation[J]. Physics in Medicine and Biology, 61, 3955-3968(2016).

[5] Ruggiero A, Holland J P, Lewis J S et al. Cerenkov luminescence imaging of medical isotopes[J]. Journal of Nuclear Medicine, 51, 1123-1130(2010).

[6] Feng J C, Bruza P, Dehghani H et al. Cherenkov-excited luminescence sheet imaging (CELSI) tomographic reconstruction[J]. Proceedings of SPIE, 10049, 1004912(2017).

[7] Bect J, Blanc-Féraud L, Aubert G et al. A l1-unified variational framework for image restoration[M]. ∥Lecture Notes in Computer Science. Heidelberg: Springer Berlin Heidelberg, 1-13(2004).

[8] He X W, Wei X, Cao X et al. A novel single-view Cerenkov luminescence tomography method based on fuzzy C-means clustering[J]. Acta Optica Sinica, 38, 0717001(2018).

[9] 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).

[10] Adeyemi T, Davies M. Sparse representations of images using overcomplete complex wavelets. [C]∥IEEE/SP 13th Workshop on Statistical Signal Processing, 2005, July 17-20, 2005. Bordeaux, France. IEEE(2005).

[11] Donoho D L, Elad M. Optimally sparse representation in general (nonorthogonal) dictionaries via 1 minimization[J]. Proceedings of the National Academy of Sciences of the United States of America, 100, 2197-2202(2003).

[12] Figueiredo M A T, Nowak R D, Wright S J. Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems[J]. IEEE Journal of Selected Topics in Signal Processing, 1, 586-597(2007).

[13] Daubechies I. Defrise M, de Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint[J]. Communications on Pure and Applied Mathematics, 57, 1413-1457(2004).

[14] Bioucas-Dias J M, Figueiredo M A T. A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration[J]. IEEE Transactions on Image Processing, 16, 2992-3004(2007).

[15] Ishimaru A. Diffusion of light in turbid material[J]. Applied Optics, 28, 2210-2215(1989).

[16] Donoho D L, Maleki A, Montanari A. Message-passing algorithms for compressed sensing[J]. Proceedings of the National Academy of Sciences of the United States of America, 106, 18914-18919(2009).

[17] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 52, 1289-1306(2006).

[18] Lü Y, Tian J, Cong W X et al. A multilevel adaptive finite element algorithm for bioluminescence tomography[J]. Optics Express, 14, 8211-8223(2006).

[19] Yuan Z, Jiang H B. Three-dimensional finite element-based photoacoustic tomography: initial results[J]. Proceedings of SPIE, 6437, 64371A(2007).

[20] Cong A X, Wang G. A finite-element-based reconstruction method for 3D fluorescence tomography[J]. Optics Express, 13, 9847-9857(2005).

[21] Zhang H B, Geng G H, Wang X D et al. Fast and robust reconstruction for fluorescence molecular tomography via L1,2 regularization[J]. BioMed Research International, 2016, 1-9(2016).

[22] Frey B J, Kschischang F R, Loeliger H A et al. Factor graphs and algorithms. [C]∥Proceedings of the Annual Allerton Conference on Communication Control and Computing, 666-680(1997).

[23] Bayati M, Montanari A. The dynamics of message passing on dense graphs, with applications to compressed sensing[J]. IEEE Transactions on Information Theory, 57, 764-785(2011).

[24] Frey B J. MacKay D J C. A revolution: belief propagation in graphs with cycles. [C]∥Proceedings of Neural Information Processing Systems, 479-485(1997).

[25] Donoho D L, Maleki A, Montanari A. Message passing algorithms for compressed sensing: I. motivation and construction. [C]∥IEEE Information Theory Workshop, 2010(2010).