Abstract

In this paper, we propose an improved deterministic regularization algorithm to handle the sparse angle data problem in optical diffraction tomography. Based on optical diffraction tomography and the deterministic regularization algorithm, the regularization iteration is performed in the space domain and the frequency domain simultaneously, which greatly reduces the computational cost. By applying piecewise-smoothness and positivity constraints as the penalty function, the missing frequency spectrum is effectively recovered and the internal refractive index distribution of the specimen is accurately reconstructed. Using simulated and experimental results, we show that the proposed regularization algorithm allows accurate refractive index reconstruction from very sparse angle data in optical diffraction tomography.

© 2015 Optical Society of America

Deterministic regularization of three-dimensional optical diffraction tomography

Yongjin Sung and Ramachandra R. Dasari
J. Opt. Soc. Am. A 28(8) 1554-1561 (2011)

Accurate image reconstruction from few-view and limited-angle data in diffraction tomography

Samuel J. LaRoque, Emil Y. Sidky, and Xiaochuan Pan
J. Opt. Soc. Am. A 25(7) 1772-1782 (2008)

Comparative study of iterative reconstruction algorithms for missing cone problems in optical diffraction tomography

JooWon Lim, KyeoReh Lee, Kyong Hwan Jin, Seungwoo Shin, SeoEun Lee, YongKeun Park, and Jong Chul Ye
Opt. Express 23(13) 16933-16948 (2015)

Improved sparse reconstruction for fluorescence molecular tomography with L_1/2 regularization

Hongbo Guo, Jingjing Yu, Xiaowei He, Yuqing Hou, Fang Dong, and Shuling Zhang
Biomed. Opt. Express 6(5) 1648-1664 (2015)

Image reconstructions in diffraction tomography from limited transmitted field data sets

Bingquan Chen, Jakob J. Stamnes, and Knut Stamnes
Appl. Opt. 39(17) 2904-2911 (2000)