Respiratory Motion Estimation from Cone-Beam Projections Using a Prior Model

Respiratory motion introduces uncertainties when planning and delivering radiotherapy for lung cancer patients. Cone-beam projections acquired in the treatment room could provide valuable information for building motion models, useful for gated treatment delivery or motion compensated reconstruction. We propose a method for estimating 3D+T respiratory motion from the 2D+T cone-beam projection sequence by including prior knowledge about the patient's breathing motion. Motion estimation is accomplished by maximizing the similarity of the projected view of a patient specific model to observed projections of the cone-beam sequence. This is done semi-globally, considering entire breathing cycles. Using realistic patient data, we show that the method is capable of good prediction of the internal patient motion from cone-beam data, even when confronted with interfractional changes in the breathing motion.

[1]  M van Herk,et al.  Reconstruction of a time-averaged midposition CT scan for radiotherapy planning of lung cancer patients using deformable registration. , 2008, Medical physics.

[2]  Michael Unser,et al.  Splines: a perfect fit for signal and image processing , 1999, IEEE Signal Process. Mag..

[3]  William H. Press,et al.  Numerical Recipes in Fortran 77: The Art of Scientific Computing 2nd Editionn - Volume 1 of Fortran Numerical Recipes , 1992 .

[4]  Jeffrey A. Fessler,et al.  Estimating 3-D Respiratory Motion From Orbiting Views by Tomographic Image Registration , 2007, IEEE Transactions on Medical Imaging.

[5]  J. McClelland,et al.  A continuous 4D motion model from multiple respiratory cycles for use in lung radiotherapy. , 2006, Medical physics.

[6]  William H. Press,et al.  Numerical recipes in C , 2002 .

[7]  Steve B. Jiang,et al.  The management of respiratory motion in radiation oncology report of AAPM Task Group 76. , 2006, Medical physics.

[8]  George Wolberg,et al.  Digital image warping , 1990 .

[9]  M. V. van Herk,et al.  Respiratory correlated cone beam CT. , 2005, Medical physics.

[10]  William H. Press,et al.  Numerical Recipes in C, 2nd Edition , 1992 .

[11]  Qinghui Zhang,et al.  A patient-specific respiratory model of anatomical motion for radiation treatment planning. , 2007, Medical physics.

[12]  Jan-Jakob Sonke,et al.  Variability of four-dimensional computed tomography patient models. , 2008, International journal of radiation oncology, biology, physics.

[13]  C. Ling,et al.  Respiration-correlated spiral CT: a method of measuring respiratory-induced anatomic motion for radiation treatment planning. , 2002, Medical physics.

[14]  Gabor Fichtinger,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008, 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I , 2008, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[15]  P Boesiger,et al.  4D MR imaging of respiratory organ motion and its variability , 2007, Physics in medicine and biology.

[16]  M. V. van Herk,et al.  Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. , 2002, International journal of radiation oncology, biology, physics.

[17]  Jean-Philippe Thirion,et al.  Image matching as a diffusion process: an analogy with Maxwell's demons , 1998, Medical Image Anal..

[18]  Jan-Jakob Sonke,et al.  On-the-Fly Motion-Compensated Cone-Beam CT Using an a Priori Motion Model , 2008, MICCAI.

[19]  P J Keall,et al.  The application of the sinusoidal model to lung cancer patient respiratory motion. , 2005, Medical physics.

[20]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.