Temporo-spatial IMRT optimization: concepts, implementation and initial results

With the recent availability of 4D-CT, the accuracy of information on internal organ motion during respiration has improved significantly. We investigate the utility of organ motion information in IMRT treatment planning, using an in-house prototype optimization system. Four approaches are compared: (1) planning with optimized margins, based on motion information; (2) the 'motion kernel' approach, in which a more accurate description of the dose deposit from a pencil beam to a moving target is achieved either through time-weighted averaging of influence matrices, calculated for different instances of anatomy (subsets of 4D-CT data, corresponding to various phases of motion) or through convolution of the pencil beam kernel with the probability density function describing the target motion; (3) optimal gating, or tracking with beam intensity maps optimized independently for each instance of anatomy; and (4) optimal tracking with beam intensity maps optimized simultaneously for all instances of anatomy. The optimization is based on a gradient technique and can handle both physical (dose-volume) and equivalent uniform dose constraints. Optimization requires voxel mapping from phase to phase in order to score the dose in individual voxels as they move. The results show that, compared to the other approaches, margin expansion has a significant disadvantage by substantially increasing the integral dose to patient. While gating or tracking result in the best dose conformation to the target, the former elongates treatment time, and the latter significantly complicates the delivery procedure. The 'motion kernel' approach does not provide a dosimetric advantage, compared to optimal tracking or gating, but might lead to more efficient delivery. A combination of gating with the 'motion kernel' or margin expansion approach will increase the duty cycle and may provide one with the most efficient solution, in terms of complexity of the delivery procedure and dose conformality to the target.

[1]  Y. Censor,et al.  A computational solution of the inverse problem in radiation-therapy treatment planning , 1988 .

[2]  T. Bortfeld,et al.  Methods of image reconstruction from projections applied to conformation radiotherapy. , 1990, Physics in medicine and biology.

[3]  Icru.  Prescribing, recording, and reporting photon beam therapy , 1993 .

[4]  E van der Schueren,et al.  Quality assessment of medical decision making in radiation oncology: variability in target volume delineation for brain tumours. , 1993, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[5]  Mohan,et al.  Field Shaping for Three-Dimensional Conformal Radiation Therapy and Multileaf Collimation. , 1995, Seminars in radiation oncology.

[6]  T Knöös,et al.  Limitations of a pencil beam approach to photon dose calculations in lung tissue. , 1995, Physics in medicine and biology.

[7]  J. V. van Santvoort,et al.  Dynamic multileaf collimation without 'tongue-and-groove' underdosage effects. , 1996, Physics in medicine and biology.

[8]  D. Yan,et al.  Adaptive radiation therapy , 1997, Physics in medicine and biology.

[9]  E. Larsen,et al.  A method for incorporating organ motion due to breathing into 3D dose calculations. , 1999, Medical physics.

[10]  Ivar Jacobson,et al.  The unified modeling language reference manual , 2010 .

[11]  T. Bortfeld,et al.  Correlation between CT numbers and tissue parameters needed for Monte Carlo simulations of clinical dose distributions. , 2000, Physics in medicine and biology.

[12]  R. Mohan,et al.  Motion adaptive x-ray therapy: a feasibility study , 2001, Physics in medicine and biology.

[13]  Simeon Nill.  Development and application of a multi-modality inverse treatment planning system , 2001 .

[14]  Gikas S. Mageras,et al.  Fluoroscopic evaluation of diaphragmatic motion reduction with a respiratory gated radiotherapy system , 2001, Journal of applied clinical medical physics.

[15]  T. Bortfeld,et al.  Inverse planning for photon and proton beams. , 2001, Medical dosimetry : official journal of the American Association of Medical Dosimetrists.

[16]  Bernard Dubray,et al.  Conformal radiotherapy for lung cancer: different delineation of the gross tumor volume (GTV) by radiologists and radiation oncologists. , 2002, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[17]  P J Keall,et al.  A fluence-convolution method to calculate radiation therapy dose distributions that incorporate random set-up error. , 2002, Physics in medicine and biology.

[18]  Indrin J Chetty,et al.  A fluence convolution method to account for respiratory motion in three-dimensional dose calculations of the liver: a Monte Carlo study. , 2003, Medical physics.

[19]  Randall K Ten Haken,et al.  A method for incorporating organ motion due to breathing into 3D dose calculations in the liver: sensitivity to variations in motion. , 2003, Medical physics.

[20]  T. Bortfeld,et al.  From physical dose constraints to equivalent uniform dose constraints in inverse radiotherapy planning. , 2003, Medical physics.

[21]  G. Christensen,et al.  A method for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing. , 2003, Medical physics.

[22]  P Zygmanski,et al.  A method of calculating a lung clinical target volume DVH for IMRT with intrafractional motion. , 2003, Medical physics.

[23]  R. Mohan,et al.  Acquiring a four-dimensional computed tomography dataset using an external respiratory signal. , 2003, Physics in medicine and biology.

[24]  R. Mohan,et al.  Quantifying the predictability of diaphragm motion during respiration with a noninvasive external marker. , 2003, Medical physics.

[25]  Steve B Jiang,et al.  Synchronized moving aperture radiation therapy (SMART): average tumour trajectory for lung patients. , 2003, Physics in medicine and biology.

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

[27]  Shinichi Shimizu,et al.  Intrafractional tumor motion: lung and liver. , 2004, Seminars in radiation oncology.

[28]  J. Unkelbach,et al.  Inclusion of organ movements in IMRT treatment planning via inverse planning based on probability distributions. , 2004, Physics in medicine and biology.

[29]  T. Pan,et al.  4D-CT imaging of a volume influenced by respiratory motion on multi-slice CT. , 2004, Medical physics.

[30]  R. Jeraj,et al.  Treatment plan optimization incorporating respiratory motion. , 2004, Medical physics.

[31]  Christopher G Willett,et al.  Quantification of respiration-induced abdominal tumor motion and its impact on IMRT dose distributions. , 2004, International journal of radiation oncology, biology, physics.

[32]  George T. Y. Chen,et al.  Four-dimensional image-based treatment planning: Target volume segmentation and dose calculation in the presence of respiratory motion. , 2005, International journal of radiation oncology, biology, physics.