Real-time prediction of respiratory motion based on a local dynamic model in an augmented space

Physics in Medicine and Biology

Volumn 56, Issue 6, 2011, Pages 1775-1789

  Hong, S M ^a   Jung, B H ^b   Ruan, D ^c  

^a Kyungpook National University   (South Korea)

^b Yong in Songdam College   (South Korea)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

A-PLANE; ABDOMINAL TUMORS; ADAPTIVE RADIOTHERAPY; CHOICE OF PARAMETERS; CIRCULAR MOTION; CIRCULAR PATTERN; COMMUNICATION AND CONTROL; COMPUTATION COSTS; ERROR METRICES; FIRST-ORDER; KERNEL DENSITY ESTIMATORS; LINEAR MODEL; LINEAR MOTION MODELS; LOCAL DYNAMIC MODEL; MULTIPLE LINEAR MODELS; NATURAL EVOLUTION; NONPARAMETRIC METHODS; NORMAL TISSUE; PARAMETRIC MODELS; PHYSICAL INTERPRETATION; RADIATION DOSE; REAL-TIME PREDICTION; RESPIRATORY MOTIONS; RESPIRATORY MOVEMENT; ROOT MEAN SQUARED; SEMIPARAMETRIC; STATE ESTIMATES; STATE REPRESENTATION; SYSTEM LATENCY; SYSTEM STATE; TARGET LOCATION; TARGET MOVEMENTS; WORK FOCUS;

ANGULAR VELOCITY; ESTIMATION; FORECASTING; MODELS; RESPIRATORY MECHANICS; SENSITIVITY ANALYSIS; STATE ESTIMATION; TUMORS;

DYNAMIC MODELS;

ABDOMINAL TUMOR; ARTICLE; BREATHING MECHANICS; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED RADIOTHERAPY; HUMAN; METHODOLOGY; MOTION; RADIOGRAPHY; STATISTICAL MODEL; THORAX TUMOR; TIME;

ABDOMINAL NEOPLASMS; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LINEAR MODELS; MOTION; RADIOTHERAPY, COMPUTER-ASSISTED; RESPIRATORY MECHANICS; THORACIC NEOPLASMS; TIME FACTORS;

EID: 79952919736     PISSN: 00319155     EISSN: 13616560     Source Type: Journal    
DOI: 10.1088/0031-9155/56/6/016     Document Type: Article

References (24)

1. Bar-Shalom Y, Li X-R and Kirubarajan T 2001 Estimation with Applications to Tracking and Navigation (New York: Wiley)
2. Ernst F, Schlaefer A and Schweikard A 2007 Prediction of respiratory motion with wavelet-based multiscale autoregression MICCAI 2007, Part II (Lecture Notes in Computer Science vol 4792) ed N Ayache, S Ourselin and A Maeder (Heidelberg: Springer) pp 668-75
3. Greenwood D T 1987 Principles of Dynamics (Englewood Cliffs, NJ: Prentice-Hall)
4. Grewal M S and Andrews A P 1993 Kalman Filtering: Theory and Practice (Englewood Cliffs, NJ: Prentice-Hall)
5. Haykin S 2002 Adaptive Filter Theory (Upper Saddle River, NJ: Prentice-Hall)
6. Isaksson M, Jalden J and Murphy M J 2005 On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications Med. Phys. 32 3801-9
7. Jiang S 2006 Technical aspects of image-guided respiration-gated radiation therapy Med. Dosim. 31 141-51
8. Kakar M, Nyström H, Aarup L R, Nøttrup T J and Olsen D R 2005 Respiratory motion prediction by using the adaptive neuro-fuzzy inference system (ANFIS) Phys. Med. Biol. 50 4721-8
9. Kazantzis N and Kravaris C 1999 Time-discretization of nonlinear control systems via Taylor methods Computers and Chem. Eng. 23 763-84 (Pubitemid 29251568)
10. Keall P J, Kini V R, Vedam S S and Mohan R 2002 Potential radiotherapy improvements with respiratory gating Australas. Phys. Eng. Sci. Med. 25 1-6 (Pubitemid 34464218)
11. McCall K C and Jeraj R 2007 Dual-component model of respiratory motion based on the periodic autoregressive moving average (periodic ARMA) method Phys. Med. Biol. 52 3455-66 (Pubitemid 46830636)
12. McMahon R, Papiez L and Sandison G 2007 Addressing relative motion of tumors and normal tissue during dynamic MLC tracking delivery Australas. Phys. Eng. Sci. Med. 30 331-6
13. Murphy M J and Dieterich S 2006 Comparative performance of linear and nonlinear neural networks to predict irregular breathing Phys. Med. Biol. 51 5903-14 (Pubitemid 46217675)
14. Nuyttens J J, Prevost J B, Praag J, Hoogeman M, Van Klaveren R J, Levendag P C and Pattynama P M 2006 Lung tumor tracking during stereotactic radiotherapy treatment with the cyberknife: marker placement and early results Acta Oncol. 45 961-5 (Pubitemid 44423146)
15. Putra D, Haas O C, Mills J A and Burnham K J 2008 A multiple model approach to respiratory motion prediction for real-time IGRT Phys. Med. Biol. 53 1651-63 (Pubitemid 351423002)
16. Reif K, Günther S, Yaz E and Unbehauen R 1999 Stochastic stability of the discrete-time extended Kalman filter IEEE Trans. Automatic Control 44 714-28
17. Ristic B, Arulampalam S and Gordon N 2004 Beyond the Kalman Filter: Particle Filters for Tracking Applications (Boston, MA: Artech House Publishers)
18. Ruan D 2010 Kernel density estimation-based real-time prediction for respiratory motion Phys. Med. Biol. 55 1311-26
19. Ruan D, Fessler J A and Balter J M 2007 Real-time prediction of respiratory motion based on local regression methods Phys. Med. Biol. 52 7137-52 (Pubitemid 350150636)
20. Ruan D, Fessler J A, Balter J M, Berbeco R I, Nishioka S and Shirato H 2008 Inference of hysteretic respiratory tumour motion from external surrogates: a state augmentation approach Phys. Med. Biol. 53 2923-36 (Pubitemid 351868614)
21. Ruan D and Keall P J 2010 Online prediction of respiratory motion: multidimensional processing with low-dimensional feature learning Phys. Med. Biol. 55 3011-25
22. Rugh W J 1996 Linear System Theory (Upper Saddle River, NJ: Prentice-Hall) p 74
23. Sharp G C, Jiang S B, Shimizu S and Shirato H 2004 Prediction of respiratory tumour motion for real-time image-guided radiotherapy Phys. Med. Biol. 49 425-40 (Pubitemid 38208152)
24. Vedam S S, Keall P J, Docef A, Todor D A, Kini V R and Mohan R 2004 Predicting respiratory motion for four-dimensional radiotherapy Med. Phys. 31 2274-83 (Pubitemid 39079720)