Diffuse Optical Tomography Reconstruction Based on Deep Learning

Huiquan Wang; Nian Wu; Zhe Zhao; Guang Han; Jinhai Wang

doi:10.3788/LOP57.040003

[1] Boas D A, Brooks D H, Miller E L et al. Imaging the body with diffuse optical tomography[J]. IEEE Signal Processing Magazine, 18, 57-75(2001).

[2] Hielscher A H, Bluestone A Y, Abdoulaev G S et al. Near-infrared diffuse optical tomography[J]. Disease Markers, 18, 313-337(2002).

[3] Dehghani H, Srinivasan S, Pogue B W et al. Numerical modelling and image reconstruction in diffuse optical tomography[J]. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 367, 3073-3093(2009).

[4] Eggebrecht A T, Ferradal S L, Robichaux-Viehoever A et al. Mapping distributed brain function and networks with diffuse optical tomography[J]. Nature Photonics, 8, 448-454(2014).

[5] Lee C W, Cooper R J, Austin T. Diffuse optical tomography to investigate the newborn brain[J]. Pediatric Research, 82, 376-386(2017).

[6] Srinivasan S, Pogue B W, Jiang S D et al. Validation of hemoglobin and water molar absorption spectra in near-infrared diffuse optical tomography[J]. Proceedings of SPIE, 4955, 407-415(2003).

[7] Choe R, Corlu A, Lee K et al. Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI[J]. Medical Physics, 32, 1128-1139(2005).

[8] Arridge S R, Schotland J C. Optical tomography: forward and inverse problems[J]. Inverse Problems, 25, 123010(2009).

[9] Engl H W, Hanke M, Neubauer A. Regularization of inverse problems[M]. Netherlands: Kluwer Academic Publishers, 347-366(2000).

[10] Kaczmarz S. Approximate solution of systems of linear equations[J]. International Journal of Control, 57, 1269-1271(1993).

[11] Gordon R, Bender R, Herman G T. Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography[J]. Journal of theoretical Biology, 29, 471-481(1970).

[12] Arridge S R, Schweiger M. Photon-measurement density functions Part 2: finite-element-method calculations[J]. Applied Optics, 34, 8026-8037(1995).

[13] 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).

[14] Brendel B, Ziegler R, Nielsen T . Algebraic reconstruction techniques for spectral reconstruction in diffuse optical tomography[J]. Applied Optics, 47, 6392-6403(2009).

[15] Yalavarthy P K, Pogue B W, Dehghani H et al. Structural information within regularization matrices improves near infrared diffuse optical tomography[J]. Optics Express, 15, 8043-8058(2007).

[16] Wang B Y, Chen W T, Ma W J et al. Reconstruction method of breast diffuse optical tomography based on non-negative-constraint L1-norm regularization[J]. Acta Optica Sinica, 36, 1117002(2016).

[17] Cao N N, Nehorai A, Jacobs M. Image reconstruction for diffuse optical tomography using sparsity regularization and expectation-maximization algorithm[J]. Optics Express, 15, 13695-13708(2007).

[18] Okawa S, Hoshi Y, Yamada Y. Improvement of image quality of time-domain diffuse optical tomography with Ip sparsity regularization[J]. Biomedical Optics Express, 2, 3334-3348(2011).

[19] Morisue M, Sakai K, Koinuma H. Neural networks for computed tomography. [C]∥1992 IEEE International Symposium on Circuits and Systems, May 10-13, 1992, San Diego, CA, USA. New York: IEEE, 2893-2896(1992).

[20] Munley M T, Floyd C E. Jr, Bowsher J E, et al. An artificial neural network approach to quantitative single photon emission computed tomographic reconstruction with collimator, attenuation, and scatter compensation[J]. Medical Physics, 21, 1889-1899(1994).

[21] Wu Q, Qian Z Y, Gu Y Q. An optimization algorithm to inverse problem in 2-D optical computed tomography by BP neural network. [C]∥2006 International Symposium on Biophotonics, Nanophotonics and Metamaterials, October 16-18, 2006, Hangzhou, China. New York: IEEE, 10252141(2006).

[22] Kang E, Min J H, Ye J C. A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction[J]. Medical Physics, 44, e360-e375(2017).

[23] Zhang Y G, Yi B S, Wu C Y et al. Low-dose CT image denoising method based on convolutional neural network[J]. Acta Optica Sinica, 38, 0410003(2018).

[24] Adler J, Öktem O. Solving ill-posed inverse problems using iterative deep neural networks[J]. Inverse Problems, 33, 124007(2017).

[25] 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).

[26] Sun Y, Xia Z H, Kamilov U S. Efficient and accurate inversion of multiple scattering with deep learning[J]. Optics Express, 26, 14678-14688(2018).

[27] Gao Y, Wang K, An Y et al. Nonmodel-based bioluminescence tomography using a machine-learning reconstruction strategy[J]. Optica, 5, 1451-1454(2018).

[28] Guo L, Liu F, Cai C J et al. 3D deep encoder-ecoder network for fluorescence molecular tomography[J]. Optics Letters, 44, 1892-1895(2019).

[29] Yoo J, Sabir S, Heo D et al. -12-04)[2019-05-28]. https:∥arxiv.gg363., site/abs/1712, 00912(2017).

[30] Ben Yedder H. BenTaieb A, Shokoufi M, et al. Deep learning based image reconstruction for diffuse optical tomography[M]. ∥Knoll F, Maier A, Rueckert D. Machine learning for medical image reconstruction. Lecture notes in computer science. Cham: Springer, 11074, 112-119(2018).

[31] Xu Q W, Wang X, Jiang H B. Convolutional neural network for breast cancer diagnosis using diffuse optical tomography[J]. Visual Computing for Industry, Biomedicine, and Art, 2, 1(2019).