@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 {Jain2005a,
	title = {FTRAC},
	journal = {Journal of Medical Physics},
	volume = {32},
	number = {10},
	year = {2005},
	month = {Oct},
	pages = {3185{\textendash}3198},
	abstract = {<p>C-arm fluoroscopy is ubiquitous in contemporary surgery, but it lacks the ability to accurately reconstruct three-dimensional (3D) information A major obstacle in fluoroscopic reconstruction is discerning the pose of the x-ray image, in 3D space Optical/magnetic trackers tend to be prohibitively expensive, intrusive, cumbersome in many applications We present single-image-based fluoroscope tracking (FTRAC) with the use of an external radiographic fiducial consisting of a mathematically optimized set of ellipses, lines, and points This is an improvement over contemporary fiducials, which use only points The fiducial encodes six degrees of freedom in a single image by creating a unique view from any direction A nonlinear optimizer can rapidly compute the pose of the fiducial using this image The current embodiment has salient attributes: small dimensions (3 x 3 x 5 cm); need not be close to the anatomy of interest;, accurately segmentable We tested the fiducial, the pose recovery method on synthetic data, also experimentally on a precisely machined mechanical phantom Pose recovery in phantom experiments had an accuracy of 0 56 mm in translation, 0 33 degrees in orientation Object reconstruction had a mean error of 0 53 mm with 0 16 mm STD The method offers accuracies similar to commercial tracking systems, and appears to be sufficiently robust for intraoperative quantitative C-arm fluoroscopy Simulation experiments indicate that the size can be further reduced to 1 x 1 X 2 cm, with only a marginal drop in accuracy</p>
},
	keywords = {Algorithms, Computer-Assisted, Fluoroscopy, Imaging, Information Storage, instrumentation/methods, methods, Phantoms, Radiographic Image Enhancement, Radiographic Image Interpretation, Reproducibility of Results, Retrieval, Sensitivity, Specificity, Three-Dimensional},
	author = {Ameet K. Jain and Tabish Mustufa and Yu Zhou and E. Clif Burdette and Gregory Chirikjian and Gabor Fichtinger}
}
