Contour based respiratory motion analysis for free breathing CT

We propose a method to quantify superior-inferior (SI) motion of a rigid target using the 3D contour from free-breathing CT (FBCT). The technique utilizes similarity between 2D contours (Jaccard Index) and a population based density function for probability of motion amplitude, and is applicable both when the static target shape is and is not known beforehand. Simulations and phantom measurements showed that motion reconstruction is often feasible, with decreasing accuracy as discrepancy is introduced between assumed and actual static shape. When no static shape is used the analysis is most robust for slow scanning speeds relative to the motion period.

[1]  Pedro Larrañaga,et al.  Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators , 1999, Artificial Intelligence Review.

[2]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[3]  G. Laporte The traveling salesman problem: An overview of exact and approximate algorithms , 1992 .

[4]  Lech Papiez,et al.  Do maximum intensity projection images truly capture tumor motion? , 2009, International journal of radiation oncology, biology, physics.

[5]  Shinichi Shimizu,et al.  Three-dimensional intrafractional movement of prostate measured during real-time tumor-tracking radiotherapy in supine and prone treatment positions. , 2002, International journal of radiation oncology, biology, physics.

[6]  Suresh Senan,et al.  Use of maximum intensity projections (MIP) for target volume generation in 4DCT scans for lung cancer. , 2005, International journal of radiation oncology, biology, physics.

[7]  Michael T. Goodrich,et al.  Straight-skeleton based contour interpolation , 2003, SODA '03.

[8]  Wayne Pullan,et al.  Adapting the genetic algorithm to the travelling salesman problem , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[9]  Suresh Senan,et al.  Reproducibility of target volumes generated using uncoached 4-dimensional CT scans for peripheral lung cancer , 2006, Radiation oncology.

[10]  Steve B. Jiang,et al.  A theoretical model for respiratory motion artifacts in free-breathing CT scans , 2008, Physics in medicine and biology.

[11]  K. Lam,et al.  Uncertainties in CT-based radiation therapy treatment planning associated with patient breathing. , 1996, International journal of radiation oncology, biology, physics.

[12]  R. Real,et al.  The Probabilistic Basis of Jaccard's Index of Similarity , 1996 .

[13]  Geoffrey Hugo,et al.  Changes in the respiratory pattern during radiotherapy for cancer in the lung. , 2006, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[14]  P. Keall 4-dimensional computed tomography imaging and treatment planning. , 2004, Seminars in radiation oncology.

[15]  John M Buatti,et al.  Optically guided patient positioning techniques. , 2005, Seminars in radiation oncology.

[16]  Jeffrey C. Lagarias,et al.  Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions , 1998, SIAM J. Optim..

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

[18]  S. Webb Motion effects in (intensity modulated) radiation therapy: a review , 2006, Physics in medicine and biology.

[19]  George Starkschall,et al.  Evaluation of internal lung motion for respiratory-gated radiotherapy using MRI: Part I--correlating internal lung motion with skin fiducial motion. , 2004, International journal of radiation oncology, biology, physics.

[20]  George T. Y. Chen,et al.  Artifacts in computed tomography scanning of moving objects. , 2004, Seminars in radiation oncology.