@article {2236,
	title = {Automatic anomaly detection from X-ray images based on autoencoder},
	journal = {Nondestructive Testing and Evaluation},
	volume = {37},
	year = {2022},
	doi = {10.1080/10589759.2022.2074415},
	author = {Alice Presenti and Zhihua Liang and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2194,
	title = {CNN-based pose estimation from a single X-ray projection for 3D inspection of manufactured objects},
	booktitle = {11th Conference on Industrial Computed Tomography},
	year = {2022},
	author = {Alice Presenti and Zhihua Liang and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2201,
	title = {CNN-based Pose Estimation of Manufactured Objects During Inline X-ray Inspection},
	booktitle = {2021 IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI)},
	year = {2021},
	abstract = {X-ray Computed Tomography (CT) is a nondestructive technique widely used for inspection of manufactured objects generated from a reference computer-aided-design (CAD) representation. In a conventional CT inspection framework, a volumetric reconstruction is computed from a large number of X-ray projections of the object. Afterwards, a surface is extracted, aligned and compared to the CAD model. For an accurate comparison, a high-resolution reconstruction is needed, requiring hundreds of projections, making this procedure not suitable for real-time inspection. In contrast to CT-based inspection, radiograph-based inspection only requires a few radiographs that then can be compared with simulated projections from the reference CAD model. For an effective comparison, however, an accurate 3D pose estimation of the object and consequent alignment between the measured object and the reference model are crucial. In this paper, we present an inline projection-based 3D pose estimation framework using convolutional neural networks (CNNs). Through realistic simulation experiments, we show that, with only two projections, estimation of the pose of the object is possible at the resolution of the acquisition system.},
	author = {Alice Presenti and Zhihua Liang and Luis Filipe Alves Pereira and Jan Sijbers and Jan De Beenhouwer}
}
@inproceedings {2043,
	title = {SPIE ProceedingsEnergy calibration of photon counting detectors using x-ray tube potential as a reference for material decomposition applications},
	booktitle = {SPIE Medical ImagingMedical Imaging 2015: Physics of Medical Imaging},
	volume = {9412},
	year = {2015},
	pages = {941214},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Orlando, Florida, United States},
	doi = {10.1117/12.2082979},
	url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2082979},
	author = {Das, Mini and Kandel, Bigyan and Park, Chan Soo and Zhihua Liang},
	editor = {Hoeschen, Christoph and Kontos, Despina and Flohr, Thomas G.}
}
@inproceedings {2044,
	title = {SPIE ProceedingsSingle-step, quantitative x-ray differential phase contrast imaging using spectral detection in a coded aperture setup},
	booktitle = {SPIE Medical ImagingMedical Imaging 2015: Physics of Medical Imaging},
	volume = {9412},
	year = {2015},
	pages = {941252},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Orlando, Florida, United States},
	doi = {10.1117/12.2082977},
	url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2082977},
	author = {Das, Mini and Zhihua Liang},
	editor = {Hoeschen, Christoph and Kontos, Despina and Flohr, Thomas G.}
}
@inproceedings {2042,
	title = {SPIE ProceedingsTowards using eye-tracking data to develop visual-search observers for x-ray breast imaging},
	booktitle = {SPIE Medical ImagingMedical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment},
	volume = {9416},
	year = {2015},
	pages = {94160V},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Orlando, Florida, United States},
	doi = {10.1117/12.2082978},
	url = {http://proceedings.spiedigitallibrary.org/proceeding.aspx?doi=10.1117/12.2082978},
	author = {Jiang, Zhengqiang and Zhihua Liang and Das, Mini and Gifford, Howard C.},
	editor = {Mello-Thoms, Claudia R. and Kupinski, Matthew A.}
}
@article {2045,
	title = {Approximated transport-of-intensity equation for coded-aperture x-ray phase-contrast imaging},
	journal = {Optics Letters},
	volume = {39},
	year = {2014},
	month = {Jan-01-2014},
	pages = {5395},
	issn = {0146-9592},
	doi = {10.1364/OL.39.005395},
	url = {https://www.osapublishing.org/abstract.cfm?URI=ol-39-18-5395https://www.osapublishing.org/viewmedia.cfm?URI=ol-39-18-5395\&seq=0},
	author = {Das, Mini and Zhihua Liang}
}
