@inbook {1835,
	title = {Iterative reconstruction methods in X-ray CT},
	booktitle = {Handbook of X-ray Imaging: Physics and Technology},
	year = {2018},
	pages = {693-712},
	publisher = {CRC Press},
	organization = {CRC Press},
	chapter = {34},
	author = {Van Eyndhoven, Geert and Jan Sijbers}
}
@inproceedings {1627,
	title = {A dynamic region estimation method for cerebral perfusion CT},
	booktitle = {6th International Conference on Optical Measurement Techniques for Structures and Systems (OPTIMESS)},
	year = {2016},
	pages = {331-342},
	abstract = {In cerebral perfusion computed tomography (PCT), multiple scans of the brain are acquired after an intravenous contrast bolus injection. Therefore, radiation dose is a major issue. 
Recently, methods have been proposed that can reconstruct high quality dynamic (i.e., 4D) images, while keeping the radiation dose limited. These methods typically require an accurate estimate of the dynamic region inside the brain volume, i.e., the region containing tissue/vessels. Conventionally, the dynamic region is indicated manually.
In this work, a method for low-dose cerebral PCT is presented in which the dynamic region is estimated in an automatic way.
Simulation results on two PCT phantoms show that the dynamic region can be accurately estimated, even in a very low-dose regime, which is an important step towards more powerful reconstruction methods for low-dose cerebral PCT.
},
	author = {Van Eyndhoven, Geert and Jan De Beenhouwer and Jan Sijbers}
}
@article {1703,
	title = {Local Attenuation Curve Optimization (LACO) framework for high quality perfusion maps in low-dose cerebral perfusion CT},
	journal = {Medical Physics},
	volume = {43},
	year = {2016},
	pages = {6429-6438},
	doi = {10.1118/1.4967263},
	author = {Vincent Van Nieuwenhove and Van Eyndhoven, Geert and Kees Joost Batenburg and Nico Buls and Jaf Vandemeulebroucke and Jan De Beenhouwer and Jan Sijbers}
}
@conference {1589,
	title = {A 4D CT reconstruction algorithm for fast liquid flow imaging},
	year = {2015},
	abstract = {  The study of fluid flow through solid matter by computed tomography (CT) imaging has a broad range of applications, ranging from oil extraction to scientific research on fluid dynamics. Current techniques are often limited by a low temporal/spatial resolution. In this talk, a new iterative CT reconstruction algorithm for improved temporal/spatial resolution in the imaging of fluid flowing through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. Firstly, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Secondly, the attenuation of a particular voxel in the dynamic region is modeled by a piecewise constant function over time (i.e., the voxel consists of fluid or air). Experiments on simulation data and on a real neutron tomography dataset demonstrate that the proposed approach can significantly increase the temporal resolution in comparison to conventional algorithms.},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Kazantsev, Daniil and Vincent Van Nieuwenhove and Lee, Peter D. and Katherine J Dobson and Jan Sijbers}
}
@article {1570,
	title = {4D-CT reconstruction with unified spatial-temporal patch-based regularization},
	journal = { Inverse Problems and Imaging},
	volume = {9},
	year = {2015},
	pages = {447-467},
	doi = {10.3934/ipi.2015.9.447},
	author = {Kazantsev, Daniil and Thompson, William M. and Lionheart, William R. B. and Van Eyndhoven, Geert and Kaestner,  Anders P. and Katherine J Dobson and Withers, Philip J. and Lee, Peter D.}
}
@article {1579,
	title = {Employing temporal self-similarity across the entire time domain in computed tomography reconstruction},
	journal = {Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences},
	volume = {373},
	number = {2043},
	year = {2015},
	publisher = {The Royal Society},
	abstract = {There are many cases where one needs to limit the X-ray dose, or the number of projections, or both, for high frame rate (fast) imaging. Normally, it improves temporal resolution but reduces the spatial resolution of the reconstructed data. Fortunately, the redundancy of information in the temporal domain can be employed to improve spatial resolution. In this paper, we propose a novel regularizer for iterative reconstruction of time-lapse computed tomography. The non-local penalty term is driven by the available prior information and employs all available temporal data to improve the spatial resolution of each individual time frame. A high-resolution prior image from the same or a different imaging modality is used to enhance edges which remain stationary throughout the acquisition time while dynamic features tend to be regularized spatially. Effective computational performance together with robust improvement in spatial and temporal resolution makes the proposed method a competitive tool to state-of-the-art techniques.},
	isbn = {1471-2962},
	issn = {1364-503X},
	doi = {10.1098/rsta.2014.0389},
	author = {Kazantsev, Daniil and Van Eyndhoven, Geert and Lionheart, William R. B. and Withers, Philip J. and Katherine J Dobson and McDonald, S. A. and Atwood, R and Lee, Peter D.}
}
@article {1606,
	title = {An iterative CT reconstruction algorithm for fast fluid flow imaging},
	journal = {IEEE Transactions on Image Processing},
	volume = {24},
	year = {2015},
	pages = {4446-4458},
	abstract = {The study of fluid flow through solid matter by computed tomography (CT) imaging has many applications, ranging from petroleum and aquifer engineering to biomedical, manufacturing and environmental research. To avoid motion artifacts, current experiments are often limited to slow fluid flow dynamics. This severely limits the applicability of the technique.

In this paper, a new iterative CT reconstruction algorithm for improved temporal/spatial resolution in the imaging of fluid flow through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. Firstly, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Secondly, the attenuation curve of a particular voxel in the dynamic region is modeled by a piecewise constant function over time, which is in accordance with the actual advancing fluid/air boundary.

Quantitative and qualitative results on different simulation experiments and a real neutron tomography dataset show that, in comparison state-of-the-art algorithms, the proposed algorithm allows reconstruction from substantially fewer projections per rotation without image quality loss. Therefore, temporal resolution can be substantially increased and thus fluid flow experiments with faster dynamics can be performed.},
	issn = {1057-7149},
	doi = {10.1109/TIP.2015.2466113},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Kazantsev, Daniil and Vincent Van Nieuwenhove and Lee, Peter D. and Katherine J Dobson and Jan Sijbers}
}
@conference {1588,
	title = {An iterative prior knowledge based reconstruction algorithm for increased temporal/spatial resolution in the CT imaging of fluid flow through solid matter},
	year = {2015},
	abstract = {The study of fluid flow through solid matter by computed tomography (CT) imaging has a broad range of applications, e.g., in oil production (the manner in which water displaces oil in rocks determines the fraction of oil that can be recovered) and in scientific research on fluid dynamics (validation of fluid flow models) [1-4]. In this work, a novel iterative computed tomography reconstruction algorithm for improved temporal/spatial resolution in the imaging of fluid flowing through solid matter is introduced. The proposed algorithm exploits prior knowledge in a twofold manner. Firstly, a dynamic reconstruction is generated assuming the existence of stationary regions (the solid matter) and dynamic regions (the fluid flow) throughout the reconstruction domain. This assumption is enforced by sharing iterative updates in the stationary regions over different time frames. Secondly, the fact that a particular voxel in the dynamic region can typically be described by a piecewise constant (PWC) function over time (i.e., the voxel consists of fluid or air) is exploited by estimating a PWC function in a robust manner in all voxels belonging to the dynamic region at intermediate iterations. The proposed reconstruction algorithm was validated on simulation data and on a real neutron tomography dataset (PSI ICON experiment, courtesy of Manchester X-ray Imaging Facility). These experiments demonstrated that the new iterative technique is able to significantly increase the temporal resolution in comparison to more conventional algorithms such as the Simultaneous Iterative Reconstruction Technique (SIRT) or Filtered Backprojection (FBP) [5-6].

[1]	V. Cnudde and M. Boone: High-resolution x-ray computed tomography in geosciences: A review of the current technology and applications. Earth-Science Reviews, vol. 123, pp. 1 {\textendash} 17, 2013
[2]	S. Akin and A. Kovscek: Computed tomography in petroleum engineering research. Geological Society, London, Special Publications, vol. 215, no. 1, pp. 23{\textendash}38, 2003
[3]	O. P. Wennberg, L. Rennan, and R. Basquet: Computed tomography scan imaging of natural open fractures in a porous rock; geometry and fluid flow. Geophysical Prospecting, vol. 57, no. 2, pp. 239{\textendash}249, 2009
[4]	M. Kumar, T. J. Senden, A. P. Sheppard, J. P. Middleton, and M. A. Knackstedt: Visualizing and quantifying the residual phase distribution in core material. Petrophysics, vol. 51, no. 5, p. 323, 2010
[5]	J. Gregor and T. Benson: Computational analysis and improvement of SIRT. IEEE Trans. Med. Imag., vol. 27, no. 7, pp. 918{\textendash}24, 2008
[6]	L. Feldkamp, L. Davis, and J. Kress: Practical cone-beam algorithm. J. Opt. Soc. Am. A, vol. 1, no. 6, pp. 612{\textendash}619, 1984
},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Kazantsev, Daniil and Jan Sijbers}
}
@mastersthesis {1631,
	title = {Model-based iterative reconstruction algorithms for computed tomography},
	volume = {PhD in Sciences},
	year = {2015},
	month = {12/2015},
	type = {PhD thesis},
	author = {Van Eyndhoven, Geert}
}
@article {1496,
	title = {Pore REconstruction and Segmentation (PORES) method for improved porosity quantification of nanoporous materials},
	journal = {Ultramicroscopy},
	volume = {148},
	year = {2015},
	pages = {10-19},
	abstract = {Electron tomography is currently a versatile tool to investigate the connection between the structure and properties of nanomaterials. However, a quantitative interpretation of electron tomography results is still far from straightforward. Especially accurate quantification of pore-space is hampered by artifacts introduced in all steps of the processing chain, i.e., acquisition, reconstruction, segmentation and quantification. Furthermore, most common approaches require subjective manual user input. In this paper, the PORES algorithm ({\textquotedblleft}POre REconstruction and Segmentation{\textquotedblright}) is introduced; it is a tailor-made, integral approach, for the reconstruction, segmentation, and quantification of porous nanomaterials. The PORES processing chain starts by calculating a reconstruction with a nanoporous-specific reconstruction algorithm: the Simultaneous Update of Pore Pixels by iterative REconstruction and Simple Segmentation algorithm (SUPPRESS). It classifies the interior region to the pores during reconstruction, while reconstructing the remaining region by reducing the error with respect to the acquired electron microscopy data. The SUPPRESS reconstruction can be directly plugged into the remaining processing chain of the PORES algorithm, resulting in accurate individual pore quantification and full sample pore statistics. The proposed approach was extensively validated on both simulated and experimental data, indicating its ability to generate accurate statistics of nanoporous materials.},
	doi = {10.1016/j.ultramic.2014.08.008},
	author = {Van Eyndhoven, Geert and Mert Kurttepeli and C. J. {Van Oers} and Pegie Cool and Sara Bals and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {1590,
	title = {Region based 4D tomographic image reconstruction: application to cardiac X-ray CT},
	booktitle = {IEEE International Conference on Image Processing},
	year = {2015},
	pages = {113 - 117},
	abstract = {X-ray computed tomography (CT) is a powerful tool for noninvasive cardiac imaging. However, radiation dose is a major issue. In this paper, we propose an iterative reconstruction method that reduces the radiation dose without compromising image quality. This is achieved by exploiting prior knowledge in two ways: the reconstructed object is assumed to consist of both stationary and dynamic regions over time and the dynamic region is assumed to have sparse structures after a proper sparsifying space-time transform. Experiments on simulation data and a real micro CT cardiac mouse dataset show that, with comparable image quality, the radiation dose can be substantially reduced compared to conventional acquisition/reconstruction protocols.},
	doi = {10.1109/ICIP.2015.7350770},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1607,
	title = {Accurate reconstruction of porous samples in CT},
	year = {2014},
	month = {2014},
	abstract = {Quantification of a sample{\textquoteright}s porosity from computed tomography (CT) images is important in many industrial applications. However, in non-standard acquisition set-ups (e.g., limited data or angular range), the reconstruction and subsequent segmentation of pore-space is challenging. In this paper, a novel algorithm exploiting prior knowledge about the porous structure of the scanned is proposed, improving the quality of both reconstruction and segmentation.},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@article {1504,
	title = {Combined Estimation of Affine Movement and Reconstruction in  Tomography},
	year = {2014},
	publisher = {3D Materials Science Conference},
	author = {Vincent Van Nieuwenhove and Van Eyndhoven, Geert and Jan De Beenhouwer and Jan Sijbers}
}
@conference {1503,
	title = {Compensation of affine deformations in fan and cone beam projections},
	year = {2014},
	pages = {187-189},
	author = {Vincent Van Nieuwenhove and Van Eyndhoven, Geert and Jan De Beenhouwer and Jan Sijbers}
}
@conference {1449,
	title = {A local enhancement based tomographic reconstruction technique for radiation exposure reduction in cerebral perfusion computed tomography},
	year = {2014},
	doi = {10.1594/ecr2014/C-0282},
	url = {http://posterng.netkey.at/esr/online_viewing/index.php?module=view_postercoverpage\&task=viewcoverpage\&start=0\&ls=authorlist\&pi=121333\&perid=248156\&num=1\&count=1\&cid=515},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@article {1436,
	title = {Region-based iterative reconstruction of structurally changing objects in CT},
	journal = {IEEE Transactions on Image Processing},
	volume = {23},
	year = {2014},
	pages = {909-919},
	chapter = {909-919},
	doi = {10.1109/TIP.2013.2297024},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1454,
	title = {Reliable Pore-size Measurements Based on a Procedure Specifically Designed for Electron Tomography Measurements of Nanoporous Samples},
	year = {2014},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Cynthia Van Oers and Mert Kurttepeli and Sara Bals and Pegie Cool and Jan Sijbers}
}
@inproceedings {1399,
	title = {An Algebraic Reconstruction Technique for the Study of Local Structural Changes during CT},
	booktitle = {1st International Conference on Tomography of Materials and Structures (ICTMS)},
	year = {2013},
	pages = {137-141},
	abstract = {If an object undergoes structural changes during a CT-scan, artefacts are introduced in the reconstruction. Current methods that compensate for these changes assume a continuous deformation model, which is insufficient to capture the complexity of structural changes. In this paper, an iterative method is proposed that incorporates prior knowledge about the changing region in the reconstruction algorithm, which results in significantly more accurate reconstructions.},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@conference {1398,
	title = {Region-Based SIRT algorithm for the reconstrcution of phase bins in dynamic micro-CT},
	year = {2013},
	author = {Van Eyndhoven, Geert and Kees Joost Batenburg and Jan Sijbers}
}
@inproceedings {1367,
	title = {Combined Motion Estimation and Reconstruction in Tomography},
	booktitle = {12th European Conference on Computer Vision},
	volume = {7583},
	year = {2012},
	pages = {12-21},
	publisher = {Lecture Notes on Computer Science},
	organization = {Lecture Notes on Computer Science},
	address = {Firenze, Italy},
	abstract = {If objects or patients move during a CT scan, reconstructions suffer from severe motion artifacts. Time dependent computed tomography (4DCT) tries to minimize these artifacts by estimating motion and/or reconstruction simultaneously. Most current methods assume a known deformation or a reconstruction without artifacts at a certain time point. This work explores the possibilities of estimating the motion model and reconstruction simultaneously. It does so by modifying the simultaneous iterative reconstruction technique (SIRT) to incorporate motion (trans-SIRT) and uses this method in an optimization routine that computes motion and reconstruction at the same time. Results show that the optimization routine is able to estimate motion accurately, assuming only the type of parametrization for the motion model. Our approach can potentially be extended to more complex motion models.},
	doi = {10.1007/978-3-642-33863-2_2},
	author = {Van Eyndhoven, Geert and Jan Sijbers and Kees Joost Batenburg}
}
