@article {Xu2006a,
	title = {Lung deformation estimation and four-dimensional CT lung reconstruction},
	journal = {Journal of Academic Radiology},
	volume = {13},
	number = {9},
	year = {2006},
	month = {Sep},
	pages = {1082{\textendash}1092},
	publisher = {Philips Research North America, 345 Scarborough Road, Briarcliff Manor, NY 10510, USA sheng xu@philips com},
	abstract = {<p>RATIONALE AND OBJECTIVES: Four-dimensional (4D) computed tomography (CT) can be used in radiation treatment planning to account for respiratory motion Current 4D CT techniques have limitations in either spatial or temporal resolution In addition, most of these techniques rely on auxiliary surrogates to relate the time of the CT scan to the patient{\textquoteright}s respiratory phase We propose a 4D CT method for lung applications to overcome these problems MATERIALS AND METHODS: A set of axial scans are taken at multiple table positions to obtain a series of two-dimensional images while the patient is breathing freely Each two-dimensional image is registered to a reference CT volume The deformation of the image with respect to the volume is used to synchronize the image with the respiratory cycle assuming that there is no phase variation along the craniocaudal direction The reconstructed 4D dataset is a series of deformable transformations of the reference volume RESULTS: A synthetic 4D dataset showed that the registration error is less than 5\% of the image deformation A swine study showed that the algorithm can generate better image quality than the image sorting method A respiratory-gated 4D dataset showed that the algorithm{\textquoteright}s result is consistent with the ground truth CONCLUSION: The algorithm can reconstruct good quality 4D images without external surrogates even if the CT scans are acquired under irregular respiratory motion The algorithm may allow for reduced radiation dose to the patient with a limited loss of image quality Although the phase variation exists along the craniocaudal direction, the 4D reconstruction is reasonably accurate</p>
},
	keywords = {Algorithms, Artifacts, Computer-Assisted, Humans, Imaging, Information Storage, Lung, methods, Movement, Phantoms, Radiographic Image Enhancement, Radiographic Image Interpretation, Radiography, Reproducibility of Results, Respiratory Mechanics, Retrieval, Sensitivity, Specificity, Three-Dimensional, Tomography, X-Ray Computed},
	doi = {10 1016/j acra 2006 05 004},
	url = {http://dx doi org/10 1016/j acra 2006 05 004},
	author = {Sheng Xu and Russell H. Taylor and Gabor Fichtinger and Kevin Cleary}
}
@article {Xu2005b,
	title = {Lung deformation estimation and four-dimensional CT lung reconstruction},
	journal = {Medical image computing and computer-assisted intervention (MICCAI)},
	volume = {8},
	number = {Pt 2},
	year = {2005},
	pages = {312{\textendash}319},
	abstract = {<p>Four-dimensional (4D) computed tomography (CT) image acquisition is a useful technique in radiation treatment planning, interventional radiology in that it can account for respiratory motion of lungs Current 4D lung reconstruction techniques have limitations in either spatial or temporal resolution In addition, most of these techniques rely on auxiliary surrogates to relate the time of CT scan to the patient{\textquoteright}s respiratory phase In this paper, we propose a novel 4D CT lung reconstruction, deformation estimation algorithm Our algorithm is purely image based The algorithm can reconstruct high quality 4D images even if the original images are acquired under irregular respiratory motion The algorithm is validated using synthetic 4D lung data Experimental results from a swine study data are also presented</p>
},
	keywords = {Algorithms, Animals, Artificial Intelligence, Biological, Computer Simulation, Computer-Assisted, Elasticity, Humans, Imaging, Lung, methods, Models, physiology/radiography, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Respiratory Mechanics, Sensitivity, Specificity, Swine, Three-Dimensional, Tomography, X-Ray Computed},
	author = {Sheng Xu and Russell H. Taylor and Gabor Fichtinger and Kevin Cleary}
}
