Statistically optimal image reconstruction for multi-detector intensity diffraction tomography

Yin Huang; Mark A. Anastasio

doi:10.1117/12.682987

5 September 2006 Statistically optimal image reconstruction for multi-detector intensity diffraction tomography

Yin Huang, Mark A. Anastasio

Proceedings Volume 6316, Image Reconstruction from Incomplete Data IV; 63160K (2006) https://doi.org/10.1117/12.682987
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States

Abstract

Intensity diffraction tomography (I-DT) is an in-line holographic imaging method for reconstructing the three-dimensional complex refractive index distribution of a weakly scattering object. Because it circumvents the phase retrieval problem of diffraction tomography, I-DT reconstruction methods may benefit a range of imaging problems involving optical and coherent X-ray radiation. In this work, we investigate the use of statistically complementary data, provided by multiple (> 2) in-line intensity measurements, for effective suppression of image noise in I-DT. The noise properties of the reconstructed images are demonstrated to depend strongly on the specification of measurement geometry. The effects of experimental uncertainties on the performance of I-DT is investigated also. Computer-simulation studies that are representative of a tomographic microscopy implementation of I-DT are presented.

Citation Download Citation

Yin Huang and Mark A. Anastasio "Statistically optimal image reconstruction for multi-detector intensity diffraction tomography", Proc. SPIE 6316, Image Reconstruction from Incomplete Data IV, 63160K (5 September 2006); https://doi.org/10.1117/12.682987

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