@inproceedings{2ee698439e7c495b971486ab2ebc4a79,
title = "Non-Linear 3d Reconstruction for Compressive X-Ray Tomosynthesis",
abstract = "Compressive X-ray tomosynthesis is an emerging technology that can be used for medical diagnosis, safety inspection and industrial non-destructive testing. Traditional compressive X-ray tomosynthesis uses sequential illumination to interrogate the object of interest, which generates non-overlapping projection measurements. However, when multiple X-ray sources emit simultaneously, the projections corresponding to different sources overlap creating multiplexed measurements. Such measurement strategy leads to a non-linear reconstruction problem. The reconstruction of the three-dimensional (3D) object from multiplexed projections is an important problem. This paper proposes a non-linear 3D reconstruction approach for compressive X-ray tomosynthesis, where a set of coding masks are used to generate structured illumination and reduce radiation dose. The effectiveness of the method is verified by simulation experiments. It is shown that the proposed approach can reconstruct high-quality images with just a few snapshots.",
keywords = "X-ray tomosynthesis, coding mask, compressed sensing, non-linear reconstruction",
author = "Qile Zhao and Xu Ma and Angela Cuadros and Arce, {Gonzalo R.} and Rui Chen",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Image Processing, ICIP 2020 ; Conference date: 25-09-2020 Through 28-09-2020",
year = "2020",
month = oct,
doi = "10.1109/ICIP40778.2020.9190988",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "3149--3153",
booktitle = "2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings",
address = "United States",
}