Prediction framework for statistical respiratory motion modeling

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6363 LNCS, Issue PART 3, 2010, Pages 327-334

  Klinder, Tobias ^a   Lorenz, Cristian ^b   Ostermann, Jörn ^c  

^a PHILIPS RESEARCH LABORATORIES   (Netherlands)

^b PHILIPS RESEARCH LABORATORIES   (Germany)

^c LEIBNIZ UNIVERSITY HANNOVER   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BREATHING MODELS; BREATHING MOTIONS; CT DATA; DISPLACEMENT VECTORS; DYNAMIC DATA; IMAGE-GUIDED INTERVENTION; MOTION MODELING; PREDICTION ERRORS; PRIOR KNOWLEDGE; RESPIRATORY MOTIONS;

COMPUTERIZED TOMOGRAPHY; FORECASTING; MEDICAL COMPUTING; RESPIRATORY MECHANICS;

MEDICAL IMAGING;

ARTICLE; ARTIFACT; BIOLOGICAL MODEL; BREATHING MECHANICS; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; COMPUTER SIMULATION; HUMAN; METHODOLOGY; MOVEMENT (PHYSIOLOGY); REPRODUCIBILITY; RESPIRATORY GATED IMAGING; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL MODEL; THORAX RADIOGRAPHY; THREE DIMENSIONAL IMAGING;

ARTIFACTS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; IMAGING, THREE-DIMENSIONAL; MODELS, BIOLOGICAL; MODELS, STATISTICAL; MOVEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RADIOGRAPHY, THORACIC; REPRODUCIBILITY OF RESULTS; RESPIRATORY MECHANICS; RESPIRATORY-GATED IMAGING TECHNIQUES; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84861901172     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15711-0_41     Document Type: Conference Paper

References (9)

1. Keall, P., Mageras, G., Balter, J., et al.: The management of resp. motion in radiation oncology report of AAPM Task group. Med. Phys. 33(10), 3874-3900 (2006)
2. Bosch, J., Mitchell, S., Lelieveldt, B., et al.: Automatic segmentation of echocardiographic sequences by active appearance motion models. IEEE Transactions on Medical Imaging 21 (11), 1374-1383 (2002)
3. Casero, R., Noble, J.: A novel explicit 2D+t cyclic shape model applied to echocardiography. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 527-534. Springer, Heidelberg (2008)
4. Ehrhardt, J., Werner, R., Schmidt-Richberg, A., et al.: Generation of a mean motion model of the lung using 4D-CT image data. In: Eurographics Workshop on Visual Computing for Biomedicine (2008)
5. McClelland, J., Blackall, J., Tarte, S., et al.: A continuous 4D motion model from multiple respiratory cycles for use in lung radiotherapy. Med. Phys. 33 (9), 3348-3358 (2006)
6. Zhang, Q., Pevsner, A., Hertanto, A., et al.: A patient-specific respiratory model of anatomical motion for radiation treatment planning. Medical Physics 34(12), 4772-4782 (2007)
7. von Berg, J., Barschdorf, H., Blaffert, T., et al.: Surface based cardiac and respiratory motion extraction motion extraction for pulmonary structures from multi-phase CT. In: Proc. of SPIE, vol. 6511, pp. 65110Y1-11 (2007)
8. Castillo, R., Castillo, E., Guerra, R., et al.: A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Physics in Medicine and Biology 54, 1849-1870 (2009)
9. Rosipal, R., Krämer, N.: Overview and recent advances in partial least squares. In: Saunders, C., Grobelnik, M., Gunn, S., Shawe-Taylor, J. (eds.) SLSFS 2005. LNCS, vol. 3940, pp. 34-51. Springer, Heidelberg (2006) (Pubitemid 44029878)