Quantitation of the a priori dosimetric capabilities of spatial points in inverse planning and its significant implication in defining IMRT solution space

In inverse planning, the likelihood for the points in a target or sensitive structure to meet their dosimetric goals is generally heterogeneous and represents the a priori knowledge of the system once the patient and beam configuration are chosen. Because of this intrinsic heterogeneity, in some extreme cases, a region in a target may never meet the prescribed dose without seriously deteriorating the doses in other areas. Conversely, the prescription in a region may be easily met without violating the tolerance of any sensitive structure. In this work, we introduce the concept of dosimetric capability to quantify the a priori information and develop a strategy to integrate the data into the inverse planning process. An iterative algorithm is implemented to numerically compute the capability distribution on a case specific basis. A method of incorporating the capability data into inverse planning is developed by heuristically modulating the importance of the individual voxels according to the a priori capability distribution. The formalism is applied to a few specific examples to illustrate the technical details of the new inverse planning technique. Our study indicates that the dosimetric capability is a useful concept to better understand the complex inverse planning problem and an effective use of the information allows us to construct a clinically more meaningful objective function to improve IMRT dose optimization techniques.

[1]  A Brahme,et al.  Solution of an integral equation encountered in rotation therapy. , 1982, Physics in medicine and biology.

[2]  K. Lange,et al.  EM reconstruction algorithms for emission and transmission tomography. , 1984, Journal of computer assisted tomography.

[3]  M. Langer,et al.  Optimization of beam weights under dose-volume restrictions. , 1987, International journal of radiation oncology, biology, physics.

[4]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..

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

[6]  J. Shapiro,et al.  Large scale optimization of beam weights under dose-volume restrictions. , 1990, International journal of radiation oncology, biology, physics.

[7]  E Atalar,et al.  A respiratory motion artifact reduction method in magnetic resonance imaging of the chest. , 1991, IEEE transactions on medical imaging.

[8]  S. Webb.  Optimization by simulated annealing of three-dimensional conformal treatment planning for radiation fields defined by a multileaf collimator. , 1991, Physics in medicine and biology.

[9]  G Starkschall,et al.  An interactive beam-weight optimization tool for three-dimensional radiotherapy treatment planning. , 1992, Medical physics.

[10]  F D Thomas,et al.  Imaging of the human torso using cone-beam transmission CT implemented on a rotating gamma camera. , 1992, Journal of nuclear medicine : official publication, Society of Nuclear Medicine.

[11]  G. Wang,et al.  A general cone-beam reconstruction algorithm , 1993, IEEE Trans. Medical Imaging.

[12]  T. Holmes,et al.  A comparison of three inverse treatment planning algorithms. , 1994, Physics in medicine and biology.

[13]  Ge Wang,et al.  Preliminary study on helical CT algorithms for patient motion estimation and compensation , 1995, IEEE Trans. Medical Imaging.

[14]  K S Lam,et al.  Comparison of simulated annealing algorithms for conformal therapy treatment planning. , 1995, International journal of radiation oncology, biology, physics.

[15]  Yoram Bresler,et al.  Optimal scan for time-varying tomography. I. Theoretical analysis and fundamental limitations , 1995, IEEE Trans. Image Process..

[16]  L. Xing,et al.  Iterative methods for inverse treatment planning. , 1996, Physics in medicine and biology.

[17]  Yongmin Kim,et al.  Correction of computed tomography motion artifacts using pixel-specific back-projection , 1996, IEEE Trans. Medical Imaging.

[18]  Cameron J. Ritchie,et al.  Respiratory compensation in projection imaging using a magnification and displacement model , 1996, IEEE Trans. Medical Imaging.

[19]  B G Fallone,et al.  An active set algorithm for treatment planning optimization. , 1997, Medical physics.

[20]  T R Mackie,et al.  Maximum likelihood as a common computational framework in tomotherapy , 1998, Physics in medicine and biology.

[21]  S. Webb,et al.  Inverse planning with constraints to generate smoothed intensity-modulated beams. , 1998, Physics in medicine and biology.

[22]  S. Sutlief,et al.  Optimization of intensity modulated beams with volume constraints using two methods: cost function minimization and projections onto convex sets. , 1998, Medical physics.

[23]  S. Spirou,et al.  A gradient inverse planning algorithm with dose-volume constraints. , 1998, Medical physics.

[24]  Yongyi Yang,et al.  Vector Space Projections: A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics , 1998 .

[25]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[26]  A L Boyer,et al.  Optimization of importance factors in inverse planning. , 1999, Physics in medicine and biology.

[27]  U Oelfke,et al.  Inverse planning for x-ray rotation therapy: a general solution of the inverse problem. , 1999, Physics in medicine and biology.

[28]  D A Jaffray,et al.  A radiographic and tomographic imaging system integrated into a medical linear accelerator for localization of bone and soft-tissue targets. , 1999, International journal of radiation oncology, biology, physics.

[29]  J. Galvin,et al.  An optimized forward-planning technique for intensity modulated radiation therapy. , 2000, Medical physics.

[30]  Jonathan G. Li,et al.  Inverse planning incorporating organ motion. , 2000 .

[31]  Norma C. Linney,et al.  Organ motion detection in CT images using opposite rays in fan-beam projection systems , 2001, IEEE Transactions on Medical Imaging.

[32]  G Starkschall,et al.  Treatment planning using a dose-volume feasibility search algorithm. , 2001, International journal of radiation oncology, biology, physics.

[33]  X. Wu,et al.  An optimization method for importance factors and beam weights based on genetic algorithms for radiotherapy treatment planning. , 2001, Physics in medicine and biology.

[34]  A C Dhanantwari,et al.  Correcting organ motion artifacts in x-ray CT medical imaging systems by adaptive processing. I. Theory. , 2001, Medical physics.

[35]  Yan Chen,et al.  A deterministic iterative least-squares algorithm for beam weight optimization in conformal radiotherapy. , 2002, Physics in medicine and biology.

[36]  R. Mohan,et al.  Optimization of intensity-modulated radiotherapy plans based on the equivalent uniform dose. , 2002, International journal of radiation oncology, biology, physics.

[37]  George Starkschall,et al.  Plan space: representation of treatment plans in multidimensional space. , 2002, International journal of radiation oncology, biology, physics.

[38]  L. Xing,et al.  Incorporating prior knowledge into beam orientation optimization in IMRT. , 2002, International journal of radiation oncology, biology, physics.

[39]  George Starkschall,et al.  Prostate cancer radiation dose response: results of the M. D. Anderson phase III randomized trial. , 2002, International journal of radiation oncology, biology, physics.

[40]  Lei Xing,et al.  Using voxel-dependent importance factors for interactive DVH-based dose optimization. , 2002, Physics in medicine and biology.

[41]  Eric C Ford,et al.  Optimization of conformal thoracic radiotherapy using cone-beam CT imaging for treatment verification. , 2003, International journal of radiation oncology, biology, physics.

[42]  Lei Xing,et al.  IMRT dose shaping with regionally variable penalty scheme. , 2003, Medical physics.

[43]  Sha X. Chang,et al.  Beam orientation selection for intensity-modulated radiation therapy based on target equivalent uniform dose maximization. , 2001, International journal of radiation oncology, biology, physics.

[44]  David R. Haynor,et al.  PET-CT image registration in the chest using free-form deformations , 2003, IEEE Transactions on Medical Imaging.

[45]  D L McShan,et al.  Inclusion of organ deformation in dose calculations. , 2003, Medical physics.

[46]  R. Jeraj,et al.  Treatment plan modification using voxel-based weighting factors/dose prescription. , 2003, Physics in medicine and biology.

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

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

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

[50]  Torsten Rohlfing,et al.  Volume-preserving nonrigid registration of MR breast images using free-form deformation with an incompressibility constraint , 2003, IEEE Transactions on Medical Imaging.

[51]  B. Daniel,et al.  Mapping of the prostate in endorectal coil-based MRI/MRSI and CT: a deformable registration and validation study. , 2004, Medical physics.

[52]  C. Catton,et al.  Comparison of implanted fiducial markers and cone-beam computed tomography for on-line image-guided radiotherapy of the prostate , 2004 .

[53]  Guang-Zhong Yang,et al.  Predictive cardiac motion modeling and correction with partial least squares regression , 2004, IEEE Transactions on Medical Imaging.

[54]  Yair Censor,et al.  The dose–volume constraint satisfaction problem for inverse treatment planning with field segments , 2004, Physics in medicine and biology.

[55]  Uwe Just,et al.  An accurate method for correction of head movement in PET , 2004, IEEE Transactions on Medical Imaging.

[56]  David Jaffray,et al.  First clinical experience with cone-beam CT guided radiation therapy; evaluation of dose and geometric accuracy , 2004 .

[57]  Lei Xing,et al.  Incorporating model parameter uncertainty into inverse treatment planning. , 2004, Medical physics.

[58]  Tinsu Pan,et al.  Four-dimensional computed tomography: image formation and clinical protocol. , 2005, Medical physics.

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

[60]  Fang-Fang Yin,et al.  Time delay measurement for linac based treatment delivery in synchronized respiratory gating radiotherapy. , 2005, Medical physics.

[61]  Lei Xing,et al.  Narrow band deformable registration of prostate magnetic resonance imaging, magnetic resonance spectroscopic imaging, and computed tomography studies. , 2005, International journal of radiation oncology, biology, physics.

[62]  F J Beekman,et al.  Parallel statistical image reconstruction for cone-beam x-ray CT on a shared memory computation platform. , 2005 .

[63]  Steve B. Jiang,et al.  Temporo-spatial IMRT optimization: concepts, implementation and initial results , 2005, Physics in medicine and biology.

[64]  S Webb,et al.  The effect on IMRT conformality of elastic tissue movement and a practical suggestion for movement compensation via the modified dynamic multileaf collimator (dMLC) technique , 2005, Physics in medicine and biology.

[65]  Radhe Mohan,et al.  Four-dimensional radiotherapy planning for DMLC-based respiratory motion tracking. , 2005, Medical physics.