@article {DiMaio2007b,
	title = {Challenges in image-guided therapy system design},
	journal = {Neuroimage},
	volume = {37 Suppl 1},
	year = {2007},
	pages = {S144{\textendash}S151},
	publisher = {Brigham, Women{\textquoteright}s Hospital, 75 Francis St , Boston, MA 02115, USA simond@bwh harvard edu},
	abstract = {<p>System development for image-guided therapy (IGT), or image-guided interventions (IGI), continues to be an area of active interest across academic, industry groups This is an emerging field that is growing rapidly: major academic institutions, medical device manufacturers have produced IGT technologies that are in routine clinical use, dozens of high-impact publications are published in well regarded journals each year, and several small companies have successfully commercialized sophisticated IGT systems In meetings between IGT investigators over the last two years, a consensus has emerged that several key areas must be addressed collaboratively by the community to reach the next level of impact, efficiency in IGT research, development to improve patient care These meetings culminated in a two-day workshop that brought together several academic, industrial leaders in the field today The goals of the workshop were to identify gaps in the engineering infrastructure available to IGT researchers, develop the role of research funding agencies, the recently established US-based National Center for Image Guided Therapy (NCIGT), and ultimately to facilitate the transfer of technology among research centers that are sponsored by the National Institutes of Health (NIH) Workshop discussions spanned many of the current challenges in the development, deployment of new IGT systems Key challenges were identified in a number of areas, including: validation standards; workflows, use-cases, and application requirements; component reusability;, device interface standards This report elaborates on these key points, proposes research challenges that are to be addressed by a joint effort between academic, industry, and NIH participants</p>
},
	keywords = {adverse effects/instrumentation/trends, Algorithms, Computational Biology, Computer Systems, Computer-Assisted, Humans, instrumentation/trends, Knowledge Bases, Models, National Institutes of Health (U S ), Neurosurgical Procedures, Organizational, Outcome Assessment (Health Care), Reproducibility of Results, Robotics, Software, Surgery, Treatment Outcome, United States},
	doi = {10 1016/j neuroimage 2007 04 026},
	url = {http://dx doi org/10 1016/j neuroimage 2007 04 026},
	author = {Simon DiMaio and Tina Kapur and Kevin Cleary and Stephen Aylward and Peter Kazanzides and Kirby Vosburgh and Randy Ellis and James Duncan and Keyvan Farahani and Heinz Lemke and T. M. Peters and William Bill Lorensen and David Gobbi and John Haller and Laurence Larry Clarke and Stephen Pizer and Russell H. Taylor and Robert L. Galloway, Jr. and Gabor Fichtinger and Nobuhiko Hata and Kimberly Lawson and Clare M Tempany and Ron Kikinis and Ferenc Jolesz}
}
@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}
}
